What Statistical Test Should I Use for My Psychology Research?

Choosing the right statistical test for your psychology research can feel overwhelming, but with the right approach, you can make this process much simpler. Understanding your research design, data type, and research question are all crucial to selecting the correct test. This guide will walk you through the process of choosing the appropriate test, step by step, with a focus on clarity and practical examples. By the end, you'll be confident in matching your research question to the correct statistical test.

Table of Contents

Step 1: Start with Your Research Question

Before diving into the data or statistical methods, take a moment to reflect on your research question. What exactly are you trying to figure out?

Here are some examples to help clarify:

1. Are you comparing two or more groups, like people who received different types of therapy?

2. Are you trying to see if there’s a relationship between two things, like stress levels and sleep quality?

3. Are you predicting an outcome, such as whether age and income can predict overall life satisfaction?

Once you know what you’re trying to do—compare groups, test relationships, or make predictions—you’re already halfway to picking the right test!

Step 2: Understand the Type of Data You Have

In psychology, data isn’t just “numbers.” It comes in different types, and the type you have determines which statistical tests you can use. These types of data fall into four categories: nominal, ordinal, interval, and ratio. Let’s explore these in plain terms.

Nominal Data

Nominal data is all about categories or labels. There’s no inherent order; one category isn’t “better” or “greater” than another.

• Examples: Gender (male, female, non-binary), type of therapy (CBT, psychodynamic), or whether someone has a diagnosis (yes/no).

• What You Can Do With It: Since there’s no numerical or ranking element, you’ll need tests that analyze frequencies or proportions, like a Chi-square test.

Ordinal Data

Ordinal data adds order to the mix. You can rank the data, but you can’t measure the exact distance between ranks.

• Examples: Likert scale responses (strongly agree, agree, neutral), pain levels (none, mild, moderate, severe), or socioeconomic status (low, middle, high).

• What You Can Do With It: Because the intervals between ranks aren’t consistent, tests like the Mann-Whitney U test or Spearman’s rank correlation work well for ordinal data.

Interval Data

Interval data takes things up a notch. Here, the distances between values are equal, but there’s no “true zero.”

• Examples: IQ scores, temperature in Celsius.

• What You Can Do With It: Interval data allows for more powerful tests, like t-tests or ANOVA, as long as the data meets certain assumptions like normality.

Ratio Data

Finally, ratio data has it all: equal intervals and a true zero, meaning zero means “none of it exists.”

• Examples: Reaction time, age, income, or weight.

• What You Can Do With It: Like interval data, ratio data supports parametric tests like t-tests, ANOVA, and regression analysis.

Step 3: Match Your Question to the Right Test

Now that you know your research question and the type of data you’re working with, let’s match them to some common statistical tests.

If You’re Comparing Groups

If your research involves comparing two or more groups, the tests you’ll use depend on the number of groups and the type of data:

• For comparing two groups:

  • Use an independent samples t-test if your data is interval or ratio.

  • Use a Mann-Whitney U test for ordinal data.

• For comparing more than two groups:

  • Use ANOVA for interval or ratio data.

  • Use a Kruskal-Wallis test for ordinal data.

• For comparing frequencies in categories:

  • Use a Chi-square test, which is perfect for nominal data like survey responses.

If You’re Looking at Relationships

When you want to see if two variables are related, the test depends on how the variables are measured:

• For two continuous variables (interval or ratio), use Pearson’s correlation.

• If one or both variables are ordinal, try Spearman’s rank correlation.

• If you’re interested in prediction, use regression analysis.

If You’re Testing Frequencies

If you’re working with counts or proportions, like the number of people in different categories:

• Use a Chi-square test for independence to see if two categorical variables are related.

• Use a goodness-of-fit test to see if your data matches an expected distribution.

3. Analysing Frequencies

When your data consists of counts or frequencies (e.g., how many participants fall into certain categories), you can use these tests:

• Comparing Observed Frequencies to Expected Frequencies:

  • The Chi-Square Goodness of Fit Test is used when you are comparing observed frequencies to an expected distribution. For example, you might use this test to determine if personality types are evenly distributed in your sample.

• Comparing Frequencies Between Groups:

  • The Chi-Square Test of Independence is used when you want to compare the frequencies of categories across two or more groups (e.g., gender differences in therapy preference).

Step 4: Check Assumptions Before Running a Test

Statistical tests come with rules about the data they’re designed for. These rules, called assumptions, are important to check because violating them can make your results unreliable. Here are the most common ones:

1. Normality: Many tests assume the data follows a normal distribution (a bell-shaped curve). If your data isn’t normal, you can use a non-parametric test like the Mann-Whitney U test instead of a t-test.

2. Equal Variances: For tests like ANOVA, the variance in each group should be about the same. If this isn’t true, you might need to adjust your analysis.

3. Sample Size: Some tests work better with larger samples. If your sample is small, stick to non-parametric tests, which are less affected by sample size.

Step 5: Interpret and Report Your Results

Once you’ve run your statistical test, you’ll get numbers like a p-value or a test statistic. But what do you do with them? Here’s a roadmap for interpretation:

1. State Your Test and Findings: Start by saying what test you used and what you found. For example:

   • “An independent samples t-test showed that stress levels were significantly higher for students during finals week compared to mid-semester (t(48) = 3.12, p = .002).”

2. Include Effect Size: Report the effect size to show how big the difference or relationship is. For instance, include Cohen’s d for t-tests or η² for ANOVA.

3. Explain the Meaning: Go beyond the numbers to interpret your findings. What do they mean in the context of your research question?

Simply Put

By following these steps and understanding the types of data you are working with, you can confidently choose the appropriate statistical test for your psychology research. Always remember to ensure that your data meets the assumptions of the test and use non-parametric alternatives when necessary. With practice, selecting the right test will become a straightforward part of your research process.