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The Beginner's Guide to Statistical Analysis | 5 Steps & Examples
Statistical analysis means investigating trends, patterns, and relationships using quantitative data . It is an important research tool used by scientists, governments, businesses, and other organizations.
To draw valid conclusions, statistical analysis requires careful planning from the very start of the research process . You need to specify your hypotheses and make decisions about your research design, sample size, and sampling procedure.
After collecting data from your sample, you can organize and summarize the data using descriptive statistics . Then, you can use inferential statistics to formally test hypotheses and make estimates about the population. Finally, you can interpret and generalize your findings.
This article is a practical introduction to statistical analysis for students and researchers. We’ll walk you through the steps using two research examples. The first investigates a potential cause-and-effect relationship, while the second investigates a potential correlation between variables.
Table of contents
Step 1: write your hypotheses and plan your research design, step 2: collect data from a sample, step 3: summarize your data with descriptive statistics, step 4: test hypotheses or make estimates with inferential statistics, step 5: interpret your results.
To collect valid data for statistical analysis, you first need to specify your hypotheses and plan out your research design.
Writing statistical hypotheses
The goal of research is often to investigate a relationship between variables within a population . You start with a prediction, and use statistical analysis to test that prediction.
A statistical hypothesis is a formal way of writing a prediction about a population. Every research prediction is rephrased into null and alternative hypotheses that can be tested using sample data.
While the null hypothesis always predicts no effect or no relationship between variables, the alternative hypothesis states your research prediction of an effect or relationship.
- Null hypothesis: A 5-minute meditation exercise will have no effect on math test scores in teenagers.
- Alternative hypothesis: A 5-minute meditation exercise will improve math test scores in teenagers.
- Null hypothesis: Parental income and GPA have no relationship with each other in college students.
- Alternative hypothesis: Parental income and GPA are positively correlated in college students.
Planning your research design
A research design is your overall strategy for data collection and analysis. It determines the statistical tests you can use to test your hypothesis later on.
First, decide whether your research will use a descriptive, correlational, or experimental design. Experiments directly influence variables, whereas descriptive and correlational studies only measure variables.
- In an experimental design , you can assess a cause-and-effect relationship (e.g., the effect of meditation on test scores) using statistical tests of comparison or regression.
- In a correlational design , you can explore relationships between variables (e.g., parental income and GPA) without any assumption of causality using correlation coefficients and significance tests.
- In a descriptive design , you can study the characteristics of a population or phenomenon (e.g., the prevalence of anxiety in U.S. college students) using statistical tests to draw inferences from sample data.
Your research design also concerns whether you’ll compare participants at the group level or individual level, or both.
- In a between-subjects design , you compare the group-level outcomes of participants who have been exposed to different treatments (e.g., those who performed a meditation exercise vs those who didn’t).
- In a within-subjects design , you compare repeated measures from participants who have participated in all treatments of a study (e.g., scores from before and after performing a meditation exercise).
- In a mixed (factorial) design , one variable is altered between subjects and another is altered within subjects (e.g., pretest and posttest scores from participants who either did or didn’t do a meditation exercise).
- Experimental
- Correlational
First, you’ll take baseline test scores from participants. Then, your participants will undergo a 5-minute meditation exercise. Finally, you’ll record participants’ scores from a second math test.
In this experiment, the independent variable is the 5-minute meditation exercise, and the dependent variable is the math test score from before and after the intervention. Example: Correlational research design In a correlational study, you test whether there is a relationship between parental income and GPA in graduating college students. To collect your data, you will ask participants to fill in a survey and self-report their parents’ incomes and their own GPA.
Measuring variables
When planning a research design, you should operationalize your variables and decide exactly how you will measure them.
For statistical analysis, it’s important to consider the level of measurement of your variables, which tells you what kind of data they contain:
- Categorical data represents groupings. These may be nominal (e.g., gender) or ordinal (e.g. level of language ability).
- Quantitative data represents amounts. These may be on an interval scale (e.g. test score) or a ratio scale (e.g. age).
Many variables can be measured at different levels of precision. For example, age data can be quantitative (8 years old) or categorical (young). If a variable is coded numerically (e.g., level of agreement from 1–5), it doesn’t automatically mean that it’s quantitative instead of categorical.
Identifying the measurement level is important for choosing appropriate statistics and hypothesis tests. For example, you can calculate a mean score with quantitative data, but not with categorical data.
In a research study, along with measures of your variables of interest, you’ll often collect data on relevant participant characteristics.
In most cases, it’s too difficult or expensive to collect data from every member of the population you’re interested in studying. Instead, you’ll collect data from a sample.
Statistical analysis allows you to apply your findings beyond your own sample as long as you use appropriate sampling procedures . You should aim for a sample that is representative of the population.
Sampling for statistical analysis
There are two main approaches to selecting a sample.
- Probability sampling: every member of the population has a chance of being selected for the study through random selection.
- Non-probability sampling: some members of the population are more likely than others to be selected for the study because of criteria such as convenience or voluntary self-selection.
In theory, for highly generalizable findings, you should use a probability sampling method. Random selection reduces several types of research bias , like sampling bias , and ensures that data from your sample is actually typical of the population. Parametric tests can be used to make strong statistical inferences when data are collected using probability sampling.
But in practice, it’s rarely possible to gather the ideal sample. While non-probability samples are more likely to at risk for biases like self-selection bias , they are much easier to recruit and collect data from. Non-parametric tests are more appropriate for non-probability samples, but they result in weaker inferences about the population.
If you want to use parametric tests for non-probability samples, you have to make the case that:
- your sample is representative of the population you’re generalizing your findings to.
- your sample lacks systematic bias.
Keep in mind that external validity means that you can only generalize your conclusions to others who share the characteristics of your sample. For instance, results from Western, Educated, Industrialized, Rich and Democratic samples (e.g., college students in the US) aren’t automatically applicable to all non-WEIRD populations.
If you apply parametric tests to data from non-probability samples, be sure to elaborate on the limitations of how far your results can be generalized in your discussion section .
Create an appropriate sampling procedure
Based on the resources available for your research, decide on how you’ll recruit participants.
- Will you have resources to advertise your study widely, including outside of your university setting?
- Will you have the means to recruit a diverse sample that represents a broad population?
- Do you have time to contact and follow up with members of hard-to-reach groups?
Your participants are self-selected by their schools. Although you’re using a non-probability sample, you aim for a diverse and representative sample. Example: Sampling (correlational study) Your main population of interest is male college students in the US. Using social media advertising, you recruit senior-year male college students from a smaller subpopulation: seven universities in the Boston area.
Calculate sufficient sample size
Before recruiting participants, decide on your sample size either by looking at other studies in your field or using statistics. A sample that’s too small may be unrepresentative of the sample, while a sample that’s too large will be more costly than necessary.
There are many sample size calculators online. Different formulas are used depending on whether you have subgroups or how rigorous your study should be (e.g., in clinical research). As a rule of thumb, a minimum of 30 units or more per subgroup is necessary.
To use these calculators, you have to understand and input these key components:
- Significance level (alpha): the risk of rejecting a true null hypothesis that you are willing to take, usually set at 5%.
- Statistical power : the probability of your study detecting an effect of a certain size if there is one, usually 80% or higher.
- Expected effect size : a standardized indication of how large the expected result of your study will be, usually based on other similar studies.
- Population standard deviation: an estimate of the population parameter based on a previous study or a pilot study of your own.
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Once you’ve collected all of your data, you can inspect them and calculate descriptive statistics that summarize them.
Inspect your data
There are various ways to inspect your data, including the following:
- Organizing data from each variable in frequency distribution tables .
- Displaying data from a key variable in a bar chart to view the distribution of responses.
- Visualizing the relationship between two variables using a scatter plot .
By visualizing your data in tables and graphs, you can assess whether your data follow a skewed or normal distribution and whether there are any outliers or missing data.
A normal distribution means that your data are symmetrically distributed around a center where most values lie, with the values tapering off at the tail ends.
In contrast, a skewed distribution is asymmetric and has more values on one end than the other. The shape of the distribution is important to keep in mind because only some descriptive statistics should be used with skewed distributions.
Extreme outliers can also produce misleading statistics, so you may need a systematic approach to dealing with these values.
Calculate measures of central tendency
Measures of central tendency describe where most of the values in a data set lie. Three main measures of central tendency are often reported:
- Mode : the most popular response or value in the data set.
- Median : the value in the exact middle of the data set when ordered from low to high.
- Mean : the sum of all values divided by the number of values.
However, depending on the shape of the distribution and level of measurement, only one or two of these measures may be appropriate. For example, many demographic characteristics can only be described using the mode or proportions, while a variable like reaction time may not have a mode at all.
Calculate measures of variability
Measures of variability tell you how spread out the values in a data set are. Four main measures of variability are often reported:
- Range : the highest value minus the lowest value of the data set.
- Interquartile range : the range of the middle half of the data set.
- Standard deviation : the average distance between each value in your data set and the mean.
- Variance : the square of the standard deviation.
Once again, the shape of the distribution and level of measurement should guide your choice of variability statistics. The interquartile range is the best measure for skewed distributions, while standard deviation and variance provide the best information for normal distributions.
Using your table, you should check whether the units of the descriptive statistics are comparable for pretest and posttest scores. For example, are the variance levels similar across the groups? Are there any extreme values? If there are, you may need to identify and remove extreme outliers in your data set or transform your data before performing a statistical test.
From this table, we can see that the mean score increased after the meditation exercise, and the variances of the two scores are comparable. Next, we can perform a statistical test to find out if this improvement in test scores is statistically significant in the population. Example: Descriptive statistics (correlational study) After collecting data from 653 students, you tabulate descriptive statistics for annual parental income and GPA.
It’s important to check whether you have a broad range of data points. If you don’t, your data may be skewed towards some groups more than others (e.g., high academic achievers), and only limited inferences can be made about a relationship.
A number that describes a sample is called a statistic , while a number describing a population is called a parameter . Using inferential statistics , you can make conclusions about population parameters based on sample statistics.
Researchers often use two main methods (simultaneously) to make inferences in statistics.
- Estimation: calculating population parameters based on sample statistics.
- Hypothesis testing: a formal process for testing research predictions about the population using samples.
You can make two types of estimates of population parameters from sample statistics:
- A point estimate : a value that represents your best guess of the exact parameter.
- An interval estimate : a range of values that represent your best guess of where the parameter lies.
If your aim is to infer and report population characteristics from sample data, it’s best to use both point and interval estimates in your paper.
You can consider a sample statistic a point estimate for the population parameter when you have a representative sample (e.g., in a wide public opinion poll, the proportion of a sample that supports the current government is taken as the population proportion of government supporters).
There’s always error involved in estimation, so you should also provide a confidence interval as an interval estimate to show the variability around a point estimate.
A confidence interval uses the standard error and the z score from the standard normal distribution to convey where you’d generally expect to find the population parameter most of the time.
Hypothesis testing
Using data from a sample, you can test hypotheses about relationships between variables in the population. Hypothesis testing starts with the assumption that the null hypothesis is true in the population, and you use statistical tests to assess whether the null hypothesis can be rejected or not.
Statistical tests determine where your sample data would lie on an expected distribution of sample data if the null hypothesis were true. These tests give two main outputs:
- A test statistic tells you how much your data differs from the null hypothesis of the test.
- A p value tells you the likelihood of obtaining your results if the null hypothesis is actually true in the population.
Statistical tests come in three main varieties:
- Comparison tests assess group differences in outcomes.
- Regression tests assess cause-and-effect relationships between variables.
- Correlation tests assess relationships between variables without assuming causation.
Your choice of statistical test depends on your research questions, research design, sampling method, and data characteristics.
Parametric tests
Parametric tests make powerful inferences about the population based on sample data. But to use them, some assumptions must be met, and only some types of variables can be used. If your data violate these assumptions, you can perform appropriate data transformations or use alternative non-parametric tests instead.
A regression models the extent to which changes in a predictor variable results in changes in outcome variable(s).
- A simple linear regression includes one predictor variable and one outcome variable.
- A multiple linear regression includes two or more predictor variables and one outcome variable.
Comparison tests usually compare the means of groups. These may be the means of different groups within a sample (e.g., a treatment and control group), the means of one sample group taken at different times (e.g., pretest and posttest scores), or a sample mean and a population mean.
- A t test is for exactly 1 or 2 groups when the sample is small (30 or less).
- A z test is for exactly 1 or 2 groups when the sample is large.
- An ANOVA is for 3 or more groups.
The z and t tests have subtypes based on the number and types of samples and the hypotheses:
- If you have only one sample that you want to compare to a population mean, use a one-sample test .
- If you have paired measurements (within-subjects design), use a dependent (paired) samples test .
- If you have completely separate measurements from two unmatched groups (between-subjects design), use an independent (unpaired) samples test .
- If you expect a difference between groups in a specific direction, use a one-tailed test .
- If you don’t have any expectations for the direction of a difference between groups, use a two-tailed test .
The only parametric correlation test is Pearson’s r . The correlation coefficient ( r ) tells you the strength of a linear relationship between two quantitative variables.
However, to test whether the correlation in the sample is strong enough to be important in the population, you also need to perform a significance test of the correlation coefficient, usually a t test, to obtain a p value. This test uses your sample size to calculate how much the correlation coefficient differs from zero in the population.
You use a dependent-samples, one-tailed t test to assess whether the meditation exercise significantly improved math test scores. The test gives you:
- a t value (test statistic) of 3.00
- a p value of 0.0028
Although Pearson’s r is a test statistic, it doesn’t tell you anything about how significant the correlation is in the population. You also need to test whether this sample correlation coefficient is large enough to demonstrate a correlation in the population.
A t test can also determine how significantly a correlation coefficient differs from zero based on sample size. Since you expect a positive correlation between parental income and GPA, you use a one-sample, one-tailed t test. The t test gives you:
- a t value of 3.08
- a p value of 0.001
The final step of statistical analysis is interpreting your results.
Statistical significance
In hypothesis testing, statistical significance is the main criterion for forming conclusions. You compare your p value to a set significance level (usually 0.05) to decide whether your results are statistically significant or non-significant.
Statistically significant results are considered unlikely to have arisen solely due to chance. There is only a very low chance of such a result occurring if the null hypothesis is true in the population.
This means that you believe the meditation intervention, rather than random factors, directly caused the increase in test scores. Example: Interpret your results (correlational study) You compare your p value of 0.001 to your significance threshold of 0.05. With a p value under this threshold, you can reject the null hypothesis. This indicates a statistically significant correlation between parental income and GPA in male college students.
Note that correlation doesn’t always mean causation, because there are often many underlying factors contributing to a complex variable like GPA. Even if one variable is related to another, this may be because of a third variable influencing both of them, or indirect links between the two variables.
Effect size
A statistically significant result doesn’t necessarily mean that there are important real life applications or clinical outcomes for a finding.
In contrast, the effect size indicates the practical significance of your results. It’s important to report effect sizes along with your inferential statistics for a complete picture of your results. You should also report interval estimates of effect sizes if you’re writing an APA style paper .
With a Cohen’s d of 0.72, there’s medium to high practical significance to your finding that the meditation exercise improved test scores. Example: Effect size (correlational study) To determine the effect size of the correlation coefficient, you compare your Pearson’s r value to Cohen’s effect size criteria.
Decision errors
Type I and Type II errors are mistakes made in research conclusions. A Type I error means rejecting the null hypothesis when it’s actually true, while a Type II error means failing to reject the null hypothesis when it’s false.
You can aim to minimize the risk of these errors by selecting an optimal significance level and ensuring high power . However, there’s a trade-off between the two errors, so a fine balance is necessary.
Frequentist versus Bayesian statistics
Traditionally, frequentist statistics emphasizes null hypothesis significance testing and always starts with the assumption of a true null hypothesis.
However, Bayesian statistics has grown in popularity as an alternative approach in the last few decades. In this approach, you use previous research to continually update your hypotheses based on your expectations and observations.
Bayes factor compares the relative strength of evidence for the null versus the alternative hypothesis rather than making a conclusion about rejecting the null hypothesis or not.
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What Is Data Analysis? (With Examples)
Data analysis is the practice of working with data to glean useful information, which can then be used to make informed decisions.
![[Featured image] A female data analyst takes notes on her laptop at a standing desk in a modern office space](https://d3njjcbhbojbot.cloudfront.net/api/utilities/v1/imageproxy/https://images.ctfassets.net/wp1lcwdav1p1/2CUbULaq9mEfSSIq6lsCUu/b8ec58abf5106bf9bf75b17da09c39c0/What_is_data_analysis.png?w=1500&h=680&q=60&fit=fill&f=faces&fm=jpg&fl=progressive&auto=format%2Ccompress&dpr=1&w=1000&h= "analysis research data")
"It is a capital mistake to theorize before one has data. Insensibly one begins to twist facts to suit theories, instead of theories to suit facts," Sherlock Holme's proclaims in Sir Arthur Conan Doyle's A Scandal in Bohemia.
This idea lies at the root of data analysis. When we can extract meaning from data, it empowers us to make better decisions. And we’re living in a time when we have more data than ever at our fingertips.
Companies are wisening up to the benefits of leveraging data. Data analysis can help a bank to personalize customer interactions, a health care system to predict future health needs, or an entertainment company to create the next big streaming hit.
The World Economic Forum Future of Jobs Report 2020 listed data analysts and scientists as the top emerging job, followed immediately by AI and machine learning specialists, and big data specialists [ 1 ]. In this article, you'll learn more about the data analysis process, different types of data analysis, and recommended courses to help you get started in this exciting field.
Read more: How to Become a Data Analyst (with or Without a Degree)
Data analysis process
As the data available to companies continues to grow both in amount and complexity, so too does the need for an effective and efficient process by which to harness the value of that data. The data analysis process typically moves through several iterative phases. Let’s take a closer look at each.
Identify the business question you’d like to answer. What problem is the company trying to solve? What do you need to measure, and how will you measure it?
Collect the raw data sets you’ll need to help you answer the identified question. Data collection might come from internal sources, like a company’s client relationship management (CRM) software, or from secondary sources, like government records or social media application programming interfaces (APIs).
Clean the data to prepare it for analysis. This often involves purging duplicate and anomalous data, reconciling inconsistencies, standardizing data structure and format, and dealing with white spaces and other syntax errors.
Analyze the data. By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.
Interpret the results of your analysis to see how well the data answered your original question. What recommendations can you make based on the data? What are the limitations to your conclusions?
Watch this video to hear what data analysis how Kevin, Director of Data Analytics at Google, defines data analysis.
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Types of data analysis (with examples)
Data can be used to answer questions and support decisions in many different ways. To identify the best way to analyze your date, it can help to familiarize yourself with the four types of data analysis commonly used in the field.
In this section, we’ll take a look at each of these data analysis methods, along with an example of how each might be applied in the real world.
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Descriptive analysis
Descriptive analysis tells us what happened. This type of analysis helps describe or summarize quantitative data by presenting statistics. For example, descriptive statistical analysis could show the distribution of sales across a group of employees and the average sales figure per employee.
Descriptive analysis answers the question, “what happened?”
Diagnostic analysis
If the descriptive analysis determines the “what,” diagnostic analysis determines the “why.” Let’s say a descriptive analysis shows an unusual influx of patients in a hospital. Drilling into the data further might reveal that many of these patients shared symptoms of a particular virus. This diagnostic analysis can help you determine that an infectious agent—the “why”—led to the influx of patients.
Diagnostic analysis answers the question, “why did it happen?”
Predictive analysis
So far, we’ve looked at types of analysis that examine and draw conclusions about the past. Predictive analytics uses data to form projections about the future. Using predictive analysis, you might notice that a given product has had its best sales during the months of September and October each year, leading you to predict a similar high point during the upcoming year.
Predictive analysis answers the question, “what might happen in the future?”
Prescriptive analysis
Prescriptive analysis takes all the insights gathered from the first three types of analysis and uses them to form recommendations for how a company should act. Using our previous example, this type of analysis might suggest a market plan to build on the success of the high sales months and harness new growth opportunities in the slower months.
Prescriptive analysis answers the question, “what should we do about it?”
This last type is where the concept of data-driven decision-making comes into play.
Read more : Advanced Analytics: Definition, Benefits, and Use Cases
What is data-driven decision-making (DDDM)?
Data-driven decision-making, sometimes abbreviated to DDDM), can be defined as the process of making strategic business decisions based on facts, data, and metrics instead of intuition, emotion, or observation.
This might sound obvious, but in practice, not all organizations are as data-driven as they could be. According to global management consulting firm McKinsey Global Institute, data-driven companies are better at acquiring new customers, maintaining customer loyalty, and achieving above-average profitability [ 2 ].
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Frequently asked questions (FAQ)
Where is data analytics used .
Just about any business or organization can use data analytics to help inform their decisions and boost their performance. Some of the most successful companies across a range of industries — from Amazon and Netflix to Starbucks and General Electric — integrate data into their business plans to improve their overall business performance.
What are the top skills for a data analyst?
Data analysis makes use of a range of analysis tools and technologies. Some of the top skills for data analysts include SQL, data visualization, statistical programming languages (like R and Python), machine learning, and spreadsheets.
Read : 7 In-Demand Data Analyst Skills to Get Hired in 2022
What is a data analyst job salary?
Data from Glassdoor indicates that the average salary for a data analyst in the United States is $95,867 as of July 2022 [ 3 ]. How much you make will depend on factors like your qualifications, experience, and location.
Do data analysts need to be good at math?
Data analytics tends to be less math-intensive than data science. While you probably won’t need to master any advanced mathematics, a foundation in basic math and statistical analysis can help set you up for success.
Learn more: Data Analyst vs. Data Scientist: What’s the Difference?
Article sources
World Economic Forum. " The Future of Jobs Report 2020 , https://www.weforum.org/reports/the-future-of-jobs-report-2020." Accessed July 28, 2022.
McKinsey & Company. " Five facts: How customer analytics boosts corporate performance , https://www.mckinsey.com/business-functions/marketing-and-sales/our-insights/five-facts-how-customer-analytics-boosts-corporate-performance." Accessed July 28, 2022.
Glassdoor. " Data Analyst Salaries , https://www.glassdoor.com/Salaries/data-analyst-salary-SRCH_KO0,12.htm" Accessed July 28, 2022.
This content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.
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Your Modern Business Guide To Data Analysis Methods And Techniques
Table of Contents
1) What Is Data Analysis?
2) Why Is Data Analysis Important?
3) What Is The Data Analysis Process?
4) Types Of Data Analysis Methods
5) Top Data Analysis Techniques To Apply
6) Quality Criteria For Data Analysis
7) Data Analysis Limitations & Barriers
8) Data Analysis Skills
9) Data Analysis In The Big Data Environment
In our data-rich age, understanding how to analyze and extract true meaning from our business’s digital insights is one of the primary drivers of success.
Despite the colossal volume of data we create every day, a mere 0.5% is actually analyzed and used for data discovery , improvement, and intelligence. While that may not seem like much, considering the amount of digital information we have at our fingertips, half a percent still accounts for a vast amount of data.
With so much data and so little time, knowing how to collect, curate, organize, and make sense of all of this potentially business-boosting information can be a minefield – but online data analysis is the solution.
In science, data analysis uses a more complex approach with advanced techniques to explore and experiment with data. On the other hand, in a business context, data is used to make data-driven decisions that will enable the company to improve its overall performance. In this post, we will cover the analysis of data from an organizational point of view while still going through the scientific and statistical foundations that are fundamental to understanding the basics of data analysis.
To put all of that into perspective, we will answer a host of important analytical questions, explore analytical methods and techniques, while demonstrating how to perform analysis in the real world with a 17-step blueprint for success.
What Is Data Analysis?
Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.
All these various methods are largely based on two core areas: quantitative and qualitative research.
To explain the key differences between qualitative and quantitative research, here’s a video for your viewing pleasure:
Gaining a better understanding of different techniques and methods in quantitative research as well as qualitative insights will give your analyzing efforts a more clearly defined direction, so it’s worth taking the time to allow this particular knowledge to sink in. Additionally, you will be able to create a comprehensive analytical report that will skyrocket your analysis.
Apart from qualitative and quantitative categories, there are also other types of data that you should be aware of before dividing into complex data analysis processes. These categories include:
- Big data: Refers to massive data sets that need to be analyzed using advanced software to reveal patterns and trends. It is considered to be one of the best analytical assets as it provides larger volumes of data at a faster rate.
- Metadata: Putting it simply, metadata is data that provides insights about other data. It summarizes key information about specific data that makes it easier to find and reuse for later purposes.
- Real time data: As its name suggests, real time data is presented as soon as it is acquired. From an organizational perspective, this is the most valuable data as it can help you make important decisions based on the latest developments. Our guide on real time analytics will tell you more about the topic.
- Machine data: This is more complex data that is generated solely by a machine such as phones, computers, or even websites and embedded systems, without previous human interaction.
Why Is Data Analysis Important?
Before we go into detail about the categories of analysis along with its methods and techniques, you must understand the potential that analyzing data can bring to your organization.
- Informed decision-making : From a management perspective, you can benefit from analyzing your data as it helps you make decisions based on facts and not simple intuition. For instance, you can understand where to invest your capital, detect growth opportunities, predict your income, or tackle uncommon situations before they become problems. Through this, you can extract relevant insights from all areas in your organization, and with the help of dashboard software , present the data in a professional and interactive way to different stakeholders.
- Reduce costs : Another great benefit is to reduce costs. With the help of advanced technologies such as predictive analytics, businesses can spot improvement opportunities, trends, and patterns in their data and plan their strategies accordingly. In time, this will help you save money and resources on implementing the wrong strategies. And not just that, by predicting different scenarios such as sales and demand you can also anticipate production and supply.
- Target customers better : Customers are arguably the most crucial element in any business. By using analytics to get a 360° vision of all aspects related to your customers, you can understand which channels they use to communicate with you, their demographics, interests, habits, purchasing behaviors, and more. In the long run, it will drive success to your marketing strategies, allow you to identify new potential customers, and avoid wasting resources on targeting the wrong people or sending the wrong message. You can also track customer satisfaction by analyzing your client’s reviews or your customer service department’s performance.
What Is The Data Analysis Process?
When we talk about analyzing data there is an order to follow in order to extract the needed conclusions. The analysis process consists of 5 key stages. We will cover each of them more in detail later in the post, but to start providing the needed context to understand what is coming next, here is a rundown of the 5 essential steps of data analysis.
- Identify: Before you get your hands dirty with data, you first need to identify why you need it in the first place. The identification is the stage in which you establish the questions you will need to answer. For example, what is the customer's perception of our brand? Or what type of packaging is more engaging to our potential customers? Once the questions are outlined you are ready for the next step.
- Collect: As its name suggests, this is the stage where you start collecting the needed data. Here, you define which sources of data you will use and how you will use them. The collection of data can come in different forms such as internal or external sources, surveys, interviews, questionnaires, and focus groups, among others. An important note here is that the way you collect the data will be different in a quantitative and qualitative scenario.
- Clean: Once you have the necessary data it is time to clean it and leave it ready for analysis. Not all the data you collect will be useful, when collecting big amounts of data in different formats it is very likely that you will find yourself with duplicate or badly formatted data. To avoid this, before you start working with your data you need to make sure to erase any white spaces, duplicate records, or formatting errors. This way you avoid hurting your analysis with bad-quality data.
- Analyze : With the help of various techniques such as statistical analysis, regressions, neural networks, text analysis, and more, you can start analyzing and manipulating your data to extract relevant conclusions. At this stage, you find trends, correlations, variations, and patterns that can help you answer the questions you first thought of in the identify stage. Various technologies in the market assist researchers and average users with the management of their data. Some of them include business intelligence and visualization software, predictive analytics, and data mining, among others.
- Interpret: Last but not least you have one of the most important steps: it is time to interpret your results. This stage is where the researcher comes up with courses of action based on the findings. For example, here you would understand if your clients prefer packaging that is red or green, plastic or paper, etc. Additionally, at this stage, you can also find some limitations and work on them.
Now that you have a basic understanding of the key data analysis steps, let’s look at the top 17 essential methods.
17 Essential Types Of Data Analysis Methods
Before diving into the 17 essential types of methods, it is important that we go over really fast through the main analysis categories. Starting with the category of descriptive up to prescriptive analysis, the complexity and effort of data evaluation increases, but also the added value for the company.
a) Descriptive analysis - What happened.
The descriptive analysis method is the starting point for any analytic reflection, and it aims to answer the question of what happened? It does this by ordering, manipulating, and interpreting raw data from various sources to turn it into valuable insights for your organization.
Performing descriptive analysis is essential, as it enables us to present our insights in a meaningful way. Although it is relevant to mention that this analysis on its own will not allow you to predict future outcomes or tell you the answer to questions like why something happened, it will leave your data organized and ready to conduct further investigations.
b) Exploratory analysis - How to explore data relationships.
As its name suggests, the main aim of the exploratory analysis is to explore. Prior to it, there is still no notion of the relationship between the data and the variables. Once the data is investigated, exploratory analysis helps you to find connections and generate hypotheses and solutions for specific problems. A typical area of application for it is data mining.
c) Diagnostic analysis - Why it happened.
Diagnostic data analytics empowers analysts and executives by helping them gain a firm contextual understanding of why something happened. If you know why something happened as well as how it happened, you will be able to pinpoint the exact ways of tackling the issue or challenge.
Designed to provide direct and actionable answers to specific questions, this is one of the world’s most important methods in research, among its other key organizational functions such as retail analytics , e.g.
c) Predictive analysis - What will happen.
The predictive method allows you to look into the future to answer the question: what will happen? In order to do this, it uses the results of the previously mentioned descriptive, exploratory, and diagnostic analysis, in addition to machine learning (ML) and artificial intelligence (AI). Through this, you can uncover future trends, potential problems or inefficiencies, connections, and casualties in your data.
With predictive analysis, you can unfold and develop initiatives that will not only enhance your various operational processes but also help you gain an all-important edge over the competition. If you understand why a trend, pattern, or event happened through data, you will be able to develop an informed projection of how things may unfold in particular areas of the business.
e) Prescriptive analysis - How will it happen.
Another of the most effective types of analysis methods in research. Prescriptive data techniques cross over from predictive analysis in the way that it revolves around using patterns or trends to develop responsive, practical business strategies.
By drilling down into prescriptive analysis, you will play an active role in the data consumption process by taking well-arranged sets of visual data and using it as a powerful fix to emerging issues in a number of key areas, including marketing, sales, customer experience, HR, fulfillment, finance, logistics analytics , and others.
As mentioned at the beginning of the post, data analysis methods can be divided into two big categories: quantitative and qualitative. Each of these categories holds a powerful analytical value that changes depending on the scenario and type of data you are working with. Below, we will discuss 17 methods that are divided into qualitative and quantitative approaches.
Without further ado, here are the 17 essential types of data analysis methods with some use cases in the business world:
A. Quantitative Methods
To put it simply, quantitative analysis refers to all methods that use numerical data or data that can be turned into numbers (e.g. category variables like gender, age, etc.) to extract valuable insights. It is used to extract valuable conclusions about relationships, differences, and test hypotheses. Below we discuss some of the key quantitative methods.
1. Cluster analysis
The action of grouping a set of data elements in a way that said elements are more similar (in a particular sense) to each other than to those in other groups – hence the term ‘cluster.’ Since there is no target variable when clustering, the method is often used to find hidden patterns in the data. The approach is also used to provide additional context to a trend or dataset.
Let's look at it from an organizational perspective. In a perfect world, marketers would be able to analyze each customer separately and give them the best-personalized service, but let's face it, with a large customer base, it is timely impossible to do that. That's where clustering comes in. By grouping customers into clusters based on demographics, purchasing behaviors, monetary value, or any other factor that might be relevant for your company, you will be able to immediately optimize your efforts and give your customers the best experience based on their needs.
2. Cohort analysis
This type of data analysis approach uses historical data to examine and compare a determined segment of users' behavior, which can then be grouped with others with similar characteristics. By using this methodology, it's possible to gain a wealth of insight into consumer needs or a firm understanding of a broader target group.
Cohort analysis can be really useful for performing analysis in marketing as it will allow you to understand the impact of your campaigns on specific groups of customers. To exemplify, imagine you send an email campaign encouraging customers to sign up for your site. For this, you create two versions of the campaign with different designs, CTAs, and ad content. Later on, you can use cohort analysis to track the performance of the campaign for a longer period of time and understand which type of content is driving your customers to sign up, repurchase, or engage in other ways.
A useful tool to start performing cohort analysis method is Google Analytics. You can learn more about the benefits and limitations of using cohorts in GA in this useful guide . In the bottom image, you see an example of how you visualize a cohort in this tool. The segments (devices traffic) are divided into date cohorts (usage of devices) and then analyzed week by week to extract insights into performance.
3. Regression analysis
Regression uses historical data to understand how a dependent variable's value is affected when one (linear regression) or more independent variables (multiple regression) change or stay the same. By understanding each variable's relationship and how it developed in the past, you can anticipate possible outcomes and make better decisions in the future.
Let's bring it down with an example. Imagine you did a regression analysis of your sales in 2019 and discovered that variables like product quality, store design, customer service, marketing campaigns, and sales channels affected the overall result. Now you want to use regression to analyze which of these variables changed or if any new ones appeared during 2020. For example, you couldn’t sell as much in your physical store due to COVID lockdowns. Therefore, your sales could’ve either dropped in general or increased in your online channels. Through this, you can understand which independent variables affected the overall performance of your dependent variable, annual sales.
If you want to go deeper into this type of analysis, check out this article and learn more about how you can benefit from regression.
4. Neural networks
The neural network forms the basis for the intelligent algorithms of machine learning. It is a form of analytics that attempts, with minimal intervention, to understand how the human brain would generate insights and predict values. Neural networks learn from each and every data transaction, meaning that they evolve and advance over time.
A typical area of application for neural networks is predictive analytics. There are BI reporting tools that have this feature implemented within them, such as the Predictive Analytics Tool from datapine. This tool enables users to quickly and easily generate all kinds of predictions. All you have to do is select the data to be processed based on your KPIs, and the software automatically calculates forecasts based on historical and current data. Thanks to its user-friendly interface, anyone in your organization can manage it; there’s no need to be an advanced scientist.
Here is an example of how you can use the predictive analysis tool from datapine:
**click to enlarge**
5. Factor analysis
The factor analysis also called “dimension reduction” is a type of data analysis used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. The aim here is to uncover independent latent variables, an ideal method for streamlining specific segments.
A good way to understand this data analysis method is a customer evaluation of a product. The initial assessment is based on different variables like color, shape, wearability, current trends, materials, comfort, the place where they bought the product, and frequency of usage. Like this, the list can be endless, depending on what you want to track. In this case, factor analysis comes into the picture by summarizing all of these variables into homogenous groups, for example, by grouping the variables color, materials, quality, and trends into a brother latent variable of design.
If you want to start analyzing data using factor analysis we recommend you take a look at this practical guide from UCLA.
6. Data mining
A method of data analysis that is the umbrella term for engineering metrics and insights for additional value, direction, and context. By using exploratory statistical evaluation, data mining aims to identify dependencies, relations, patterns, and trends to generate advanced knowledge. When considering how to analyze data, adopting a data mining mindset is essential to success - as such, it’s an area that is worth exploring in greater detail.
An excellent use case of data mining is datapine intelligent data alerts . With the help of artificial intelligence and machine learning, they provide automated signals based on particular commands or occurrences within a dataset. For example, if you’re monitoring supply chain KPIs , you could set an intelligent alarm to trigger when invalid or low-quality data appears. By doing so, you will be able to drill down deep into the issue and fix it swiftly and effectively.
In the following picture, you can see how the intelligent alarms from datapine work. By setting up ranges on daily orders, sessions, and revenues, the alarms will notify you if the goal was not completed or if it exceeded expectations.
7. Time series analysis
As its name suggests, time series analysis is used to analyze a set of data points collected over a specified period of time. Although analysts use this method to monitor the data points in a specific interval of time rather than just monitoring them intermittently, the time series analysis is not uniquely used for the purpose of collecting data over time. Instead, it allows researchers to understand if variables changed during the duration of the study, how the different variables are dependent, and how did it reach the end result.
In a business context, this method is used to understand the causes of different trends and patterns to extract valuable insights. Another way of using this method is with the help of time series forecasting. Powered by predictive technologies, businesses can analyze various data sets over a period of time and forecast different future events.
A great use case to put time series analysis into perspective is seasonality effects on sales. By using time series forecasting to analyze sales data of a specific product over time, you can understand if sales rise over a specific period of time (e.g. swimwear during summertime, or candy during Halloween). These insights allow you to predict demand and prepare production accordingly.
8. Decision Trees
The decision tree analysis aims to act as a support tool to make smart and strategic decisions. By visually displaying potential outcomes, consequences, and costs in a tree-like model, researchers and company users can easily evaluate all factors involved and choose the best course of action. Decision trees are helpful to analyze quantitative data and they allow for an improved decision-making process by helping you spot improvement opportunities, reduce costs, and enhance operational efficiency and production.
But how does a decision tree actually works? This method works like a flowchart that starts with the main decision that you need to make and branches out based on the different outcomes and consequences of each decision. Each outcome will outline its own consequences, costs, and gains and, at the end of the analysis, you can compare each of them and make the smartest decision.
Businesses can use them to understand which project is more cost-effective and will bring more earnings in the long run. For example, imagine you need to decide if you want to update your software app or build a new app entirely. Here you would compare the total costs, the time needed to be invested, potential revenue, and any other factor that might affect your decision. In the end, you would be able to see which of these two options is more realistic and attainable for your company or research.
9. Conjoint analysis
Last but not least, we have the conjoint analysis. This approach is usually used in surveys to understand how individuals value different attributes of a product or service and it is one of the most effective methods to extract consumer preferences. When it comes to purchasing, some clients might be more price-focused, others more features-focused, and others might have a sustainable focus. Whatever your customer's preferences are, you can find them with conjoint analysis. Through this, companies can define pricing strategies, packaging options, subscription packages, and more.
A great example of conjoint analysis is in marketing and sales. For instance, a cupcake brand might use conjoint analysis and find that its clients prefer gluten-free options and cupcakes with healthier toppings over super sugary ones. Thus, the cupcake brand can turn these insights into advertisements and promotions to increase sales of this particular type of product. And not just that, conjoint analysis can also help businesses segment their customers based on their interests. This allows them to send different messaging that will bring value to each of the segments.
10. Correspondence Analysis
Also known as reciprocal averaging, correspondence analysis is a method used to analyze the relationship between categorical variables presented within a contingency table. A contingency table is a table that displays two (simple correspondence analysis) or more (multiple correspondence analysis) categorical variables across rows and columns that show the distribution of the data, which is usually answers to a survey or questionnaire on a specific topic.
This method starts by calculating an “expected value” which is done by multiplying row and column averages and dividing it by the overall original value of the specific table cell. The “expected value” is then subtracted from the original value resulting in a “residual number” which is what allows you to extract conclusions about relationships and distribution. The results of this analysis are later displayed using a map that represents the relationship between the different values. The closest two values are in the map, the bigger the relationship. Let’s put it into perspective with an example.
Imagine you are carrying out a market research analysis about outdoor clothing brands and how they are perceived by the public. For this analysis, you ask a group of people to match each brand with a certain attribute which can be durability, innovation, quality materials, etc. When calculating the residual numbers, you can see that brand A has a positive residual for innovation but a negative one for durability. This means that brand A is not positioned as a durable brand in the market, something that competitors could take advantage of.
11. Multidimensional Scaling (MDS)
MDS is a method used to observe the similarities or disparities between objects which can be colors, brands, people, geographical coordinates, and more. The objects are plotted using an “MDS map” that positions similar objects together and disparate ones far apart. The (dis) similarities between objects are represented using one or more dimensions that can be observed using a numerical scale. For example, if you want to know how people feel about the COVID-19 vaccine, you can use 1 for “don’t believe in the vaccine at all” and 10 for “firmly believe in the vaccine” and a scale of 2 to 9 for in between responses. When analyzing an MDS map the only thing that matters is the distance between the objects, the orientation of the dimensions is arbitrary and has no meaning at all.
Multidimensional scaling is a valuable technique for market research, especially when it comes to evaluating product or brand positioning. For instance, if a cupcake brand wants to know how they are positioned compared to competitors, it can define 2-3 dimensions such as taste, ingredients, shopping experience, or more, and do a multidimensional scaling analysis to find improvement opportunities as well as areas in which competitors are currently leading.
Another business example is in procurement when deciding on different suppliers. Decision makers can generate an MDS map to see how the different prices, delivery times, technical services, and more of the different suppliers differ and pick the one that suits their needs the best.
A final example proposed by a research paper on "An Improved Study of Multilevel Semantic Network Visualization for Analyzing Sentiment Word of Movie Review Data". Researchers picked a two-dimensional MDS map to display the distances and relationships between different sentiments in movie reviews. They used 36 sentiment words and distributed them based on their emotional distance as we can see in the image below where the words "outraged" and "sweet" are on opposite sides of the map, marking the distance between the two emotions very clearly.
Aside from being a valuable technique to analyze dissimilarities, MDS also serves as a dimension-reduction technique for large dimensional data.
B. Qualitative Methods
Qualitative data analysis methods are defined as the observation of non-numerical data that is gathered and produced using methods of observation such as interviews, focus groups, questionnaires, and more. As opposed to quantitative methods, qualitative data is more subjective and highly valuable in analyzing customer retention and product development.
12. Text analysis
Text analysis, also known in the industry as text mining, works by taking large sets of textual data and arranging them in a way that makes it easier to manage. By working through this cleansing process in stringent detail, you will be able to extract the data that is truly relevant to your organization and use it to develop actionable insights that will propel you forward.
Modern software accelerate the application of text analytics. Thanks to the combination of machine learning and intelligent algorithms, you can perform advanced analytical processes such as sentiment analysis. This technique allows you to understand the intentions and emotions of a text, for example, if it's positive, negative, or neutral, and then give it a score depending on certain factors and categories that are relevant to your brand. Sentiment analysis is often used to monitor brand and product reputation and to understand how successful your customer experience is. To learn more about the topic check out this insightful article .
By analyzing data from various word-based sources, including product reviews, articles, social media communications, and survey responses, you will gain invaluable insights into your audience, as well as their needs, preferences, and pain points. This will allow you to create campaigns, services, and communications that meet your prospects’ needs on a personal level, growing your audience while boosting customer retention. There are various other “sub-methods” that are an extension of text analysis. Each of them serves a more specific purpose and we will look at them in detail next.
13. Content Analysis
This is a straightforward and very popular method that examines the presence and frequency of certain words, concepts, and subjects in different content formats such as text, image, audio, or video. For example, the number of times the name of a celebrity is mentioned on social media or online tabloids. It does this by coding text data that is later categorized and tabulated in a way that can provide valuable insights, making it the perfect mix of quantitative and qualitative analysis.
There are two types of content analysis. The first one is the conceptual analysis which focuses on explicit data, for instance, the number of times a concept or word is mentioned in a piece of content. The second one is relational analysis, which focuses on the relationship between different concepts or words and how they are connected within a specific context.
Content analysis is often used by marketers to measure brand reputation and customer behavior. For example, by analyzing customer reviews. It can also be used to analyze customer interviews and find directions for new product development. It is also important to note, that in order to extract the maximum potential out of this analysis method, it is necessary to have a clearly defined research question.
14. Thematic Analysis
Very similar to content analysis, thematic analysis also helps in identifying and interpreting patterns in qualitative data with the main difference being that the first one can also be applied to quantitative analysis. The thematic method analyzes large pieces of text data such as focus group transcripts or interviews and groups them into themes or categories that come up frequently within the text. It is a great method when trying to figure out peoples view’s and opinions about a certain topic. For example, if you are a brand that cares about sustainability, you can do a survey of your customers to analyze their views and opinions about sustainability and how they apply it to their lives. You can also analyze customer service calls transcripts to find common issues and improve your service.
Thematic analysis is a very subjective technique that relies on the researcher’s judgment. Therefore, to avoid biases, it has 6 steps that include familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. It is also important to note that, because it is a flexible approach, the data can be interpreted in multiple ways and it can be hard to select what data is more important to emphasize.
15. Narrative Analysis
A bit more complex in nature than the two previous ones, narrative analysis is used to explore the meaning behind the stories that people tell and most importantly, how they tell them. By looking into the words that people use to describe a situation you can extract valuable conclusions about their perspective on a specific topic. Common sources for narrative data include autobiographies, family stories, opinion pieces, and testimonials, among others.
From a business perspective, narrative analysis can be useful to analyze customer behaviors and feelings towards a specific product, service, feature, or others. It provides unique and deep insights that can be extremely valuable. However, it has some drawbacks.
The biggest weakness of this method is that the sample sizes are usually very small due to the complexity and time-consuming nature of the collection of narrative data. Plus, the way a subject tells a story will be significantly influenced by his or her specific experiences, making it very hard to replicate in a subsequent study.
16. Discourse Analysis
Discourse analysis is used to understand the meaning behind any type of written, verbal, or symbolic discourse based on its political, social, or cultural context. It mixes the analysis of languages and situations together. This means that the way the content is constructed and the meaning behind it is significantly influenced by the culture and society it takes place in. For example, if you are analyzing political speeches you need to consider different context elements such as the politician's background, the current political context of the country, the audience to which the speech is directed, and so on.
From a business point of view, discourse analysis is a great market research tool. It allows marketers to understand how the norms and ideas of the specific market work and how their customers relate to those ideas. It can be very useful to build a brand mission or develop a unique tone of voice.
17. Grounded Theory Analysis
Traditionally, researchers decide on a method and hypothesis and start to collect the data to prove that hypothesis. The grounded theory is the only method that doesn’t require an initial research question or hypothesis as its value lies in the generation of new theories. With the grounded theory method, you can go into the analysis process with an open mind and explore the data to generate new theories through tests and revisions. In fact, it is not necessary to collect the data and then start to analyze it. Researchers usually start to find valuable insights as they are gathering the data.
All of these elements make grounded theory a very valuable method as theories are fully backed by data instead of initial assumptions. It is a great technique to analyze poorly researched topics or find the causes behind specific company outcomes. For example, product managers and marketers might use the grounded theory to find the causes of high levels of customer churn and look into customer surveys and reviews to develop new theories about the causes.
How To Analyze Data? Top 17 Data Analysis Techniques To Apply
Now that we’ve answered the questions “what is data analysis’”, why is it important, and covered the different data analysis types, it’s time to dig deeper into how to perform your analysis by working through these 17 essential techniques.
1. Collaborate your needs
Before you begin analyzing or drilling down into any techniques, it’s crucial to sit down collaboratively with all key stakeholders within your organization, decide on your primary campaign or strategic goals, and gain a fundamental understanding of the types of insights that will best benefit your progress or provide you with the level of vision you need to evolve your organization.
2. Establish your questions
Once you’ve outlined your core objectives, you should consider which questions will need answering to help you achieve your mission. This is one of the most important techniques as it will shape the very foundations of your success.
To help you ask the right things and ensure your data works for you, you have to ask the right data analysis questions .
3. Data democratization
After giving your data analytics methodology some real direction, and knowing which questions need answering to extract optimum value from the information available to your organization, you should continue with democratization.
Data democratization is an action that aims to connect data from various sources efficiently and quickly so that anyone in your organization can access it at any given moment. You can extract data in text, images, videos, numbers, or any other format. And then perform cross-database analysis to achieve more advanced insights to share with the rest of the company interactively.
Once you have decided on your most valuable sources, you need to take all of this into a structured format to start collecting your insights. For this purpose, datapine offers an easy all-in-one data connectors feature to integrate all your internal and external sources and manage them at your will. Additionally, datapine’s end-to-end solution automatically updates your data, allowing you to save time and focus on performing the right analysis to grow your company.
4. Think of governance
When collecting data in a business or research context you always need to think about security and privacy. With data breaches becoming a topic of concern for businesses, the need to protect your client's or subject’s sensitive information becomes critical.
To ensure that all this is taken care of, you need to think of a data governance strategy. According to Gartner , this concept refers to “ the specification of decision rights and an accountability framework to ensure the appropriate behavior in the valuation, creation, consumption, and control of data and analytics .” In simpler words, data governance is a collection of processes, roles, and policies, that ensure the efficient use of data while still achieving the main company goals. It ensures that clear roles are in place for who can access the information and how they can access it. In time, this not only ensures that sensitive information is protected but also allows for an efficient analysis as a whole.
5. Clean your data
After harvesting from so many sources you will be left with a vast amount of information that can be overwhelming to deal with. At the same time, you can be faced with incorrect data that can be misleading to your analysis. The smartest thing you can do to avoid dealing with this in the future is to clean the data. This is fundamental before visualizing it, as it will ensure that the insights you extract from it are correct.
There are many things that you need to look for in the cleaning process. The most important one is to eliminate any duplicate observations; this usually appears when using multiple internal and external sources of information. You can also add any missing codes, fix empty fields, and eliminate incorrectly formatted data.
Another usual form of cleaning is done with text data. As we mentioned earlier, most companies today analyze customer reviews, social media comments, questionnaires, and several other text inputs. In order for algorithms to detect patterns, text data needs to be revised to avoid invalid characters or any syntax or spelling errors.
Most importantly, the aim of cleaning is to prevent you from arriving at false conclusions that can damage your company in the long run. By using clean data, you will also help BI solutions to interact better with your information and create better reports for your organization.
6. Set your KPIs
Once you’ve set your sources, cleaned your data, and established clear-cut questions you want your insights to answer, you need to set a host of key performance indicators (KPIs) that will help you track, measure, and shape your progress in a number of key areas.
KPIs are critical to both qualitative and quantitative analysis research. This is one of the primary methods of data analysis you certainly shouldn’t overlook.
To help you set the best possible KPIs for your initiatives and activities, here is an example of a relevant logistics KPI : transportation-related costs. If you want to see more go explore our collection of key performance indicator examples .
7. Omit useless data
Having bestowed your data analysis tools and techniques with true purpose and defined your mission, you should explore the raw data you’ve collected from all sources and use your KPIs as a reference for chopping out any information you deem to be useless.
Trimming the informational fat is one of the most crucial methods of analysis as it will allow you to focus your analytical efforts and squeeze every drop of value from the remaining ‘lean’ information.
Any stats, facts, figures, or metrics that don’t align with your business goals or fit with your KPI management strategies should be eliminated from the equation.
8. Build a data management roadmap
While, at this point, this particular step is optional (you will have already gained a wealth of insight and formed a fairly sound strategy by now), creating a data governance roadmap will help your data analysis methods and techniques become successful on a more sustainable basis. These roadmaps, if developed properly, are also built so they can be tweaked and scaled over time.
Invest ample time in developing a roadmap that will help you store, manage, and handle your data internally, and you will make your analysis techniques all the more fluid and functional – one of the most powerful types of data analysis methods available today.
9. Integrate technology
There are many ways to analyze data, but one of the most vital aspects of analytical success in a business context is integrating the right decision support software and technology.
Robust analysis platforms will not only allow you to pull critical data from your most valuable sources while working with dynamic KPIs that will offer you actionable insights; it will also present them in a digestible, visual, interactive format from one central, live dashboard . A data methodology you can count on.
By integrating the right technology within your data analysis methodology, you’ll avoid fragmenting your insights, saving you time and effort while allowing you to enjoy the maximum value from your business’s most valuable insights.
For a look at the power of software for the purpose of analysis and to enhance your methods of analyzing, glance over our selection of dashboard examples .
10. Answer your questions
By considering each of the above efforts, working with the right technology, and fostering a cohesive internal culture where everyone buys into the different ways to analyze data as well as the power of digital intelligence, you will swiftly start to answer your most burning business questions. Arguably, the best way to make your data concepts accessible across the organization is through data visualization.
11. Visualize your data
Online data visualization is a powerful tool as it lets you tell a story with your metrics, allowing users across the organization to extract meaningful insights that aid business evolution – and it covers all the different ways to analyze data.
The purpose of analyzing is to make your entire organization more informed and intelligent, and with the right platform or dashboard, this is simpler than you think, as demonstrated by our marketing dashboard .
This visual, dynamic, and interactive online dashboard is a data analysis example designed to give Chief Marketing Officers (CMO) an overview of relevant metrics to help them understand if they achieved their monthly goals.
In detail, this example generated with a modern dashboard creator displays interactive charts for monthly revenues, costs, net income, and net income per customer; all of them are compared with the previous month so that you can understand how the data fluctuated. In addition, it shows a detailed summary of the number of users, customers, SQLs, and MQLs per month to visualize the whole picture and extract relevant insights or trends for your marketing reports .
The CMO dashboard is perfect for c-level management as it can help them monitor the strategic outcome of their marketing efforts and make data-driven decisions that can benefit the company exponentially.
12. Be careful with the interpretation
We already dedicated an entire post to data interpretation as it is a fundamental part of the process of data analysis. It gives meaning to the analytical information and aims to drive a concise conclusion from the analysis results. Since most of the time companies are dealing with data from many different sources, the interpretation stage needs to be done carefully and properly in order to avoid misinterpretations.
To help you through the process, here we list three common practices that you need to avoid at all costs when looking at your data:
- Correlation vs. causation: The human brain is formatted to find patterns. This behavior leads to one of the most common mistakes when performing interpretation: confusing correlation with causation. Although these two aspects can exist simultaneously, it is not correct to assume that because two things happened together, one provoked the other. A piece of advice to avoid falling into this mistake is never to trust just intuition, trust the data. If there is no objective evidence of causation, then always stick to correlation.
- Confirmation bias: This phenomenon describes the tendency to select and interpret only the data necessary to prove one hypothesis, often ignoring the elements that might disprove it. Even if it's not done on purpose, confirmation bias can represent a real problem, as excluding relevant information can lead to false conclusions and, therefore, bad business decisions. To avoid it, always try to disprove your hypothesis instead of proving it, share your analysis with other team members, and avoid drawing any conclusions before the entire analytical project is finalized.
- Statistical significance: To put it in short words, statistical significance helps analysts understand if a result is actually accurate or if it happened because of a sampling error or pure chance. The level of statistical significance needed might depend on the sample size and the industry being analyzed. In any case, ignoring the significance of a result when it might influence decision-making can be a huge mistake.
13. Build a narrative
Now, we’re going to look at how you can bring all of these elements together in a way that will benefit your business - starting with a little something called data storytelling.
The human brain responds incredibly well to strong stories or narratives. Once you’ve cleansed, shaped, and visualized your most invaluable data using various BI dashboard tools , you should strive to tell a story - one with a clear-cut beginning, middle, and end.
By doing so, you will make your analytical efforts more accessible, digestible, and universal, empowering more people within your organization to use your discoveries to their actionable advantage.
14. Consider autonomous technology
Autonomous technologies, such as artificial intelligence (AI) and machine learning (ML), play a significant role in the advancement of understanding how to analyze data more effectively.
Gartner predicts that by the end of this year, 80% of emerging technologies will be developed with AI foundations. This is a testament to the ever-growing power and value of autonomous technologies.
At the moment, these technologies are revolutionizing the analysis industry. Some examples that we mentioned earlier are neural networks, intelligent alarms, and sentiment analysis.
15. Share the load
If you work with the right tools and dashboards, you will be able to present your metrics in a digestible, value-driven format, allowing almost everyone in the organization to connect with and use relevant data to their advantage.
Modern dashboards consolidate data from various sources, providing access to a wealth of insights in one centralized location, no matter if you need to monitor recruitment metrics or generate reports that need to be sent across numerous departments. Moreover, these cutting-edge tools offer access to dashboards from a multitude of devices, meaning that everyone within the business can connect with practical insights remotely - and share the load.
Once everyone is able to work with a data-driven mindset, you will catalyze the success of your business in ways you never thought possible. And when it comes to knowing how to analyze data, this kind of collaborative approach is essential.
16. Data analysis tools
In order to perform high-quality analysis of data, it is fundamental to use tools and software that will ensure the best results. Here we leave you a small summary of four fundamental categories of data analysis tools for your organization.
- Business Intelligence: BI tools allow you to process significant amounts of data from several sources in any format. Through this, you can not only analyze and monitor your data to extract relevant insights but also create interactive reports and dashboards to visualize your KPIs and use them for your company's good. datapine is an amazing online BI software that is focused on delivering powerful online analysis features that are accessible to beginner and advanced users. Like this, it offers a full-service solution that includes cutting-edge analysis of data, KPIs visualization, live dashboards, reporting, and artificial intelligence technologies to predict trends and minimize risk.
- Statistical analysis: These tools are usually designed for scientists, statisticians, market researchers, and mathematicians, as they allow them to perform complex statistical analyses with methods like regression analysis, predictive analysis, and statistical modeling. A good tool to perform this type of analysis is R-Studio as it offers a powerful data modeling and hypothesis testing feature that can cover both academic and general data analysis. This tool is one of the favorite ones in the industry, due to its capability for data cleaning, data reduction, and performing advanced analysis with several statistical methods. Another relevant tool to mention is SPSS from IBM. The software offers advanced statistical analysis for users of all skill levels. Thanks to a vast library of machine learning algorithms, text analysis, and a hypothesis testing approach it can help your company find relevant insights to drive better decisions. SPSS also works as a cloud service that enables you to run it anywhere.
- SQL Consoles: SQL is a programming language often used to handle structured data in relational databases. Tools like these are popular among data scientists as they are extremely effective in unlocking these databases' value. Undoubtedly, one of the most used SQL software in the market is MySQL Workbench . This tool offers several features such as a visual tool for database modeling and monitoring, complete SQL optimization, administration tools, and visual performance dashboards to keep track of KPIs.
- Data Visualization: These tools are used to represent your data through charts, graphs, and maps that allow you to find patterns and trends in the data. datapine's already mentioned BI platform also offers a wealth of powerful online data visualization tools with several benefits. Some of them include: delivering compelling data-driven presentations to share with your entire company, the ability to see your data online with any device wherever you are, an interactive dashboard design feature that enables you to showcase your results in an interactive and understandable way, and to perform online self-service reports that can be used simultaneously with several other people to enhance team productivity.
17. Refine your process constantly
Last is a step that might seem obvious to some people, but it can be easily ignored if you think you are done. Once you have extracted the needed results, you should always take a retrospective look at your project and think about what you can improve. As you saw throughout this long list of techniques, data analysis is a complex process that requires constant refinement. For this reason, you should always go one step further and keep improving.
Quality Criteria For Data Analysis
So far we’ve covered a list of methods and techniques that should help you perform efficient data analysis. But how do you measure the quality and validity of your results? This is done with the help of some science quality criteria. Here we will go into a more theoretical area that is critical to understanding the fundamentals of statistical analysis in science. However, you should also be aware of these steps in a business context, as they will allow you to assess the quality of your results in the correct way. Let’s dig in.
- Internal validity: The results of a survey are internally valid if they measure what they are supposed to measure and thus provide credible results. In other words , internal validity measures the trustworthiness of the results and how they can be affected by factors such as the research design, operational definitions, how the variables are measured, and more. For instance, imagine you are doing an interview to ask people if they brush their teeth two times a day. While most of them will answer yes, you can still notice that their answers correspond to what is socially acceptable, which is to brush your teeth at least twice a day. In this case, you can’t be 100% sure if respondents actually brush their teeth twice a day or if they just say that they do, therefore, the internal validity of this interview is very low.
- External validity: Essentially, external validity refers to the extent to which the results of your research can be applied to a broader context. It basically aims to prove that the findings of a study can be applied in the real world. If the research can be applied to other settings, individuals, and times, then the external validity is high.
- Reliability : If your research is reliable, it means that it can be reproduced. If your measurement were repeated under the same conditions, it would produce similar results. This means that your measuring instrument consistently produces reliable results. For example, imagine a doctor building a symptoms questionnaire to detect a specific disease in a patient. Then, various other doctors use this questionnaire but end up diagnosing the same patient with a different condition. This means the questionnaire is not reliable in detecting the initial disease. Another important note here is that in order for your research to be reliable, it also needs to be objective. If the results of a study are the same, independent of who assesses them or interprets them, the study can be considered reliable. Let’s see the objectivity criteria in more detail now.
- Objectivity: In data science, objectivity means that the researcher needs to stay fully objective when it comes to its analysis. The results of a study need to be affected by objective criteria and not by the beliefs, personality, or values of the researcher. Objectivity needs to be ensured when you are gathering the data, for example, when interviewing individuals, the questions need to be asked in a way that doesn't influence the results. Paired with this, objectivity also needs to be thought of when interpreting the data. If different researchers reach the same conclusions, then the study is objective. For this last point, you can set predefined criteria to interpret the results to ensure all researchers follow the same steps.
The discussed quality criteria cover mostly potential influences in a quantitative context. Analysis in qualitative research has by default additional subjective influences that must be controlled in a different way. Therefore, there are other quality criteria for this kind of research such as credibility, transferability, dependability, and confirmability. You can see each of them more in detail on this resource .
Data Analysis Limitations & Barriers
Analyzing data is not an easy task. As you’ve seen throughout this post, there are many steps and techniques that you need to apply in order to extract useful information from your research. While a well-performed analysis can bring various benefits to your organization it doesn't come without limitations. In this section, we will discuss some of the main barriers you might encounter when conducting an analysis. Let’s see them more in detail.
- Lack of clear goals: No matter how good your data or analysis might be if you don’t have clear goals or a hypothesis the process might be worthless. While we mentioned some methods that don’t require a predefined hypothesis, it is always better to enter the analytical process with some clear guidelines of what you are expecting to get out of it, especially in a business context in which data is utilized to support important strategic decisions.
- Objectivity: Arguably one of the biggest barriers when it comes to data analysis in research is to stay objective. When trying to prove a hypothesis, researchers might find themselves, intentionally or unintentionally, directing the results toward an outcome that they want. To avoid this, always question your assumptions and avoid confusing facts with opinions. You can also show your findings to a research partner or external person to confirm that your results are objective.
- Data representation: A fundamental part of the analytical procedure is the way you represent your data. You can use various graphs and charts to represent your findings, but not all of them will work for all purposes. Choosing the wrong visual can not only damage your analysis but can mislead your audience, therefore, it is important to understand when to use each type of data depending on your analytical goals. Our complete guide on the types of graphs and charts lists 20 different visuals with examples of when to use them.
- Flawed correlation : Misleading statistics can significantly damage your research. We’ve already pointed out a few interpretation issues previously in the post, but it is an important barrier that we can't avoid addressing here as well. Flawed correlations occur when two variables appear related to each other but they are not. Confusing correlations with causation can lead to a wrong interpretation of results which can lead to building wrong strategies and loss of resources, therefore, it is very important to identify the different interpretation mistakes and avoid them.
- Sample size: A very common barrier to a reliable and efficient analysis process is the sample size. In order for the results to be trustworthy, the sample size should be representative of what you are analyzing. For example, imagine you have a company of 1000 employees and you ask the question “do you like working here?” to 50 employees of which 49 say yes, which means 95%. Now, imagine you ask the same question to the 1000 employees and 950 say yes, which also means 95%. Saying that 95% of employees like working in the company when the sample size was only 50 is not a representative or trustworthy conclusion. The significance of the results is way more accurate when surveying a bigger sample size.
- Privacy concerns: In some cases, data collection can be subjected to privacy regulations. Businesses gather all kinds of information from their customers from purchasing behaviors to addresses and phone numbers. If this falls into the wrong hands due to a breach, it can affect the security and confidentiality of your clients. To avoid this issue, you need to collect only the data that is needed for your research and, if you are using sensitive facts, make it anonymous so customers are protected. The misuse of customer data can severely damage a business's reputation, so it is important to keep an eye on privacy.
- Lack of communication between teams : When it comes to performing data analysis on a business level, it is very likely that each department and team will have different goals and strategies. However, they are all working for the same common goal of helping the business run smoothly and keep growing. When teams are not connected and communicating with each other, it can directly affect the way general strategies are built. To avoid these issues, tools such as data dashboards enable teams to stay connected through data in a visually appealing way.
- Innumeracy : Businesses are working with data more and more every day. While there are many BI tools available to perform effective analysis, data literacy is still a constant barrier. Not all employees know how to apply analysis techniques or extract insights from them. To prevent this from happening, you can implement different training opportunities that will prepare every relevant user to deal with data.
Key Data Analysis Skills
As you've learned throughout this lengthy guide, analyzing data is a complex task that requires a lot of knowledge and skills. That said, thanks to the rise of self-service tools the process is way more accessible and agile than it once was. Regardless, there are still some key skills that are valuable to have when working with data, we list the most important ones below.
- Critical and statistical thinking: To successfully analyze data you need to be creative and think out of the box. Yes, that might sound like a weird statement considering that data is often tight to facts. However, a great level of critical thinking is required to uncover connections, come up with a valuable hypothesis, and extract conclusions that go a step further from the surface. This, of course, needs to be complemented by statistical thinking and an understanding of numbers.
- Data cleaning: Anyone who has ever worked with data before will tell you that the cleaning and preparation process accounts for 80% of a data analyst's work, therefore, the skill is fundamental. But not just that, not cleaning the data adequately can also significantly damage the analysis which can lead to poor decision-making in a business scenario. While there are multiple tools that automate the cleaning process and eliminate the possibility of human error, it is still a valuable skill to dominate.
- Data visualization: Visuals make the information easier to understand and analyze, not only for professional users but especially for non-technical ones. Having the necessary skills to not only choose the right chart type but know when to apply it correctly is key. This also means being able to design visually compelling charts that make the data exploration process more efficient.
- SQL: The Structured Query Language or SQL is a programming language used to communicate with databases. It is fundamental knowledge as it enables you to update, manipulate, and organize data from relational databases which are the most common databases used by companies. It is fairly easy to learn and one of the most valuable skills when it comes to data analysis.
- Communication skills: This is a skill that is especially valuable in a business environment. Being able to clearly communicate analytical outcomes to colleagues is incredibly important, especially when the information you are trying to convey is complex for non-technical people. This applies to in-person communication as well as written format, for example, when generating a dashboard or report. While this might be considered a “soft” skill compared to the other ones we mentioned, it should not be ignored as you most likely will need to share analytical findings with others no matter the context.
Data Analysis In The Big Data Environment
Big data is invaluable to today’s businesses, and by using different methods for data analysis, it’s possible to view your data in a way that can help you turn insight into positive action.
To inspire your efforts and put the importance of big data into context, here are some insights that you should know:
- By 2026 the industry of big data is expected to be worth approximately $273.4 billion.
- 94% of enterprises say that analyzing data is important for their growth and digital transformation.
- Companies that exploit the full potential of their data can increase their operating margins by 60% .
- We already told you the benefits of Artificial Intelligence through this article. This industry's financial impact is expected to grow up to $40 billion by 2025.
Data analysis concepts may come in many forms, but fundamentally, any solid methodology will help to make your business more streamlined, cohesive, insightful, and successful than ever before.
Key Takeaways From Data Analysis
As we reach the end of our data analysis journey, we leave a small summary of the main methods and techniques to perform excellent analysis and grow your business.
17 Essential Types of Data Analysis Methods:
- Cluster analysis
- Cohort analysis
- Regression analysis
- Factor analysis
- Neural Networks
- Data Mining
- Text analysis
- Time series analysis
- Decision trees
- Conjoint analysis
- Correspondence Analysis
- Multidimensional Scaling
- Content analysis
- Thematic analysis
- Narrative analysis
- Grounded theory analysis
- Discourse analysis
Top 17 Data Analysis Techniques:
- Collaborate your needs
- Establish your questions
- Data democratization
- Think of data governance
- Clean your data
- Set your KPIs
- Omit useless data
- Build a data management roadmap
- Integrate technology
- Answer your questions
- Visualize your data
- Interpretation of data
- Consider autonomous technology
- Build a narrative
- Share the load
- Data Analysis tools
- Refine your process constantly
We’ve pondered the data analysis definition and drilled down into the practical applications of data-centric analytics, and one thing is clear: by taking measures to arrange your data and making your metrics work for you, it’s possible to transform raw information into action - the kind of that will push your business to the next level.
Yes, good data analytics techniques result in enhanced business intelligence (BI). To help you understand this notion in more detail, read our exploration of business intelligence reporting .
And, if you’re ready to perform your own analysis, drill down into your facts and figures while interacting with your data on astonishing visuals, you can try our software for a free, 14-day trial .
Data Analysis
Methodology chapter of your dissertation should include discussions about the methods of data analysis. You have to explain in a brief manner how you are going to analyze the primary data you will collect employing the methods explained in this chapter.
There are differences between qualitative data analysis and quantitative data analysis . In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.
Data analysis for quantitative studies, on the other hand, involves critical analysis and interpretation of figures and numbers, and attempts to find rationale behind the emergence of main findings. Comparisons of primary research findings to the findings of the literature review are critically important for both types of studies – qualitative and quantitative.
Data analysis methods in the absence of primary data collection can involve discussing common patterns, as well as, controversies within secondary data directly related to the research area.
John Dudovskiy
A Step-by-Step Guide to the Data Analysis Process
Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.
In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.
Here are the steps we’ll take you through:
- Defining the question
- Collecting the data
- Cleaning the data
- Analyzing the data
- Sharing your results
- Embracing failure
On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.
Ready? Let’s get started with step one.
1. Step one: Defining the question
The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.
Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.
Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”
Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?
Tools to help define your objective
Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.
2. Step two: Collecting the data
Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.
What is first-party data?
First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.
What is second-party data?
To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.
What is third-party data?
Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .
Tools to help you collect data
Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .
Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .
3. Step three: Cleaning the data
Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:
- Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
- Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
- Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
- Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.
A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.
Carrying out an exploratory analysis
Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.
Tools to help you clean your data
Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?
4. Step four: Analyzing the data
Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.
Descriptive analysis
Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.
Learn more: What is descriptive analytics?
Diagnostic analysis
Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!
Predictive analysis
Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.
Prescriptive analysis
Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.
Learn more: What are the different types of data analysis?
5. Step five: Sharing your results
You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.
How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!
Tools for interpreting and sharing your findings
There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!
You can learn more about storytelling with data in this free, hands-on tutorial . We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!
6. Step six: Embrace your failures
The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.
While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.
In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:
- Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
- Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
- Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
- Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
- Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
- Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.
What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.
To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:
- These are the top 9 data analytics tools
- 10 great places to find free datasets for your next project
- How to build a data analytics portfolio
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NCDR: Transforming CV Care Through Data-Driven Insights, Analysis and Research
ACC Scientific Session Newspaper
For more than two decades, ACC's NCDR registries have provided data-driven insights, analysis and research to inform clinical and operational decisions, allowing hospitals and health systems around the world to perform at the highest level and deliver optimal care to every patient, every time.
The NCDR was born 25 years ago out of a quest to answer questions that were beginning to emerge at that time around whether metrics, data collection and outcomes analysis could improve the quality of health care.
"The future of medicine is increasingly in the hands of those who are effective users of clinical data," said Bill Weintraub, MD, MACC , et al., writing in 1997 in JACC about the vision for NCDR.
Since then, what started as a mission to provide quality benchmarking data on individual hospital performance compared to the national average, has grown into a comprehensive suite of registries that are helping measure and quantify quality improvement, identify and close gaps in guideline-recommended care, and optimize the implementation and use of new treatments and therapies across several major clinical areas.
Among its many successes, NCDR has played a key role in helping hospitals and health systems reduce door-to-balloon times to guideline-recommended levels; control costs associated with preventable procedural complications like PCI bleeds; reduce avoidable hospital readmissions; and ensure safe and effective implementation of TAVR in the U.S.
Not to mention, registry data have been used in hundreds of clinical studies published in leading peer-reviewed medical journals, including JACC , and presented at meetings like ACC's Annual Scientific Session .
"While research is not the principal objective of the registry programs, NCDR has now contributed to more than 500 peer-reviewed papers in the medical literature and has done a lot to advance our understanding of cardiovascular care in the 'real world' – more than any other data source imaginable," says Frederick A. Masoudi, MD, MSPH, MACC .
– Janet Wright, MD, FACC
This success and growth of the NCDR is in part due to collaborations across the cardiovascular community, including with the Society of Thoracic Surgeons and the Society for Cardiovascular Angiography and Interventions.
"The NCDR has shown how intersocietal collaboration and cooperation can contribute to the safe and efficacious development of innovative medical technologies that have transformed the practice of cardiovascular surgery and medicine," says William J. Oetgen, MD, MBA, MACC , who cites the STS/ACC TVT Registry, which celebrated its 10-year anniversary in 2022, as one of the best examples of collaboration in action. New research from the registry analyzing the safety and efficacy of transcatheter edge-to-edge mitral repair in degenerative mitral regurgitation will be part of the Feature Clinical Research II session taking place tomorrow.
External influencers like the Centers for Medicare and Medicaid Services (CMS), U.S. Food and Drug Administration, Centers for Disease Control and Prevention, National Quality Forum, payers, industry stakeholders and innovation partners have also played an important role in NCDR's growth and expansion.
"High quality cardiovascular care is a team sport and NCDR has operated, from the beginning, with a broad definition of 'team,'" says Janet Wright, MD, FACC , one of this year's Distinguished Award Winners who will be recognized on Monday during Convocation. "Over its history, NCDR leaders have listened to the needs of their stakeholders. The results are registries, processes and support services that meet or exceed those needs for access to timely data and expertise, insights into key issues in cardiovascular care, and educational opportunities ready-made for clinicians, practices and health systems."
Looking ahead, as the U.S. health care system continues to transition to a value-based model, the need to track health care outcomes through registry programs like the NCDR is only more critical. Continuing to leverage new technologies to ease data burden and streamline clinician and even patient access is also important. In addition, the COVID-19 pandemic has further highlighted the critical global need to address health equity and social determinants of health. The NCDR has a real opportunity to help lead and drive solutions in this area.
"The growth of the NCDR registry portfolio from the very first registry in 1997 to our current suite of registries has helped hospitals and other facilities around the world improve their patient outcomes and play a central role in transforming cardiovascular care through use of robust risk adjustment, hospital and physical benchmarking, and important feedback," says Ralph G. Brindis, MD, MPH, MACC . "Looking ahead to the next 25 years, there's a real opportunity to leverage the timely data, expertise and real-world insights to foster and grow a true local, national and international learning health care environment."
Clinical Topics: Cardiac Surgery, COVID-19 Hub, Invasive Cardiovascular Angiography and Intervention, Valvular Heart Disease, Aortic Surgery, Cardiac Surgery and VHD, Interventions and Imaging, Interventions and Structural Heart Disease, Angiography, Nuclear Imaging, Mitral Regurgitation
Keywords: ACC23, ACC Annual Scientific Session, ACC Scientific Session Newspaper, ACC.23/WCC Meeting Newspaper, National Cardiovascular Data Registries, ACC Accreditation, Patient Readmission, Medicaid, Centers for Medicare and Medicaid Services, U.S., Mitral Valve Insufficiency, Percutaneous Coronary Intervention, Transcatheter Aortic Valve Replacement, COVID-19, Surgeons, Angiography
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How to Conduct Data Analysis
Last Updated: October 6, 2020 References
This article was co-authored by Bess Ruff, MA . Bess Ruff is a Geography PhD student at Florida State University. She received her MA in Environmental Science and Management from the University of California, Santa Barbara in 2016. She has conducted survey work for marine spatial planning projects in the Caribbean and provided research support as a graduate fellow for the Sustainable Fisheries Group. There are 13 references cited in this article, which can be found at the bottom of the page. This article has been viewed 85,523 times.
Data analysis is an important step in answering an experimental question. Analyzing data from a well-designed study helps the researcher answer questions. With this data, you can also draw conclusions that further the research and contribute to future studies. Keeping well-organized data during the collection process will help make the analysis step that much easier.
Organizing the Data
- Take care when transferring data into the master spreadsheet. It is easy to accidentally copy and paste into the wrong columns or rows.
- In case something does happen to the data, you can always go back to the original master file.
- Code “No” responses as “0” and “Yes” responses as “1.”
- It may be easiest to keep all of your groups on separate sheets within one document, completely separate documents, or different columns/rows within the same sheet.
- Talk to others who have done similar data analysis to get an idea of how best to organize your data.
- For example: If you want to know differences between males and females, you would want to make sure all of the male data was grouped together and all of the female data was grouped together.
- If you have to manually enter data, make sure to double-check everything that gets entered.
Choosing Statistical Tests
- One sample t-tests are generally used in physics and product manufacturing: you know the value that your sample should have so you compare the average that you get to that known value. [6] X Research source
- Two sample t-tests are commonly used in the biomedical and clinical fields.
- A one-way ANOVA can be used to compare the means of multiple groups to one control group. For example, if you had one control group and three test groups, you would use a one-way ANOVA to compare all of the means and see if any are different. [7] X Research source
- A two-way ANOVA is used to compare the means of multiple groups with multiple variables. For example, if you wanted to know if both genotype and sex of an organism affected your data, you would run a two-way ANOVA against the control groups. [8] X Research source
- This test is used when you want to measure the strength of association between two variables.
- For example, if you wanted to test the relationship between your heart rate and the speed you move on a treadmill, you would use a linear regression.
- For example, if you wanted to test to see if males and females had different resting heart rates at different temperatures you would use an ANCOVA. You would make two regression lines (one for females and one for males) of heart rate vs temperature. Then you would use an ANCOVA to compare the two lines to see if they were different.
- There are some helpful charts and articles online to assist you in choosing a test based on the data you are collecting. [11] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source
- Look at articles from the NIH and universities or online statistics books for more information.
Analyzing the Data
- Before you begin collecting data, you should know exactly how many samples you are going to collect in each group and what statistical tests you will run.
- It is also a good idea to meet with them again after the data has been collected. They can help you analyze the data and make sure everything has been done properly.
- Ask them about the proper size of your study, what types of statistical tests will help you answer your research questions, and what the limitations of the tests are.
- Remember, a statistical test simply tells you the probability of an outcome occurring or not occurring. You must be careful not to confuse statistical significance with clinical significance or physiological relevance. [13] X Research source
- SAS, Stata, and R require some programming experience. You may need to consult someone trained to use these programs or take a course to become proficient in their use.
Presenting the Data
- Commonly used programs are GraphPad Prism and R.
- If you have multiple datasets on a single graph, make sure they are all properly labeled.
- Make sure the figure legend explains what the asterisk means, what statistical test was used, and what the actual p-value of the test was.
- Many programs have graph editors that also allow you to make layouts of multiple graphs.
- Make sure all of the graphs have the same font sizes and use the same symbols between datasets.
- Details about the statistics should be included in the legend as well: z-scores, t-scores, p-values, degrees of freedom, etc.
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- ↑ http://toolkit.pellinstitute.org/evaluation-guide/analyze/enter-organize-clean-data/
- ↑ https://www.wilder.org/sites/default/files/imports/crimevictimservices13_2-08Web.pdf
- ↑ http://www.biostathandbook.com/testchoice.html
- ↑ http://www.biostathandbook.com/onesamplettest.html
- ↑ http://www.biostathandbook.com/onewayanova.html
- ↑ http://www.biostathandbook.com/twowayanova.html
- ↑ http://www.biostathandbook.com/linearregression.html
- ↑ http://www.biostathandbook.com/ancova.html
- ↑ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3116565/
- ↑ https://ori.hhs.gov/education/products/n_illinois_u/datamanagement/datopic.html
- ↑ http://cellbio.emory.edu/bnanes/figures/
- ↑ http://www.scidev.net/global/publishing/practical-guide/how-do-i-write-a-scientific-paper-.html
- ↑ http://www.biosciencewriters.com/Tips-for-Writing-Outstanding-Scientific-Figure-Legends.aspx
About This Article
To conduct data analysis, you’ll need to keep your information well organized during the collection process. Use an electronic database, such as Excel, to organize all of your data in an easily searchable spreadsheet. If you’re working with survey data that has written responses, you can code the data into numerical form before analyzing it. When you’re ready to start analyzing your data, run all of the tests you decided on before the experiment began. For example, if you need to compare the means of samples, use a t-test. Alternatively, to analyze means of groups, you’ll want to use an analysis of variance. To learn how to present your data, keep reading! Did this summary help you? Yes No
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Quantitative Data Analysis: Methods & Techniques Simplified 101
Ofem Eteng • May 18th, 2022
Data Analysis is an important part of research as a weak analysis will produce an inaccurate report that will cause the findings to be faulty, invariably leading to wrong and poor decision-making. It is, therefore, necessary to choose an adequate data analysis method that will ensure you obtain reliable and actionable insights from your data.
Table of Contents
Finding patterns, connections, and relationships from your data can be a daunting task but with the right data analysis method and tools in place, you can run through the chunk of data you have to come up with information regarding it. There are different data analysis methods available, this article is going to focus on quantitative data analysis and discuss the methods and techniques associated with it.
You will learn about Quantitative Data Analysis in this article. You will also obtain a comprehensive understanding of Quantitative Data Analysis, including the methods and techniques involved. Continue reading to learn more about Quantitative Data Analysis.
What is Quantitative Data Analysis?
Data preparation steps for quantitative data analysis.
- Descriptive Statistics
- Inferential Statistics
Data Analysis can be explained as the process of discovering useful information by evaluating data whereas quantitative data analysis can be defined as the process of analyzing data that is number-based or data that can easily be converted into numbers. It is based on describing and interpreting objects statistically and with numbers as it aims to interpret the data collected through numeric variables and statistics.
Quantitative data analysis techniques typically work with algorithms, mathematical analysis tools, and software to gain insights from the data, answering questions such as how many, how often, and how much. Data for quantitative data analysis is usually gotten from avenues like surveys, questionnaires, polls, etc. data can also come from sales figures, email click-through rates, number of website visitors, and percentage revenue increase.
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Quantitative data has to be gathered and cleaned before proceeding to the stage of analyzing it. This step is very important and has to be discussed before mentioning the methods and techniques involved because, if the data is not gathered correctly and cleaned, the analysis may not be carried out properly leading to wrong findings, wrong judgments on the hypothesis, and misinterpretation, therefore, leading to decisions made upon statistics that did not accurately represent the dataset.
To prepare data for quantitative data analysis simply means to convert it to meaningful and readable formats, below are the steps to achieve this:
- Data Validation: This is to evaluate if the data was collected correctly through the required channels and to ascertain if it meets the set-out standards stated from the onset. This can be done by checking if the procedure was followed, making sure that the respondents were chosen based on the research criteria, and checking for completeness in the data.
- Data Editing: Large datasets may include errors where fields may be filled incorrectly or left empty accidentally. To avoid having a faulty analysis, data checks should be done to identify and clear out anything that may lead to an inaccurate result.
- Data Coding: This involves grouping and assigning values to data. It might mean forming tables and structures to represent the data accurately.
Now that you are familiar with what quantitative data analysis is and how to prepare your data for analysis, the focus will shift to the purpose of this article which is the methods and techniques of quantitative data analysis.
Methods and Techniques of Quantitative Data Analysis
Quantitative data analysis involves the use of computational and statistical methods that focuses on the statistical, mathematical, or numerical analysis of datasets. It starts with a descriptive statistical phase and is followed up with a closer analysis if needed to derive more insight such as correlation, and the production of classifications based on the descriptive statistical analysis.
As can be deduced from the statement above, there are two main commonly used quantitative data analysis methods namely the descriptive statistics used to explain certain phenomena and inferential statistics used to make predictions. Both methods are used in different ways having techniques unique to them. An explanation of both methods is done below.
1) Descriptive Statistics
Descriptive statistics as the name implies is used to describe a dataset. It helps understand the details of your data by summarizing it and finding patterns from the specific data sample. They provide absolute numbers gotten from a sample but do not necessarily explain the rationale behind the numbers and are mostly used for analyzing single variables. The methods used in descriptive statistics include:
- Mean: This is used to calculate the numerical average of a set of values.
- Median: This is used to get the midpoint of a set of values when the numbers are arranged in numerical order.
- Mode: This is used to find the most commonly occurring value in a dataset.
- Percentage: This is used to express how a value or group of respondents within the data relates to a larger group of respondents.
- Frequency: This indicates the number of times a value is found.
- Range: This shows the highest and lowest value in a set of values.
- Standard Deviation: This is used to indicate how dispersed a range of numbers is, meaning, it shows how close all the numbers are to the mean.
- Skewness: It indicates how symmetrical a range of numbers is, showing if they cluster into a smooth bell curve shape in the middle of the graph or if they skew towards the left or right.
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2) Inferential Statistics
In quantitative analysis, the expectation is to turn raw numbers into meaningful insight using numerical values and descriptive statistics is all about explaining details of a specific dataset using numbers, but, it does not explain the motives behind the numbers hence, the need for further analysis using inferential statistics.
Inferential statistics aim to make predictions or highlight possible outcomes from the analyzed data obtained from descriptive statistics. They are used to generalize results and make predictions between groups, show relationships that exist between multiple variables, and are used for hypothesis testing that predicts changes or differences.
They are various statistical analysis methods used within inferential statistics, a few are discussed below.
- Cross Tabulations: Cross tabulation or crosstab is used to show the relationship that exists between two variables and is often used to compare results by demographic groups. It uses a basic tabular form to draw inferences between different data sets and contains data that is mutually exclusive or has some connection with each other. Crosstabs are helpful in understanding the nuances of a dataset and factors that may influence a data point.
- Regression Analysis: Regression analysis is used to estimate the relationship between a set of variables. It is used to show the correlation between a dependent variable (the variable or outcome you want to measure or predict) and any number of independent variables (factors that may have an impact on the dependent variable). Therefore, the purpose of the regression analysis is to estimate how one or more variables might have an effect on a dependent variable to identify trends and patterns to make predictions and forecast possible future trends. There are many types of regression analysis and the model you choose will be determined by the type of data you have for the dependent variable. The types of regression analysis include linear regression, non-linear regression, binary logistic regression, etc.
- Monte Carlo Simulation: Monte Carlo simulation also known as the Monte Carlo method is a computerized technique of generating models of possible outcomes and showing their probability distributions. It considers a range of possible outcomes and then tries to calculate how likely each outcome will occur. It is used by data analysts to perform an advanced risk analysis to help in forecasting future events and taking decisions accordingly.
- Analysis of Variance (ANOVA): This is used to test the extent to which two or more groups differ from each other. It compares the mean of various groups and allows the analysis of multiple groups.
- Factor Analysis: A large number of variables can be reduced into a smaller number of factors using the factor analysis technique. It works on the principle that multiple separate observable variables correlate with each other because they are all associated with an underlying construct. It helps in reducing large datasets into smaller, more manageable samples.
- Cohort Analysis: Cohort analysis can be defined as a subset of behavioral analytics that operates from data taken from a given dataset. Rather than looking at all users as one unit, cohort analysis breaks down data into related groups for analysis where these groups or cohorts usually have common characteristics or similarities within a defined period.
- MaxDiff Analysis: This is a quantitative data analysis method that is used to gauge customers’ preferences for purchase and what parameters rank highest than the others in the process.
- Cluster Analysis: Cluster analysis is a technique used to identify structures within a dataset. Cluster analysis aims to be able to sort different data points into groups that are internally similar and externally different, that is, data points within a cluster will look like each other and different from data points in other clusters.
- Time Series Analysis: This is a statistical analytic technique used to identify trends and cycles over time. It is simply the measurement of the same variables at different points in time like weekly, and monthly email sign-ups to uncover trends, seasonality, and cyclic patterns. By doing this, the data analyst can forecast how variables of interest may fluctuate in the future.
- SWOT analysis: This is a quantitative data analysis method that assigns numerical values to indicate strengths, weaknesses, opportunities, and threats of an organization, product, or service to show a clearer picture of competition to foster better business strategies
This write-up has talked about quantitative data analysis showing that it is all about analyzing number-based data or converting data into the numerical format by using various statistical techniques to deduce useful insights. It went further to show that there are two methods used in quantitative analysis, descriptive and inferential stating when and how each of these methods can be used by giving techniques associated with them.
Finally, to carry out effective quantitative data analysis, one has to consider the type of data you are working with, the purpose of carrying out such analysis, and the hypothesis or outcome that may be gotten from the analysis.
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5 qualitative data analysis methods
Qualitative data uncovers valuable insights that can be used to improve the user and customer experience. But how exactly do you measure and analyze data that isn't quantifiable?
There are different qualitative data analysis methods to help you make sense of qualitative feedback and customer insights, depending on your business goals and the type of data you've collected.
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Before you choose a qualitative data analysis method for your team, you need to consider the available techniques and explore their use cases to understand how each process might help your team better understand your users.
This guide covers five qualitative analysis methods to choose from, and will help you pick the right one(s) based on your goals.
What is qualitative data analysis?
Qualitative data analysis ( QDA ) is the process of organizing, analyzing, and interpreting qualitative data—non-numeric, conceptual information and user feedback—to capture themes and patterns, answer research questions, and identify actions to take to improve your product or website.
💡 Qualitative data often refers to user behavior data and customer feedback .
Use product experience insights software—like Hotjar's Observe and Ask tools —to capture qualitative data with context, and learn the real motivation behind user behavior.
Hotjar’s feedback widget lets your customers share their opinions
5 qualitative data analysis methods explained
Here are five methods of qualitative data analysis to help you make sense of the data you've collected through customer interviews, surveys, and feedback:
Content analysis
Thematic analysis
Narrative analysis
Grounded theory analysis
Discourse analysis
Let’s look at each method one by one, using real examples of qualitative data analysis .
1. Content analysis
Content analysis is a research method that examines and quantifies the presence of certain words, subjects, and concepts in text, image, video, or audio messages. The method transforms qualitative input into quantitative data to help you make reliable conclusions about what customers think of your brand, and how you can improve their experience and opinion.
You can conduct content analysis manually or by using tools like Lexalytics to reveal patterns in communications, uncover differences in individual or group communication trends, and make connections between concepts.
Content analysis was a major part of our growth during my time at Hypercontext.
[It gave us] a better understanding of the [blog] topics that performed best for signing new users up. We were also able to go deeper within those blog posts to better understand the formats [that worked].
How content analysis can help your team
Content analysis is often used by marketers and customer service specialists, helping them understand customer behavior and measure brand reputation.
For example, you may run a customer survey with open-ended questions to discover users’ concerns—in their own words—about their experience with your product. Instead of having to process hundreds of answers manually, a content analysis tool helps you analyze and group results based on the emotion expressed in texts.
Some other examples of content analysis include:
Analyzing brand mentions on social media to understand your brand's reputation
Reviewing customer feedback to evaluate (and then improve) the customer and user experience (UX)
Researching competitors’ website pages to identify their competitive advantages and value propositions
Interpreting customer interviews and survey results to determine user preferences, and setting the direction for new product or feature developments
Content analysis benefits and challenges
Content analysis has some significant advantages for small teams:
You don’t need to directly interact with participants to collect data
The process is easily replicable once standardized
You can automate the process or perform it manually
It doesn’t require high investments or sophisticated solutions
On the downside, content analysis has certain limitations:
When conducted manually, it can be incredibly time-consuming
The results are usually affected by subjective interpretation
Manual content analysis can be subject to human error
The process isn’t effective for complex textual analysis
2. Thematic analysis
Thematic analysis helps to identify, analyze, and interpret patterns in qualitative data , and can be done with tools like Dovetail and Thematic .
While content analysis and thematic analysis seem similar, they're different in concept:
Content analysis can be applied to both qualitative and quantitative data , and focuses on identifying frequencies and recurring words and subjects.
Thematic analysis can only be applied to qualitative data, and focuses on identifying patterns and ‘themes’.
How thematic analysis can help your team
Thematic analysis can be used by pretty much anyone: from product marketers, to customer relationship managers, to UX researchers.
For example, product teams can use thematic analysis to better understand user behaviors and needs, and to improve UX . By analyzing customer feedback , you can identify themes (e.g. ‘poor navigation’ or ‘buggy mobile interface’) highlighted by users, and get actionable insight into what users really expect from the product.
Thematic analysis benefits and challenges
Some benefits of thematic analysis:
It’s one of the most accessible analysis forms, meaning you don’t have to train your teams on it
Teams can easily draw important information from raw data
It’s an effective way to process large amounts of data into digestible summaries
And some drawbacks of thematic analysis:
In a complex narrative, thematic analysis can't capture the true meaning of a text
Thematic analysis doesn’t consider the context of the data being analyzed
Similar to content analysis, the method is subjective and might drive results that don't necessarily align with reality
3. Narrative analysis
Narrative analysis is a method used to interpret research participants’ stories —things like testimonials, case studies, interviews, and other text or visual data—with tools like Delve and AI-powered ATLAS.ti .
Some formats narrative analysis doesn't work for are heavily-structured interviews and written surveys, which don’t give participants as much opportunity to tell their stories in their own words .
How narrative analysis can help your team
Narrative analysis provides product teams with valuable insight into the complexity of customers’ lives, feelings, and behaviors .
In a marketing research context, narrative analysis involves capturing and reviewing customer stories—on social media, for example—to get more insight into their lives, priorities, and challenges.
This might look like analyzing daily content shared by your audiences’ favorite influencers on Instagram, or analyzing customer reviews on sites like G2 or Capterra to understand individual customers' experiences.
Narrative analysis benefits and challenges
Businesses turn to narrative analysis for a number of reasons:
The method provides you with a deep understanding of your customers' actions—and the motivations behind them
It allows you to personalize customer experiences
It keeps customer profiles as wholes, instead of fragmenting them into components that can be interpreted differently
However, this data analysis method also has drawbacks:
Narrative analysis cannot be automated
It requires a lot of time and manual effort to make conclusions on an individual participant’s story
It’s not scalable
4. Grounded theory analysis
Grounded theory analysis is a method of conducting qualitative research to develop theories by examining real-world data. The technique involves the creation of hypotheses and theories through the collection and evaluation of qualitative data, and can be performed with tools like MAXQDA and Delve.
Unlike other qualitative data analysis methods, this technique develops theories from data, not the other way round.
How grounded theory analysis can help your team
Grounded theory analysis is used by software engineers, product marketers, managers, and other specialists that deal with data to make informed business decisions .
For example, product marketing teams may turn to customer surveys to understand the reasons behind high churn rates, then use grounded theory to analyze responses and develop hypotheses about why users churn, and how you can get them to stay.
Grounded theory can also be helpful in the talent management process. For example, HR representatives may use it to develop theories about low employee engagement, and come up with solutions based on their findings.
Grounded theory analysis benefits and challenges
Here’s why teams turn to grounded theory analysis:
It explains events that can’t be explained with existing theories
The findings are tightly connected to data
The results are data-informed, and therefore represent the proven state of things
It’s a useful method for researchers that know very little information on the topic
Some drawbacks of grounded theory are:
The process requires a lot of objectivity, creativity, and critical thinking from researchers
Because theories are developed based on data instead of the other way around, it's considered to be overly theoretical, and may not provide concise answers to qualitative research questions
5. Discourse analysis
Discourse analysis is the act of researching the underlying meaning of qualitative data. It involves the observation of texts, audio, and videos to study the relationships between the information and its context .
In contrast to content analysis, the method focuses on the contextual meaning of language: discourse analysis sheds light on what audiences think of a topic, and why they feel the way they do about it.
How discourse analysis can help your team
In a business context, the method is primarily used by marketing teams. Discourse analysis helps marketers understand the norms and ideas in their market , and reveals why they play such a significant role for their customers.
Once the origins of trends are uncovered, it’s easier to develop a company mission, create a unique tone of voice, and craft effective marketing messages.
Discourse analysis benefits and challenges
Discourse analysis has the following benefits:
It uncovers the motivation behind your customers’ or employees’ words, written or spoken
It helps teams discover the meaning of customer data, competitors’ strategies, and employee feedback
But it also has drawbacks:
Similar to most qualitative data analysis methods, discourse analysis is subjective
The process is time-consuming and labor-intensive
It’s very broad in its approach
Which qualitative data analysis method should you choose?
While the five qualitative data analysis methods we list above are aimed at processing data and answering research questions, these techniques differ in their intent and the approaches applied.
Choosing the right analysis method for your team isn't a matter of preference—selecting a method that fits is only possible when you define your research goals and have a clear intention. Once you know what you need (and why you need it), you can identify an analysis method that aligns with your objectives.
Gather qualitative data with Hotjar
Use Hotjar’s product experience insights in your qualitative research. Collect feedback, uncover behavior trends, and understand the ‘why’ behind user actions.
FAQs about qualitative data analysis methods
What is the qualitative data analysis approach.
The qualitative data analysis approach refers to the process of systematizing descriptive data collected through interviews, surveys, and observations and interpreting it. The method aims to identify patterns and themes behind textual data.
What are qualitative data analysis methods?
Five popular qualitative data analysis methods are:
What is the process of qualitative data analysis?
The process of qualitative data analysis includes six steps:
Define your research question
Prepare the data
Choose the method of qualitative analysis
Code the data
Identify themes, patterns, and relationships
Make hypotheses and act
Qualitative data analysis guide
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Big Data Analytics
Established: October 18, 2012
Research Areas
Big Data Analytics Lecture Series
Small Summaries for Big Data
Graham Cormode – AT&T Research
July 2nd, 2012, 16:00-17:00, Microsoft Research Cambridge, Jasmine Room
Abstract: In dealing with big data, we often need to look at a small summary to get the big picture. Over recent years, many new techniques have been developed which allow important properties of large distributions to be extracted from compact and easy-to-build summaries. In this talk, I’ll highlight some examples of different types of summarization: sampling, sketching, and special-purpose. Then I’ll outline the road ahead for further development of such summaries.
About the speaker: Homepage
Testing Properties of Discrete Distributions
Tugkan Batu – London School of Economics
May 15th, 2012, 16:00-17:00, Microsoft Research Cambridge, Lecture Theatre Small
Abstract: In this talk, we will survey some algorithmic results for several fundamental statistical inference tasks. The algorithms are given access only to i.i.d. samples from the discrete input distributions and make inferences based on these samples. The main focus of this research is the sample complexity of each task as a function of the domain size for the underlying discrete probability distributions. The inference tasks studied include (i) similarity to a fixed distribution (i.e., goodness-of-fit); (ii) similarity between two distributions (i.e., homogeneity); (iii) independence of joint distributions; and (iv) entropy estimation. For each of these tasks, an algorithm with sublinear sample complexity is presented (e.g., a goodness-of-fit test on a discrete domain of size $n$ is shown to require $O(sqrt{n}polylog(n))$ samples). Given certain extra information on the distributions (such as the distribution is monotone or unimodal with respect to a fixed total order on the domain), the sample complexity of these tasks become polylogarithmic in the domain size.
Streaming Pattern Matching
Ely Porat – Bar-Ilan University, Israel
May 1st, 2012, 15:00-16:00, Microsoft Research Cambridge, Primrose Room
Abstract: We present a fully online randomized algorithm for the classical pattern matching problem that uses merely O(log m) space, breaking the O(m) barrier that held for this problem for a long time. Our method can be used as a tool in many practical applications, including monitoring Internet traffic and firewall applications. In our online model we first receive the pattern P of size m and preprocess it. After the preprocessing phase, the characters of the text T of size n arrive one at a time in an online fashion. For each index of the text input we indicate whether the pattern matches the text at that location index or not. Clearly, for index i, an indication can only be given once all characters from index i till index i+m-1 have arrived. Our goal is to provide such answers while using minimal space, and while spending as little time as possible on each character (time and space which are in O(poly(log n))).
Basic Probabilistic Load Balancing Mechanisms
Tom Friedetzky – Durham University
April 24th, 2012, 15:00-16:00, Microsoft Research Cambridge, Lecture Theatre Large
Abstract: In this talk we will discuss a number of basic yet useful load balancing mechanisms for parallel and distributed computations, based on random allocation (“balls into bins”) and randomised work stealing. The focus will be on approaches that lend themselves to thorough mathematical analysis but that, due to their simplicity and general easiness on assumptions, may be considered to be good, all-purpose performers. The talk will mention theoretical results and occasionally hint at proof strategies but most parts will be accessible to a general audience.
Large Scale Semidefinite Programming
Alexandre d’Aspremont – Ecole Polytechnique, France
April 17th, 2012, 15:00-16:00, Microsoft Research Cambridge, Primrose Room
Abstract: The talk will start by a brief introduction on semidefinite programming. It will discuss some recent advances in large scale semidefinite programming solvers, detailing in particular stochastic smoothing techniques for the maximum eigenvalue function.
Joint work with Noureddine El Karoui at U.C. Berkeley.
The Online Approach to Machine Learning
Nicolo Cesa-Bianchi – Universita degli Studi di Milano
February 28th, 2012, 14:00-15:00, Microsoft Research Cambridge, Large Lecture Theatre
Abstract: Online learning has become a standard tool in machine learning and large-scale data analysis. Learning is viewed as a repeated game between an adaptive agent and an ever-changing environment. Within this simple paradigm, one can model a variety of sequential decision tasks simply by specifying the interaction protocol and the resource constraints on the agent. In the talk we will first highlight some of the main features of online learning algorithms, such as simplicity, locality, scalability, and robustness. Then, we will describe algorithmic applications to specific learning scenarios (partial feedback, attribute-efficient, multitask, semi-supervised, transductive, and more) showing how diverse settings can be effectively captured within the same conceptual framework.
About the speaker: Nicolo Cesa-Bianchi
Logic and Analysis Issues in Web-based Data Integration
Michael Benedikt – Oxford University
January 31st, 2012, 14:00-15:00, Microsoft Research Cambridge, Primrose Room
Abstract: A prime driver for much database research over the past decade has been providing unified structured (relational) query interfaces on top of web-based datasources. There are a range of issues that come up in doing this I will talk try to give an idea of a few of them, focusing on several we have worked on at Oxford: analysis of dynamic access plans, answerability of queries on the Web, and analysis of web pages to support query answering.
This is joint work with Pierre Bourhis, Tim Furche, Georg Gottlob, Andreas Savvides, and Pierre Senellart.
Collaborators
- Srikanta Tirthapura, Iowa State University, to join May 2013
- Ely Porat, Bar-Ilan University, Israel, June 2012
- Nan Li, University of California Santa Barbara, 2012/13
- Charalampos Tsourakakis, CMU, 2012
- Zengfeng Huang, Hong Kong University of Science and Technology, 2012
- Alan Roytman, UCLA, 2012
- Zhenming Liu, Harward University, now a post-doc at Princeton University, 2011
- Dinkar Vasudevan, EPFL, 2008
- Microsoft Online Services Division
Bozidar Radunovic
Senior Principal Researcher
Christos Gkantsidis
Principal Researcher
Thomas Karagiannis
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Top 4 Data Analysis Techniques That Create Business Value
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Tables of Contents
What is data analysis?
Quantitative data analysis techniques, qualitative data analysis techniques, a closer look at statistical techniques for data analysis, unlocking the business value of data analysis techniques.
- Poor data quality . Causes of poor data quality include system problems, human error, and outdated data. According to Gartner, organizations lose about $15 million a year due to poor data quality issues, making the business case for data quality improvement a high priority.
- Absence of an effective data strategy . PwC estimates that organizations can make decisions five times faster with an effective data strategy that ensures the data is protected, of high quality and value, and usable for business purposes.
- Difficulty in finding skilled employees . According to a report from SHRM, 75% of survey respondents say that the global skills shortage has made recruiting qualified candidates more difficult. Data analysis skills are one of the top three missing technical skills, according to the report.
- Lack of executive sponsorship . Companies fail to become data-driven for various reasons. However, building a data-centric culture can only succeed when executives at the highest levels of an organization are committed to unlocking data’s value.
- Data silos . The lack of a single source of truth may result in data silos, disparate collections of information not effectively shared. Effective data governance can break down these data silos and enable organizations to extract business value from their data.
Data-driven companies can extract business value from data through human ingenuity and data analysis, a process of drawing information from data to make informed decisions.
Data analysis is a technique that typically involves multiple activities such as gathering, cleaning, and organizing the data. These processes, which usually include data analysis software, are necessary to prepare the data for business purposes. Data analysis is also known as data analytics , described as the science of analyzing raw data to draw informed conclusions based on the data.
Data comes in different structures , formats, and types, including the following:
- Big data . Big data is defined as a huge data set that continues to grow at an exponential rate over time. The four fundamental characteristics of big data are volume, variety, velocity, and variability. Volume describes quantity, velocity refers to the speed of data growth, and variety indicates different data sources. Veracity speaks to the quality of the data , determining if it provides business value or not.
- Structured/unstructured data . Structured data is a predefined data model such as a traditional row-column database. Unstructured data comes in a format that does not fit in rows and columns and can include videos, photos, audio, text, and more. A comparison of structured data versus unstructured data reveals that structured data is easier to manage and analyze.
- Metadata . Metadata is a form of data that describes and provides information about other data. For example, metadata for an image can include the author, image type, and date created. Metadata enables users to organize unstructured data into categories, making it easier to work with.
- Real-time data . Data that is presented as soon as it is acquired is known as real-time data . This type of data is useful when decisions require up-to-the-minute information. For example, a stockbroker can use a stock market ticker to track the most active stocks in real time .
- Machine data . Thanks to the Internet of Things (IoT), sensors, and other technologies, data can be automatically generated by factory systems and other machines, information technology and telecommunications infrastructure, smart cars, hand-held devices, and more. This type of data is known as machine data because it is produced wholly by machines without human instruction.
Data analysis methods and techniques are useful for finding insights in data, such as metrics, facts, and figures. The two primary methods for data analysis are qualitative data analysis techniques and quantitative data analysis techniques. These data analysis techniques can be used independently or in combination with the other to help business leaders and decision-makers acquire business insights from different data types .
Quantitative data analysis
Quantitative data analysis involves working with numerical variables — including statistics, percentages, calculations, measurements, and other data — as the nature of quantitative data is numerical. Quantitative data analysis techniques typically include working with algorithms, mathematical analysis tools, and software to manipulate data and uncover insights that reveal the business value.
For example, a financial data analyst can change one or more variables on a company’s Excel balance sheet to project their employer’s future financial performance. Quantitative data analysis can also be used to assess market data to help a company set a competitive price for its new product.
Qualitative data analysis
Qualitative data describes information that is typically nonnumerical. The qualitative data analysis approach involves working with unique identifiers, such as labels and properties, and categorical variables, such as statistics, percentages, and measurements. A data analyst may use firsthand or participant observation approaches, conduct interviews, run focus groups, or review documents and artifacts in qualitative data analysis .
Qualitative data analysis can be used in various business processes. For example, qualitative data analysis techniques are often part of the software development process. Software testers record bugs — ranging from functional errors to spelling mistakes — to determine bug severity on a predetermined scale: from critical to low. When collected, this data provides information that can help improve the final product.
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Two data analysis techniques for quantitative data are regression analysis (which examines relationships between two variables) and hypothesis analysis (which tests whether a hypothesis is true). Two data analysis techniques for qualitative data are content analysis (which measures content changes over time and across media) and discourse analysis (which explores conversations in their social context).
Each of the various quantitative data analysis techniques has a different approach to extracting value from the data. For example, a Monte Carlo Simulation is a quantitative data analysis technique that simulates and estimates the probability of outcomes in uncertain conditions in fields such as finance, engineering, and science. A provider of mobile telecommunications services can use it to analyze network performance using different scenarios to find opportunities to optimize its service. Other quantitative data types and examples include cross-tabulation and trend analysis.
Below are descriptions and typical steps involved in two popular quantitative data analysis techniques: regression analysis and hypothesis analysis.
Regression analysis
Regression analysis is a type of statistical analysis method that determines the relationships between independent and dependent variables. In finance, regression is defined as a method to help investment and financial managers value assets and determine variable relationships in commodity prices and stocks.
Through experiments that involve manipulating the values of independent variables, a quantitative data analyst can assess the impact of the changes on the dependent variable. The process can be thought of in terms of cause and effect. For example, an independent variable can be the amount an individual invests in the stock market with the dependent variable the total amount of money an individual will have when they retire.
The two primary types of regression analysis are simple linear and multiple linear.
Simple linear regression analysis
A simple linear regression analysis formula includes a dependent variable and an independent variable. The mathematical representation of the dependent variable is typically Y, while X represents the independent variable.
An example of the use of linear regression is a market researcher analyzing the relationship between their company’s products and customer satisfaction. By ranking customer satisfaction levels on a scale of 1 to 10, the market researcher can place numerical values on the data collected. Using these quantitative data, they can perform a regression analysis to determine a linear relationship between a product (independent variable) and customer satisfaction (dependent variable).
Multiple linear regression analysis
Multiple linear regression analysis also includes a dependent variable. The main difference is that it contains various independent variables, resulting in a potentially complex formula for performing a regression analysis. However, tools such as Microsoft Excel and statistics software such as SPSS can simplify the task of multiple linear regression analysis.
Hypothesis analysis
Hypothesis analysis is a data analysis technique that uses sample data to test a hypothesis. Hypothesis analysis is a statistical test method to validate an assumption and determine if it’s plausible or factual. In this approach, an analyst develops two hypotheses — only one of them can be true. Two foundational components of hypothesis analysis are the null hypothesis and the alternative hypothesis.
Null hypothesis
The first hypothesis is the null hypothesis. Null means no difference between two groups represented in the data. For example, a null hypothesis would claim that no difference in school achievement exists between students from high-income communities (group 1) and those from low-income areas (group 2). In performing a hypothesis analysis, the aim of the researcher or analyst is to demonstrate that a difference does exist between the groups in the study, therefore rejecting the validity of the null hypothesis.
Alternative hypothesis
The alternative hypothesis is typically the opposite of the null hypothesis. Let’s say that the annual sales growth of a particular product in existence for 15 years is 25%. The null hypothesis in this example is that the mean growth rate is 25% for the product. The aim of a hypothesis analysis is to determine if the null hypothesis is not true. In this example, an analyst uses the alternative hypothesis to test whether the assumed 25% growth rate is accurate. Therefore, the alternative hypothesis is that the growth rate is not 25% for the product. In this example, the random sample can be the product’s growth rate over five years instead of 15 years. At the end of the test, a data analyst can draw a conclusion based on the results.
Qualitative data analysis techniques are built on two main qualitative data approaches: deductive and inductive.
- Deductive approach . This analysis method is used by researchers and analysts who already have a theory or a predetermined idea of the likely input from a sample population. The deductive approach aims to collect data that can methodically and accurately support a theory or hypothesis.
- Inductive approach . In this approach, a researcher or analyst with little insight into the outcome of a sample population collects the appropriate and proper amount of data about a topic of interest. Then, they investigate the data to look for patterns. The aim is to develop a theory to explain patterns found in the data.
Two main qualitative data analysis techniques used by data analysts are content analysis and discourse analysis. Another popular method is narrative analysis, which focuses on stories and experiences shared by a study’s participants. Below are descriptions and typical steps involved in content analysis and discourse analysis.
Content analysis
Researchers and data analysts can use content analysis to identify patterns in various forms of communication. Content analysis can reveal patterns in recorded communication that indicate the purpose, messages, and effect of the content.
Content analysis can also help determine the intent of the content producers and the impact on target audiences. For example, content analysis of political messages can provide qualitative insights about employment policy amid the COVID-19 pandemic. An analyst could identify instances where the word “employment” appears in social media, news stories, and other media and correlates with other relevant terms, such as “economy,” “business,” and “Main Street.” An analyst can then study the relationships between these keywords to better understand a political campaign’s intention with its messages.
The content analysis process contains several components, including the following:
Identify data sources
The first step in the content analysis process is to select the type of content to be analyzed. Sources can range from text found in written form from books, newspapers, and social media posts to visual form found in photographs and video.
Determine data criteria
This step involves determining what will make a particular text relevant to the study. Questions to assess data criteria can include: Does the text mention a specific topic or connote an event related to the issue? Does it fall within a specified date range or geographic location?
Develop coding for the data
Since qualitative data is not numerical, it needs to be codified in preparation for measurement. This requires developing a set or system of codes to categorize the data. Once the coding system is developed, relevant codes can be applied to specific texts.
Analyze the results
All the work in the previous steps leads to the data examination process. Data analysts look for patterns and correlations in the data to interpret results and make conclusions. They can incorporate statistical techniques for data analysis to draw insights from the data further.
Discourse analysis
A message is not always what it seems, so “reading between the lines,” or the ability to determine underlying messages in communication, is essential. When communications, whether verbal or written, have an indirect or underlying message, it can be interpreted one way by one group and in an entirely different way by another, potentially leading to a breakdown in civil discourse.
Discourse analysis helps provide an understanding of the social and cultural context of verbal and written communication throughout conversations. Discourse analysis aims to investigate the social context of communication and how people use language to achieve their aims, such as evoking an emotion, sowing doubt, or building trust. Discourse analysis analyzes verbal and nonverbal cues. For example, the way a speaker pauses on a particular word or phrase can reveal insights into the speaker’s intent or attitude toward that phrase.
Discourse analysis helps interpret the true meaning and intent of communication and clarifies misunderstandings. For example, an analysis of transcripts of conversations between a physician and a patient can reveal whether the patient truly understood a diagnosis.
An analyst can distinguish subtle subtext in communication through discourse analysis to differentiate whether the content is fact, fiction, or propaganda.
Steps in discourse analysis include:
Define the research question
Defining the research question determines the aim of the investigation and provides a clear purpose. The research question will guide the analysis.
Select the content types
Materials used for investigation can include social media text, speeches, messaging in marketing brochures, press releases, and more.
Collect the data
The content collected for the analysis typically focuses on a subject delivering the message (such as a political leader or company) and its targeted audience (citizens and customers, for example).
Analyze the content
Words, phrases, sentences, and content structure can reveal patterns in the subject’s attitudes and intent with their message and the audience’s response or reaction.
Businesses can use quantitative and qualitative data analysis techniques to improve decision-making and forecasting, enhance business performance and competitiveness, maximize sales and marketing effectiveness, streamline operational processes, create better customer experiences, drive business agility, lower costs and reduce waste, and raise quality standards.
Statistical techniques use mathematical approaches to provide insights, observations, and conclusions. The processes encompass testing hypotheses and making estimates and predictions of unknown data or quantities. Statistical techniques for data analysis can help decision-makers in various ways, such as determining the risk of different business scenarios or forecasting sales in changing market conditions.
Quantitative data is numerical, therefore, it can be analyzed using statistical analysis techniques to find patterns or meaning. Qualitative data can also be analyzed using statistical analysis techniques. But since qualitative data is typically nonnumerical, it must first be classified and grouped into meaningful categories.
Statistical techniques used in both qualitative and quantitative data analysis include grounded theory and cross-tabulation.
Grounded theory
This systematic inductive approach gathers, synthesizes, analyzes, and conceptualizes qualitative and quantitative data. Analysts using a grounded theory approach observe the data and identify patterns before developing a theory. This type of approach is typical in qualitative research.
Quantitative methods are typically structured the opposite way; first, a theory is developed and then the data is observed for patterns. Grounded theory research methods are useful when data about a particular topic is scarce. The grounded theory approach’s flexibility enables researchers to find patterns, trends, and relationships in both qualitative and quantitative data. Based on the findings, an investigator builds a theory founded or “grounded” in the data.
Cross-tabulation
This data analysis technique provides information about the relationship between different variables in a table format. It allows researchers to observe two or more variables simultaneously. The data is classified according to at least two categorical variables, represented as rows and columns. Therefore, each variable must be classified under at least two categories.
For example, cross-tabulation can be useful in marketing and for reviewing customer feedback. A column can provide values indicating whether a customer was satisfied or dissatisfied with their experience. A row can present variables identifying the type of customer (online or in store, for example). A statistical analysis of the data can reveal insights from tables populated with a lot of data. For example, the Chi-square is a statistical hypothesis technique that allows analysts to observe values and draw conclusions across more than one category, providing valuable business insight.
Businesses have a treasure trove of data within reach thanks to digital music, movies, television, and games, and the digitization of business processes. The data is generated every day by users of mobile phones and PCs, IoT-powered machines, and other devices.
Big data’s fast and evolving nature makes it difficult to manage and analyze with traditional data management software. Data analysis techniques play a key role in turning the research data into meaningful insights to help in business decision-making. The insights derived from the data can lead to revenue growth, improved marketing and operational performance, and stronger customer relationships, making data analysis a key skill for creating business value.
Infographic Sources
CFI, “Regression Analysis”
Grow, “Why Is Data Important for Your Business?”
Medium, “Hypothesis Analysis Explained”
Netrix, “10 Ways Data Analytics Can Revolutionize Your Business Strategy”
Pew Research Center, “Content Analysis”
ThoughtCo, “Understanding the Use of Language Through Discourse Analysis”
Towards Data Science, “Effective Ways How Data Analytics Help to Make a Better Entrepreneur”
Wipro, “How to Create Business Value from Data”
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Data Analysis: Definition, Types and Examples
Nowadays, data is collected at various stages of processes and transactions, which has the potential to significantly improve the way we work. However, to fully realize value of data analysis, this data must be analyzed in order to gain valuable insights into improving products and services.
Data analysis is a crucial aspect of making informed decisions in various industries. With the advancement of technology, it has become a dynamic and exciting field But w hat is it in simple words?
Content Index
- What is data analysis?
Types of Data Analysis
Data analysis advantages.
- Uses of data analysis
Techniques for Analysis
- Step-by-Step Guide of Data Analysis
Data Analysis with QuestionPro
What is data analysis.
Data analysis is the science of examining data to conclude the information to make decisions or expand knowledge on various subjects. It consists of subjecting data to operations. This process happens to obtain precise conclusions to help us achieve our goals, such as operations that cannot be previously defined since data collection may reveal specific difficulties.
“A lot of this [data analysis] will help humans work smarter and faster because we have data on everything that happens” . – Daniel Burrus, business consultant and speaker on business and innovation issues.
There are several types of data analysis, each with a specific purpose and method. Let’s talk about some significant types:
Descriptive Analysis
Descriptive analysis is used to summarize and describe the main features of a dataset. It involves calculating measures of central tendency and dispersion to describe the data. The descriptive analysis provides a comprehensive overview of the data and insights into its properties and structure.
Inferential Analysis
The inferential analysis is used statistical models and testing to make inferences about the population parameters, such as the mean or proportion. This analysis involves using models and hypothesis testing to make predictions and draw conclusions about the population.
Predictive Analysis
Predictive analysis is used to predict future events or outcomes based on historical data and other relevant information. It involves using statistical models and machine learning algorithms to identify patterns in the data and make predictions about future outcomes.
Prescriptive Analysis
The prescriptive analysis is a decision-making analysis that uses mathematical modeling, optimization algorithms, and other data-driven techniques to identify the action for a given problem or situation. It combines mathematical models, data, and business constraints to find the best move or decision.
Text Analysis
Text analysis is a process of extracting meaningful information from unstructured text data. It involves a variety of techniques, including natural language processing (NLP), text mining, sentiment analysis, and topic modeling, to uncover insights and patterns in the text data.
Currently, many industries use data to draw conclusions and decide on actions to implement. It is worth mentioning that science also uses to test or discard existing theories or models.
There’s more than one advantage to data analysis done right. Here are some examples:
- Make faster and more informed business decisions backed by facts.
- Identify performance issues that require action.
- Gain a deeper understanding of customer requirements, which creates better business relationships.
- Increase awareness of risks to implement preventive measures.
- Visualize different dimensions of the data.
- Gain competitive advantage.
- Better understand the financial performance of the business.
- Identify ways to reduce costs and thus increase profits.
These questions are examples of different types of data analysis. You can include them in your post-event surveys aimed at your customers:
- Questions start with: Why? How?
Example of qualitative data research analysis: Panels where a discussion is held, and consumers are interviewed about what they like or dislike about the place.
- Data is collected by asking questions like: How many? Who? How often? Where?
Example of quantitative research analysis: Surveys focused on measuring sales, trends, reports, or perceptions.
Uses of Data Analysis
It is used in many industries regardless of the branch. It gives us the basis to make decisions or confirm if a hypothesis is true.
- Marketing: Mainly, researchers perform data analysis to predict consumer behavior and help companies place their products and services in the market accordingly. For instance, sales data analysis can help you identify the product range not-so-popular in a specific demographic group. It can give you insights into tweaking your current marketing campaign to better connect with the target audience and address their needs.
- Human Resources: Organizations can use data analysis to offer a great experience to their employees and ensure an excellent work environment. They can also utilize the data to find out the best resources whose skill set matches the organizational goals.
- Academics: Universities and academic institutions can perform the analysis to measure student performance and gather insights on how certain behaviors can further improve education.
IT is essential to analyze raw data to understand it. We must resort to various techniques that depend on the type of information collected, so it is crucial to define the method before implementing it.
- Qualitative data: Researchers collect qualitative data from the underlying emotions, body language, and expressions. Its foundation is the data interpretation of verbal responses. The most common ways of obtaining this information are through open-ended interviews, focus groups, and observation groups, where researchers generally analyze patterns in observations throughout the data collection phase.
- Quantitative data: Quantitative data presents itself in numerical form. It focuses on tangible results.
Data analysis focuses on reaching a conclusion based solely on the researcher’s current knowledge. How you collect your data should relate to how you plan to analyze and use it. You also need to collect accurate and trustworthy information.
There are many data collection techniques, but experts’ most commonly used method is online surveys. It offers significant benefits such as reducing time and money compared to traditional data collection methods. The
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Step-by-Step Guide Data Analysis
With these five steps in your process, you will make better decisions for your business because data that has been well collected and analyzed support your choices.
Step 1: Define your questions
Start by selecting the right questions. Questions should be measurable, clear, and concise. Design your questions to qualify or disqualify possible solutions to your specific problem.
Step 2: Establish measurement priorities
This step divides into two sub-steps:
- Decide what to measure: Analyze what kind of data you need.
- Decide how to measure it: Thinking about how to measure your data is just as important, especially before the data collection phase, because your measurement process supports or discredits your thematic analysis later on.
Step 3: Collect data
With the question clearly defined and your measurement priorities established, now it’s time to collect your data. As you manage and organize your data, remember to keep these essential points in mind:
- Before collecting new data, determine what information you could gather from existing databases or sources.
- Determine a storage and file naming system to help all team members collaborate in advance. This process saves time and prevents team members from collecting the same information twice.
- If you need to collect data through surveys, observation, or interviews, develop a questionnaire in advance to ensure consistency and save time.
- Keep the collected data organized with a log of collection dates, and add any source notes as you go along.
Step 4: Analyze the data
Once you’ve collected the correct data to answer your Step 1 question, it’s time to conduct a deeper statistical analysis . Find relationships, identify trends, sort and filter your data according to variables. As you analyze the data, you will find the exact data you need.
Step 5: Interpret the results
After analyzing the data and possibly conducting further research, it is finally time to interpret the results. Ask yourself these key questions:
- Does the data answer your original question? How?
- Does the data help you defend any objections? How?
- Are there any limitations to the conclusions, any angles you haven’t considered?
If the interpretation of data holds up under these questions and considerations, you have reached a productive conclusion. The only remaining step is to use the results of the process to decide how you are going to act.
Join us as we look into the most frequently used question types, and how to effectively analyze your findings.
Make the right decisions by analyzing data the right way!
Data analysis is crucial in aiding organizations and individuals in making informed decisions by comprehensively understanding the data. If you’re in need of a data analysis solution, consider using QuestionPro. Our software allows you to collect data easily, create real-time reports, and analyze data.
Start a free trial or schedule a demo to see the full potential of our powerful tool. We’re here to help you every step of the way!
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Reading Craze Academic Research Knowledge for Students Teachers and Professional Researchers
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How to analyze data in research
ReadingCraze.com February 20, 2013 Data Analysis , Data Collection , Data Evaluation , Research Writing , Steps in Research Process Leave a comment 24,970 Views
Data analysis in research
Research analysis is one of the main steps of the research process, it is by far the most important steps of the research. How to analyze the data is an important question that every researcher asks. The researcher collects the data using one of the qualitative or quantitative methods of data collection. Data analysis highly depends on whether the data is a qualitative data or a quantitative data.
What is data analysis?
Data analysis is the process of scanning, examining and interpreting data available in tabulated form. The purpose of data analysis is to understand the nature of the data and reach a conclusion. Data analysis actually provides answers to the research questions or research problems that you have formulated. Without data analysis you cannot draw any conclusion. Data organization alone cannot help you in drawing conclusions but data analysis helps you in this regard. After analyzing data you get an organized and well examined form of data that can help you know whether your hypothesis got accepted or rejected.
How to analyze data?
There is not a single hard and fast rule for data analysis but you need to look at your data and decide on the method of data analysis. There are some basic tips you need to follow to analyze data in research papers and dissertations.
Data organization
Organize your data before scanning, examining or interpreting it. Data organization is necessary because you cannot analyze haphazard data. You can arrange and organize data in tables or groups. This is easier to do if your data is quantitative on the other hand qualitative data is difficult to tabulate. You can first arrange your data in groups or categories and under each category you can tabulate the data. For qualitative data you have to follow different methods of data organization. Well organized data lends itself easily to analysis.
Graphical representation
Now look at the tabulated data and make graphs to show the data in more clear form. Plotting graphs is necessary because it helps you in looking at the extreme points as well as the average points. You can use any one of the methods of graphs. You can use a statistical software to make graphs. Otherwise if you are good in statistics you can make the graphs yourself. Graphs will make the data more presentable and easy to comprehend.
Data explanation
In the next step explain the data that is present in both tabulated and graphical forms. This explanation will help you draw main conclusions. Explore the graphs and tables and find out how you can write down the interpretation of your research study. You can correlate the variables and you can also explain the results. Try to make the interpretation specific and to the point. Extremely lengthy explanations are unnecessary in most cases, on the other hand a specific interpretation of the data is easy to understand.
Statistical methods
In the last stage the hypothesis is rejected or accepted in the light of your interpretations. You have to confirm that your hypothesis proved right or it proved wrong. You can use any one of the statistical methods for confirmation of the hypothesis. Generally you can use ANOVA , t-test, z-test or chi square to test the hypothesis. There are also software that can help you in this regard. You can also get help of a statistician to apply statistical methods to your research. Statistical application is important because it makes your research valid and generalizable.
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Biomedical refrigerators and freezers market forcast 2023 to 2028 : in-depth research analysis with top countries data.
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Mar 03, 2023 (The Expresswire) -- Biomedical Refrigerators and Freezers Market " is expected to grow at a steady growth during the forecast period 2023-2028, Biomedical Refrigerators and Freezers Market report offers insights into the latest trends. It summarizes key aspects of the market, with focus on leading key player’s areas that have witnessed the highest demand, leading regions and applications. It offers qualitative as well as quantitative information regarding the factors, challenges, and opportunities that will define the growth of the market over 2023-2028, The Report Contain Many Pages With Detailed Analysis.
COVID-19 can affect the global economy in three main ways: by directly affecting production and demand, by creating supply chain and market disruption, and by its financial impact on firms and financial markets. Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.
Final Report will add the analysis of the impact of COVID-19 on this industry.
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This Biomedical Refrigerators and Freezers Market report includes the estimation of market size for value (million USD) and volume (K Units). Both top-down and bottom-up approaches have been used to estimate and validate the market size of Biomedical Refrigerators and Freezers market, to estimate the size of various other dependent submarkets in the overall market. Key players in the market have been identified through secondary research, and their market shares have been determined through primary and secondary research. All percentage shares, splits, and breakdowns have been determined using secondary sources and verified primary sources.
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The research covers the current Rectifiers market size of the market and its growth rates based on 6-year records with company outline of Key players/manufacturers:
Biomedical Refrigerators and Freezers Market Analysis and Insights:
The Global Biomedical Refrigerators and Freezers market is anticipated to rise at a considerable rate during the forecast period, between 2023 and 2028. In 2022, the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.
Biomedical refrigerators and freezers are used in hospitals, research laboratories, diagnostic centers, pharmacies, and blood banks etc. Unlike domestic refrigerators and freezers, biomedical refrigerators and freezers provide optimum conditions for storage of medical products.
The Biomedical Refrigerators and Freezers market revenue was Million USD in 2016, grew to Million USD in 2022, and will reach Million USD in 2028, with a CAGR of during 2022-2028.
Global Biomedical Refrigerators and Freezers Market Development Strategy Pre and Post COVID-19, by Corporate Strategy Analysis, Landscape, Type, Application, and Leading 20 Countries covers and analyzes the potential of the global Biomedical Refrigerators and Freezers industry, providing statistical information about market dynamics, growth factors, major challenges, PEST analysis and market entry strategy Analysis, opportunities and forecasts. The biggest highlight of the report is to provide companies in the industry with a strategic analysis of the impact of COVID-19. At the same time, this report analyzed the market of leading 20 countries and introduce the market potential of these countries.
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Report further studies the market development status and future Biomedical Refrigerators and Freezers Market trend across the world. Also, it splits Biomedical Refrigerators and Freezers market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.
On the basis of product type this report displays the production, revenue, price, market share and growth rate of each type, primarily split into:
On the basis of the end users/applications this report focuses on the status and outlook for major applications/end users, consumption (sales), market share and growth rate for each application, including:
Chapters 7-26 focus on the regional market. We have selected the most representative 20 countries from 197 countries in the world and conducted a detailed analysis and overview of the market development of these countries.
Some of the key questions answered in this report:
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Major Points from Table of Contents
Global Biomedical Refrigerators and Freezers Market Research Report 2023-2028, by Manufacturers, Regions, Types and Applications
1 Introduction 1.1 Objective of the Study 1.2 Definition of the Market 1.3 Market Scope 1.3.1 Market Segment by Type, Application and Marketing Channel 1.3.2 Major Regions Covered (North America, Europe, Asia Pacific, Mid East and Africa) 1.4 Years Considered for the Study (2015-2028) 1.5 Currency Considered (U.S. Dollar) 1.6 Stakeholders 2 Key Findings of the Study 3 Market Dynamics 3.1 Driving Factors for this Market 3.2 Factors Challenging the Market 3.3 Opportunities of the Global Biomedical Refrigerators and Freezers Market (Regions, Growing/Emerging Downstream Market Analysis) 3.4 Technological and Market Developments in the Biomedical Refrigerators and Freezers Market 3.5 Industry News by Region 3.6 Regulatory Scenario by Region/Country 3.7 Market Investment Scenario Strategic Recommendations Analysis
4 Value Chain of the Biomedical Refrigerators and Freezers Market
4.1 Value Chain Status 4.2 Upstream Raw Material Analysis 4.3 Midstream Major Company Analysis (by Manufacturing Base, by Product Type) 4.4 Distributors/Traders 4.5 Downstream Major Customer Analysis (by Region) Get a Sample Copy of the Biomedical Refrigerators and Freezers Market Report 2023
5 Global Biomedical Refrigerators and Freezers Market-Segmentation by Type 6 Global Biomedical Refrigerators and Freezers Market-Segmentation by Application 7 Global Biomedical Refrigerators and Freezers Market-Segmentation by Marketing Channel 7.1 Traditional Marketing Channel (Offline) 7.2 Online Channel 8 Competitive Intelligence Company Profiles
9 Global Biomedical Refrigerators and Freezers Market-Segmentation by Geography
9.1 North America 9.2 Europe 9.3 Asia-Pacific 9.4 Latin America
9.5 Middle East and Africa 10 Future Forecast of the Global Biomedical Refrigerators and Freezers Market from 2023-2028
10.1 Future Forecast of the Global Biomedical Refrigerators and Freezers Market from 2023-2028 Segment by Region 10.2 Global Biomedical Refrigerators and Freezers Production and Growth Rate Forecast by Type (2023-2028) 10.3 Global Biomedical Refrigerators and Freezers Consumption and Growth Rate Forecast by Application (2023-2028) 11 Appendix 11.1 Methodology 12.2 Research Data Source
Continued….
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Market is changing rapidly with the ongoing expansion of the industry. Advancement in the technology has provided today’s businesses with multifaceted advantages resulting in daily economic shifts. Thus, it is very important for a company to comprehend the patterns of the market movements in order to strategize better. An efficient strategy offers the companies with a head start in planning and an edge over the competitors. Precision Reports is the credible source for gaining the market reports that will provide you with the lead your business needs.
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What Is Data Analysis and Why Is It Important?
What is data analysis? We explain data mining, analytics, and data visualization in simple to understand terms.
The world is becoming more and more data-driven, with endless amounts of data available to work with. Big companies like Google and Microsoft use data to make decisions, but they're not the only ones.
Is it important? Absolutely!
Data analysis is used by small businesses, retail companies, in medicine, and even in the world of sports. It's a universal language and more important than ever before. It seems like an advanced concept but data analysis is really just a few ideas put into practice.
What Is Data Analysis?
Data analysis is the process of evaluating data using analytical or statistical tools to discover useful information. Some of these tools are programming languages like R or Python. Microsoft Excel is also popular in the world of data analytics .
Once data is collected and sorted using these tools, the results are interpreted to make decisions. The end results can be delivered as a summary, or as a visual like a chart or graph.
The process of presenting data in visual form is known as data visualization . Data visualization tools make the job easier. Programs like Tableau or Microsoft Power BI give you many visuals that can bring data to life.
There are several data analysis methods including data mining, text analytics, and business intelligence.
How Is Data Analysis Performed?
Data analysis is a big subject and can include some of these steps:
- Defining Objectives: Start by outlining some clearly defined objectives. To get the best results out of the data, the objectives should be crystal clear.
- Posing Questions: Figure out the questions you would like answered by the data. For example, do red sports cars get into accidents more often than others? Figure out which data analysis tools will get the best result for your question.
- Data Collection: Collect data that is useful to answer the questions. In this example, data might be collected from a variety of sources like DMV or police accident reports, insurance claims and hospitalization details.
- Data Scrubbing: Raw data may be collected in several different formats, with lots of junk values and clutter. The data is cleaned and converted so that data analysis tools can import it. It's not a glamorous step but it's very important.
- Data Analysis: Import this new clean data into the data analysis tools. These tools allow you to explore the data, find patterns, and answer what-if questions. This is the payoff, this is where you find results!
- Drawing Conclusions and Making Predictions: Draw conclusions from your data. These conclusions may be summarized in a report, visual, or both to get the right results.
Let's dig a little deeper into some concepts used in data analysis.
Data Mining
Data mining is a method of data analysis for discovering patterns in large data sets using statistics, artificial intelligence, and machine learning. The goal is to turn data into business decisions.
What can you do with data mining? You can process large amounts of data to identify outliers and exclude them from decision making. Businesses can learn customer purchasing habits, or use clustering to find previously unknown groups within the data.
If you use email, you see another example of data mining to sort your mailbox. Email apps like Outlook or Gmail use this to categorize your emails as "spam" or "not spam".
Text Analytics
Data is not just limited to numbers, information can come from text information as well.
Text analytics is the process of finding useful information from text. You do this by processing raw text, making it readable by data analysis tools, and finding results and patterns. This is also known as text mining.
Excel does a great job with this. Excel has many formulas to work with text that can save you time when you go to work with the data.
Text mining can also collect information from the web, a database or a file system. What can you do with this text information? You can import email addresses and phone numbers to find patterns. You can even find frequencies of words in a document.
Business Intelligence
Business intelligence transforms data into intelligence used to make business decisions. It may be used in an organization's strategic and tactical decision making. It offers a way for companies to examine trends from collected data and get insights from it.
Business intelligence is used to do a lot of things:
- Make decisions about product placement and pricing
- Identify new markets for product
- Create budgets and forecasts that make more money
- Use visual tools such as heat maps, pivot tables, and geographical mapping to find the demand for a certain product
Data Visualization
Data visualization is the visual representation of data. Instead of presenting data in tables or databases, you present it in charts and graphs. It makes complex data more understandable, not to mention easier to look at.
Increasing amounts of data are being generated by applications you use (Also known as the "Internet of Things"). The amount of data (referred to as "big data") is pretty massive. Data visualization can turn millions of data points into simple visuals that make it easy to understand.
There are various ways to visualize data:
- Using a data visualization tool like Tableau or Microsoft Power BI
- Standard Excel graphs and charts
- Interactive Excel graphs
- For the web, a tool like D3.js built using JavaScript
The visualization of Google datasets is a great example of how big data can visually guide decision-making.
Data Analysis in Review
Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining , text analytics, business intelligence, combining data sets , and data visualization.
The Power Query tool in Microsoft Excel is especially helpful for data analysis. If you want to familiarize yourself with it, read our guide to create your first Microsoft Power Query script .
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Research methods are specific procedures for collecting and analyzing data. Developing your research methods is an integral part of your research design. When planning your methods, there are two key decisions you will make. First, decide how you will collect data. Your methods depend on what type of data you need to answer your research question:
Step 1: Write your hypotheses and plan your research design Step 2: Collect data from a sample Step 3: Summarize your data with descriptive statistics Step 4: Test hypotheses or make estimates with inferential statistics Step 5: Interpret your results Step 1: Write your hypotheses and plan your research design
By manipulating the data using various data analysis techniques and tools, you can begin to find trends, correlations, outliers, and variations that tell a story. During this stage, you might use data mining to discover patterns within databases or data visualization software to help transform data into an easy-to-understand graphical format.
Definition of research in data analysis: According to LeCompte and Schensul, research data analysis is a process used by researchers for reducing data to a story and interpreting it to derive insights. The data analysis process helps in reducing a large chunk of data into smaller fragments, which makes sense.
Data analysis is the process of collecting, modeling, and analyzing data using various statistical and logical methods and techniques. Businesses rely on analytics processes and tools to extract insights that support strategic and operational decision-making.
nonexperimental and experimental research and the differences between descriptive and inferential analyses. Finally, it presents basic concepts in hypothesis testing. After completing this chapter, you should be familiar with the fundamental issues and terminology of data analysis, and be prepared to learn about using JMP for data analysis.
Data analysis also provides researchers with a vast selection of different tools, such as descriptive statistics, inferential analysis, and quantitative analysis. So, to sum it up, data analysis offers researchers better data and better ways to analyze and study said data. What is Data Analysis: Types of Data Analysis
Analyzing data effectively helps organizations make business decisions. Nowadays, data is collected by businesses constantly: through surveys, online tracking, online marketing analytics, collected subscription and registration data (think newsletters), social media monitoring, among other methods.
There are differences between qualitative data analysis and quantitative data analysis. In qualitative researches using interviews, focus groups, experiments etc. data analysis is going to involve identifying common patterns within the responses and critically analyzing them in order to achieve research aims and objectives.
The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the 'problem statement'. Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve?
While data analysis in qualitative research can include statistical procedures, many times analysis becomes an ongoing iterative process where data is continuously collected and analyzed almost simultaneously. Indeed, researchers generally analyze for patterns in observations through the entire data collection phase (Savenye, Robinson, 2004).
For more than two decades, ACC's NCDR registries have provided data-driven insights, analysis and research to inform clinical and operational decisions, allowing hospitals and health systems around the world to perform at the highest level and deliver optimal care to every patient, every time. The NCDR was born 25 years ago out of a quest to answer questions that were beginning to emerge at ...
Determining Research and Data Analysis Methods. Hailey McCausland. September 24, 2022. A. Restatement Topic: To improve reading comprehension of lower elementary students through the implementation of updated curriculum. Problem Statement: There is a problem with my 2nd grade students' reading comprehension skills.
Perform RNA-Seq, ChIP-Seq, and DNA methylation data analyses, using open source software, including R and Bioconductor. Free*. 5 weeks long. Available now.
1. Use an electronic database to organize the data. Copy the data into a new file for editing. You never want to work on the master data file in case something gets corrupted during the analysis process. A program such as Excel allows you to organize all of your data into an easily searchable spreadsheet.
First, the researcher began analyzing the data by identifying the novel's text. Second, the researcher categorizes the data based on the relationship between the bourgeoisie and the proletariat contained in the novel. Following that, the data is connected to Karl Marx's theory. The final is part to make conclusions from the analysis.
Data Analysis can be explained as the process of discovering useful information by evaluating data whereas quantitative data analysis can be defined as the process of analyzing data that is number-based or data that can easily be converted into numbers.
Qualitative data analysis is the process of organizing, analyzing, and interpreting qualitative data—non-numeric, conceptual information and user feedback—to capture themes and patterns, answer research questions, and identify actions to take to improve your product or website.
Small Summaries for Big Data. Graham Cormode - AT&T Research July 2nd, 2012, 16:00-17:00, Microsoft Research Cambridge, Jasmine Room. Abstract: In dealing with big data, we often need to look at a small summary to get the big picture. Over recent years, many new techniques have been developed which allow important properties of large distributions to be extracted from compact and easy-to ...
Data analysis is a technique that typically involves multiple activities such as gathering, cleaning, and organizing the data. These processes, which usually include data analysis software, are necessary to prepare the data for business purposes.
Qualitative research analysis focuses on opinions, attitudes, and beliefs. Questions start with: Why? How? Example of qualitative data research analysis: Panels where a discussion is held, and consumers are interviewed about what they like or dislike about the place. Quantitative research analysis focuses on complex data and information that ...
Data analysis is the process of scanning, examining and interpreting data available in tabulated form. The purpose of data analysis is to understand the nature of the data and reach a conclusion. Data analysis actually provides answers to the research questions or research problems that you have formulated. Without data analysis you cannot draw ...
Get the latest and most comprehensive real estate statistics, forecasts, analysis, and commentary. Realtor.com economic research provides proprietary insights into real estate market trends.
4.1 Value Chain Status 4.2 Upstream Raw Material Analysis 4.3 Midstream Major Company Analysis (by Manufacturing Base, by Product Type) 4.4 Distributors/Traders 4.5 Downstream Major Customer ...
For many researchers unfamiliar with qualitative research, determining how to conduct qualitative analyses is often quite challenging. Part of this challenge is due to the seemingly limitless approaches that a qualitative researcher might leverage, as well as simply learning to think like a qualitative researcher when analyzing data. From framework analysis (Ritchie & Spencer, 1994) to content ...
Data analysis is used to evaluate data with statistical tools to discover useful information. A variety of methods are used including data mining, text analytics, business intelligence, combining data sets, and data visualization. The Power Query tool in Microsoft Excel is especially helpful for data analysis.