SPSS Data Analysis Explained: From Data Entry to Interpretation

SPSS data analysis spans the full journey from blank spreadsheet to interpreted findings ready for your dissertation results chapter. Students often jump straight to t-tests without properly entering data, defining variables, or running descriptives—then wonder why output looks wrong or examiners question their analysis. This guide explains every stage from data entry through interpretation, with the detail you need to analyse survey and experimental data confidently in SPSS.

Data entry in SPSS: getting it right

Each row is one participant (case); each column is one variable. Enter numeric codes for categories—1 = Male, 2 = Female—not text labels in Data View. Define labels in Variable View → Values. Never leave blank cells as zero unless zero is meaningful. Use consistent decimal places for continuous variables.

Variable View setup checklist

• Name: short identifier (e.g., age, sat_total).
• Type: Numeric for most research variables.
• Width and Decimals: appropriate for your data.
• Label: full description ('Total Satisfaction Score').
• Values: category codes with labels.
• Missing: define user-missing values (e.g., 99 = refused).
• Measure: Nominal, Ordinal, or Scale.
• Columns and Align: formatting preferences.

Importing data from external sources

Excel import: File → Open → select .xlsx. First row should contain variable names. CSV import: specify delimiter and header row. Google Forms: download CSV, import to SPSS. SurveyMonkey and Qualtrics: export to SPSS format directly when available. Always verify row count matches expected sample size after import.

Descriptive analysis: know your data first

Before hypothesis testing, characterise your sample. Frequencies show category counts and percentages. Descriptives show mean, median, SD, range for continuous variables. Explore adds skewness, kurtosis, and normality tests. Present summary tables in your thesis before inferential results.

Recoding and computing new variables

Transform → Recode into Same or Different Variables for collapsing categories or reverse scoring. Transform → Compute Variable for scale totals, averages, and difference scores. Example: COMPUTE sat_total = mean.3(sat1, sat2, sat3, sat4, sat5) computes mean of 5 items, requiring minimum 3 valid responses.

Running inferential tests

Select Analyze menu procedure matching your hypothesis. Move variables to correct roles: Dependent List, Grouping Variable, Predictors, etc. Click Options or Statistics sub-dialogs for effect sizes and confidence intervals where available. Click Paste to save syntax before OK.

Reading SPSS output: essential tables

• Group Statistics: n, mean, SD per group before t-test.
• Independent Samples Test: Levene's + t-statistic, df, p (use equal/unequal variance row).
• ANOVA table: Between Groups, Within Groups, Total SS, df, F, p.
• Correlations matrix: Pearson r and p for each pair.
• Model Summary: R, R², adjusted R² for regression.
• Coefficients: B, β, t, p, VIF for each predictor.
• Chi-Square Tests: Pearson Chi-Square, df, p.

Interpreting results for your research question

Ask: does this output answer my hypothesis? Compare p to alpha. Examine direction (positive or negative correlation; which group has higher mean). Calculate effect size. Consider practical significance—not just statistical. Relate finding back to literature in discussion chapter.

Exporting and documenting analysis

Copy tables from Output Viewer to Word as editable objects. Include SPSS output in appendix if required. Save .spv output file with dated filename. Save .sps syntax file documenting every procedure. This documentation supports examiner questions about your analytical choices.

SPSS analysis from entry to interpretation—done for you

ReportLift handles complete SPSS analysis pipelines for dissertations: data setup, testing, output interpretation, and APA results writing.