Methodology | Quantitative vs Qualitative | Sampling | Data Collection | Data Analysis
Data analysis can feel like a maze of numbers, charts, and confusing terms, but it doesn’t have to be that way. Whether you are interpreting survey results or unraveling the deeper meaning behind interview responses, understanding how to analyze data is the key to turning raw information into clear insights. In this blog, we’ll break down the essentials of quantitative and qualitative data analysis – what they are, how they work, and when to use them. No overwhelming jargon – just straightforward explanations to help you analyze your research data.
1. Quantitative analysis methods
Quantitative analysis is used when data is numbers or can be converted to numbers. You will use quantitative analysis methods when measuring the difference or assessing relationships between groups. Quantitative methods are also used when you are testing a hypothesis. There are two types of quantitative analysis: descriptive statistics and inferential statistics.
1.1. Descriptive statistics
Descriptive statistical analysis will describe your sample by calculating the mean, median, mode, standard deviation, skewness, and frequency of numerical ratings or responses. Let’s look at an example.
✔ Consider this research study where the sample is customers of an online shopping platform. The research question is “How satisfied are the customers with our service?
An online shopping platform conducts a survey asking 500 customers to rate their satisfaction with the service on a scale of 1 to 10 (1 being “very dissatisfied” and 10 being “very satisfied”). The goal is to summarize the satisfaction levels using descriptive statistics.
|
Descriptive Statistic |
Description and Interpretation |
|---|---|
|
🧮 Mean (Average) |
The sum of all ratings is equal to 3800. The mean is the sum of the ratings/total number of responses (3800/500 = 7.6). A mean satisfaction score suggests that, on average, customers are relatively satisfied with the service. |
|
📏 Median (Middle Value) |
The median is the middle score if the individual ratings are arranged in ascending order. In our example study, if half of the ratings were above 8 and half were below 8, the median value would be 8. This indicates that most customers rated their satisfaction at or above this level, further supporting positive satisfaction. |
|
📊 Mode (Most Frequent Value) |
The mode is the most frequent rating. In our study, if the most frequent rating given were 8, then 8 would be the mode. A mode of 8 shows that most customers gave this rating, reinforcing a perception of high satisfaction. |
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↔️ Standard Deviation (Spread of Scores) |
The standard deviation measures how much the ratings deviate from the mean. If the standard deviation is 1.2, most ratings are close to the average satisfaction of 7.6. |
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📐 Skewness (Symmetry of Distribution) |
Skewness suggests whether the responses are evenly distributed, positively skewed, or negatively skewed. In our study, a skewness of -.05 (slightly negative) shows that more customers rated their satisfaction toward the higher end of the scale, with fewer dissatisfied customers. |
|
📅 Frequency Distribution |
Frequency distribution reveals how often each rating occurred. If most ratings were 8 and 10, the pattern highlights a general trend toward strong customer satisfaction. |
Descriptive Statistics Results Summary
Positive Satisfaction Levels: The mean (7.6), median (8), and mode (8) collectively suggest customers are generally satisfied.
Consistency in Responses: The low standard deviation (1.2) suggests minimal variation, meaning most customers had similar experiences.
Few Dissatisfied Customers: The slightly negative skewness and frequency data reveal that dissatisfaction is rare, with most responses clustering at the higher end of the scale.
This analysis gives the company a clear picture of overall satisfaction while highlighting opportunities to improve the situation of a minority of dissatisfied customers.
1.2. Inferential statistics
Inferential statistics allow you to make predictions about your population. Two types of predictions can be made:
✔ Example of a study predicting the differences between groups
❓ Question: Determine if freshmen college students who attend an online study group (Group A) spend more time studying per week than students who do not attend the group (Group B).
🔍 Hypothesis: Freshmen college students at XYZ University who attend an online study group each week spend more time studying than freshmen college students who do not attend an online study group.
🎲 Sample: A good sample must represent the population. If there are 60% female freshmen students and 40% male freshmen students, your sample should match those statistics as closely as possible.
✔ Example of a study predicting relationship between variables
❓ Question: Determine the relationship between number of hours women spend at the gym and body weight.
🔍 Hypothesis: Women in the U.S. between 25 and 45 who spend more hours at the gym have lower body weight.
🎲 Sample: Only women within the specified age range and who live in the U.S. provide a good sample.
Tip: Your predictions can only be correct with a representative sample of your population.
But wait! There is still more to consider about the prediction. When we make a prediction and test a hypothesis, the hypothesis is stated as the null and alternative hypothesis.
Even when there is a relationship between the two variables, we must know if that relationship is statistically significant to support the alternative hypothesis and reject the null hypothesis. To determine this, we use the p-value (probability value). It represents the probability of observing the sample data if the null hypothesis is true. The p-value tells the researcher if their results are statistically significant. When you run specific tests, the p-value is obtained from your statistical testing tool (e.g., IBM’s SPSS).
✔ Null Hypothesis (H0)
There is no relationship between number of hours women between 25 and 45 living in the U.S. spend at the gym and their body weight.
✔ Alternative Hypothesis (H1)
There is a relationship between number of hours women between 25 and 45 living in the U.S. spend at the gym and their body weight.
Even when there is a relationship between the two variables, we must know if that relationship is statistically significant to support the alternative hypothesis and reject the null hypothesis. To determine this, we use the p-value (probability value). It represents the probability of observing the sample data if the null hypothesis is true. The p-value tells the researcher if their results are statistically significant. When you run specific tests, the p-value is obtained from your statistical testing tool (e.g., IBM’s SPSS).
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Importance of the P-value |
|---|
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1: The p-value is compared to a predetermined significance level (typically set at .05). |
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2: If p≤.05, the null hypothesis is rejected, and the observed results are statistically significant. |
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3: If p≥.05, there is insufficient evidence to reject the null hypothesis. |
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4: Smaller p-values (e.g., .001) suggest more substantial evidence against the null hypothesis |
Tip: In inferential statistics, generalizations and predictions about populations are often based on hypothesis tests where the p-value determines whether patterns in sample data are likely to hold for the larger population.
Several statistical methods will be available to you when using inferential statistics. Let’s look at several. Some statistical methods will only work with certain types of data. Some require normally distributed data (following the bell curve), while others are designed for non-normally distributed data (skewed or with outliers or ordinal data). For non-normally distributed and ordinal data, non-parametric tests will be used.
|
Type of Data |
Definition |
Example |
|---|---|---|
|
📂 Nominal Data |
Data that represents categories with no inherent ranking or order. |
Gender, Movie Genre, Blood Type |
|
📊 Ordinal Data |
Data that represents categories with a meaningful order or ranking but without consistent intervals between values. |
Customer Satisfaction Ratings (very satisfied, satisfied, neutral, dissatisfied, very dissatisfied). |
|
🧩 Categorical Data (Nominal or Ordinal) |
Data that falls into distinct groups or categories (either ordered or unordered). |
Nominal: Types of vegetables (peas, corn, lettuce) Ordinal: Education level (high school, bachelors, masters, doctorate) |
|
🌡️ Interval Data |
Quantitative data with equal intervals between values but no true zero point. |
Fahrenheit temperatures. They can be compared, but ratios are meaningless. (90 degrees compared to 110 degrees) |
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📐 Ratio Data |
Quantitative data with equal intervals and a true zero point, allowing for meaningful comparison of rations. |
Weight, income, height – $5000 is twice as much as $2500. |
|
Statistical Method/Test |
When to Use? |
Examples |
|---|---|---|
|
T-test (Parametric) |
Measure the difference between two groups. |
The difference in mean blood pressure between two groups of people taking two different types of medication. |
|
ANOVA (Analysis of Variance) (Parametric) |
Measure the difference between multiple groups. |
The difference in mean blood pressure between five groups each taking a different type of blood pressure medication. |
|
Chi-square test 🔢 |
Analyze relationships in categorical data and determine if differences are because of chance or a meaningful relationship. |
Determine if there is an association between gender and preference for a specific movie genre. |
|
Pearson Correlation (Parametric) |
Measure the relationship between variables. Do the variables move together? Correlation does not show cause and effect. |
The relationship between cold temperatures (variable A) and number of hours people spend indoors (variable B). |
|
Regression Analysis 📈 |
Measure the relationship between variables PLUS understand the cause and effect between variables. |
Determine whether the number of hours a student studied can predict their exam score, and if so, for each additional hour of study, how much would the exam score increase? |
1.3. How to choose the right analysis method?
Here is a table that can help you choose the correct method:
|
Aspect |
Parametric Tests |
Non-Parametric Tests |
|---|---|---|
|
⚖️ Assumptions |
Requires normal distribution |
No specific distribution needed |
|
🔢 Data Type |
Interval or Ratio Scale |
Ordinal, Nominal, or Skewed |
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💡 Nature of Hypothesis |
Assumes means or relationships between variables |
Assumes rankings or medians |
|
📊 Statistical Method |
T-test, ANOVA, Pearson Correlation |
Mann-Whitney, Kruskal-Wallis, Spearman’s Rank Correlation |
|
⚡ Statistical Power |
More powerful when assumptions are met |
Less statistically powerful, but more robust |
Tip: Always consider your research question. What is it you want from your data?
If your research question involves numeric data (interval or ratio) (e.g., “What is the effect of a particular drug on weight gain?”), parametric tests are best because they use actual values and have greater statistical power.
For research questions involving categories or rankings (e.g., “Does customer satisfaction differ by gender?”), non-parametric tests are necessary because these data types do not meet parametric assumptions.
2. Qualitative analysis methods
Qualitative analysis methods are research techniques used to explore and understand complex phenomena through non-numerical data, such as words, images, or behaviors gathered through interviews, documents, observation, or focus groups. Qualitative analysis emphasizes interpretation over measurement and focuses on frequency of common content to reveal common themes in the non-numerical data. Let’s examine some qualitative analysis methods.
|
Qualitative Method |
Steps |
Research Example |
|---|---|---|
|
🧩 Thematic Analysis |
– Familiarize yourself with data (read transcripts). |
Research Question: What challenges do remote workers experience? Perform interviews with a small sample of remote workers. |
|
📂 Content Analysis (documents) |
– Gather relevant documents. |
Research Question: How is “cybersecurity risk” discussed in public communication in newspapers? Gather newspaper articles in which cybersecurity risk is discussed within the last year. |
|
📜 Narrative Analysis (personal stories) |
– Gather diaries and written letters. |
Research Question: What do the written stories from women’s diaries and letters during the Civil War reveal about the personal and social context of the time. Gather publicly available personal diaries and letters from women during the Civil War. |
Tip: Keep this in mind to enhance qualitative data analysis:
- Understand your research goals.
- Immerse yourself in the data to understand the context and nuances.
- Use a systematic approach (like thematic, content, or narrative).
- Reflect on your assumptions and biases.
- Focus on the themes that answer your research question.
- Interpret your findings and tie findings back to existing literature.
3. Summary
Understanding data analysis doesn’t have to be overwhelming. This blog simplifies the two primary methods of data analysis—quantitative and qualitative – exploring their core techniques when to use them, and how they work. Whether it’s using descriptive statistics to summarize numerical trends or employing thematic analysis to uncover patterns in interview responses, this guide provides straightforward explanations and examples. From calculating means and medians to interpreting narrative insights, readers will gain the confidence to analyze and understand their research data effectively. Perfect for anyone looking to demystify data analysis and turn raw information into actionable insights.
