Running, Interpreting, and Reporting Descriptive Statistics using SPSS
Based on Research With Fawad's video on YouTube. If you like this content, support the original creators by watching, liking and subscribing to their content.
Use **Analyze → Descriptive Statistics → Frequencies** for categorical variables like gender, and report counts plus appropriate distribution summaries.
Briefing
Descriptive statistics in SPSS hinge on matching the analysis tool to the type of variable—categorical data gets Frequencies, while continuous data gets Descriptives. In the session, the workflow starts under **Analyze → Descriptive Statistics**, where **Frequencies** is used to summarize discrete categories like gender. After selecting the variable (e.g., Gender), the analysis can report counts and distribution measures such as mode, minimum, and maximum. The output also distinguishes between **percentage** and **valid percentage**: valid percentage ignores missing cases, while percentage reflects them. That difference matters when reporting results, because a table can show 100% valid percentage even when some responses are missing—an example is given where removing cases changes percentage but leaves valid percentage at 100%.
Reporting the results follows a consistent AP-style approach: copy the SPSS table, add a table number and italicized caption/title, and then present the descriptive statistics for each variable before moving on to hypothesis testing or model evaluation. For categorical variables, the session demonstrates how to report both **n** and percentages (e.g., male vs. female counts and their percentages) and how to state the total sample size.
For continuous variables, the process shifts to **Analyze → Descriptive Statistics → Descriptives**. Here, the key outputs are **mean**, **standard deviation**, and related dispersion statistics such as minimum, maximum, variance, range, skewness, and kurtosis. The session illustrates how to translate SPSS output into thesis or paper language—for instance, reporting the average age of students along with standard deviation.
A more complex reporting need arises when a study uses a multi-item construct measured by indicators. For a construct like **CSR (corporate social responsibility)** measured with eight items, the session shows how to summarize item-level descriptives and also compute an **overall composite mean score**. In SPSS, that composite is created via **Transform → Compute Variable**, using the mean of CSR1 through CSR8. Once computed, the overall mean and standard deviation provide a single interpretive summary of respondents’ perceptions, which the session links to the response scale (e.g., a mean near 4 suggests agreement on a strongly disagree to strongly agree scale). It also emphasizes that item interpretation should be grounded in the questionnaire wording—such as explaining what CSR1 represents by checking the instrument.
Finally, the session covers descriptive comparisons across groups and cross-tabulation. To report CSR scores by country (Pakistan, China, Italy), it uses **Analyze → Compare Means → Means**, treating **country** as the factor and requesting mean, standard deviation, and sample size. For categorical relationships, it uses **Analyze → Descriptive Statistics → Crosstabs**, placing **country** in rows and **gender** in columns to produce counts by cell and a bar chart. The session closes by tying these outputs to where they belong in a write-up: demographic profiles at the start of a paper, or descriptive statistics right before evaluating measurement and structural models in thesis work.
Cornell Notes
SPSS descriptive statistics depend on variable type: use **Frequencies** for categorical variables (e.g., gender) and **Descriptives** for continuous variables (e.g., age). Frequencies output counts plus measures like mode, minimum, and maximum, and it distinguishes **percentage** from **valid percentage**—valid percentage excludes missing data. For multi-item constructs such as **CSR** measured by CSR1–CSR8, the session recommends reporting item-level descriptives and creating a composite score using **Transform → Compute Variable** to compute the mean across items. To compare descriptive results across groups like countries, use **Compare Means → Means** and report mean and standard deviation (often with sample size). For relationships between categorical variables, use **Crosstabs** with rows and columns to produce cell counts and charts.
When should SPSS use Frequencies versus Descriptives?
What’s the practical difference between percentage and valid percentage in SPSS output?
How should a multi-item construct like CSR be reported descriptively?
How do you create and interpret an overall mean score for CSR in SPSS?
How can descriptive statistics be compared across countries for a continuous score like CSR?
How does Crosstabs differ from Descriptives, and what does it produce?
Review Questions
- If a categorical variable has missing responses, how would you decide whether to report percentage or valid percentage?
- What SPSS steps would you use to compute an overall CSR composite score from CSR1–CSR8, and what should guide the interpretation of its mean?
- How would you report descriptive statistics for age versus gender in an AP-style thesis or paper?
Key Points
- 1
Use **Analyze → Descriptive Statistics → Frequencies** for categorical variables like gender, and report counts plus appropriate distribution summaries.
- 2
Report **valid percentage** when you want results that ignore missing data; use **percentage** when you want missing cases reflected in the denominator.
- 3
Use **Analyze → Descriptive Statistics → Descriptives** for continuous variables and report mean and standard deviation (plus min/max if relevant).
- 4
For multi-item constructs (e.g., CSR measured by CSR1–CSR8), report both item-level descriptives and a composite mean created via **Transform → Compute Variable**.
- 5
Interpret composite means using the questionnaire scale direction (e.g., a mean near the upper end suggests agreement).
- 6
Compare descriptive statistics across groups like countries using **Analyze → Compare Means → Means**, reporting mean and standard deviation (often with sample size).
- 7
Use **Analyze → Descriptive Statistics → Crosstabs** to show relationships between categorical variables via row/column contingency tables and charts.