What our SPSS help covers ¶
Press Paste instead of OK in any dialog box. SPSS writes the syntax for you, and your analysis becomes reproducible.
SPSS help at Copperplate is one-to-one support for using IBM SPSS Statistics to analyze your own data. We help you prepare and clean the dataset, choose and run the right procedures, check assumptions, read the output and report results in APA style. You can work with us as a consultant on a live project or as an SPSS tutor while you learn.
Both routes keep you in control. You run or approve each step, and we explain what each dialog box option does. For broader questions about study design and test choice, see our statistics help and consulting page. If you are taking a course and want to learn SPSS alongside the statistics, our statistics tutoring combines both.
SPSS data analysis procedures we support ¶
These are the SPSS tools for data analysis that thesis and journal projects use most. Menu paths are from recent versions; in SPSS 29 and later, Compare Means appears as Compare Means and Proportions.
| Analysis | SPSS menu path |
|---|---|
| Descriptives (mean, SD, range) | Analyze > Descriptive Statistics > Descriptives |
| Frequencies and normality checks | Analyze > Descriptive Statistics > Frequencies, or Explore |
| Chi-square test of independence | Analyze > Descriptive Statistics > Crosstabs > Statistics > Chi-square |
| Independent and paired t tests | Analyze > Compare Means > Independent-Samples T Test / Paired-Samples T Test |
| One-way ANOVA | Analyze > Compare Means > One-Way ANOVA |
| Factorial ANOVA and ANCOVA | Analyze > General Linear Model > Univariate |
| Repeated-measures ANOVA | Analyze > General Linear Model > Repeated Measures |
| Pearson and Spearman correlation | Analyze > Correlate > Bivariate |
| Linear regression | Analyze > Regression > Linear |
| Logistic regression | Analyze > Regression > Binary Logistic |
| Exploratory factor analysis | Analyze > Dimension Reduction > Factor |
| Reliability (Cronbach's alpha) | Analyze > Scale > Reliability Analysis |
Not sure whether you need a one-way or factorial design? Our guide to one-way vs two-way ANOVA explains the difference.
SPSS Help
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Data cleaning and preparation in SPSS ¶
Most SPSS errors begin in the data file. Before any test, we work through the dataset with you:
- Variable View: correct Measure levels (Scale, Ordinal, Nominal), value labels and user-defined missing values such as 999.
- Range checks: Frequencies and Descriptives to find impossible values and data-entry errors.
- Duplicates: Data > Identify Duplicate Cases.
- Reverse-scored items: Transform > Recode into Different Variables, so the original items stay intact.
- Scale scores: Transform > Compute Variable, using MEAN or SUM with a rule for missing items.
- Subgroups: Data > Select Cases and Data > Split File, with a check that the filter is switched off afterward.
Reverse-score negatively worded items before you run Reliability Analysis. A negative item-total correlation is the usual sign that one was missed.
SPSS regression, factor analysis and reliability ¶
Linear regression in SPSS
In Analyze > Regression > Linear we set up blocks for hierarchical models, request confidence intervals, R squared change and collinearity diagnostics under Statistics, plot ZRESID against ZPRED under Plots, and save Cook's distance to screen influential cases. We then explain the Model Summary, ANOVA and Coefficients tables, including the difference between unstandardized B and standardized Beta. Our guide to the regression equation shows how B values become a prediction equation.
Factor analysis in SPSS
In Analyze > Dimension Reduction > Factor we check the KMO measure and Bartlett's test, choose an extraction method (principal axis factoring for latent constructs, principal components for data reduction), select an oblique rotation such as Promax or Direct Oblimin when factors are likely correlated, and read the pattern matrix with you.
Reliability analysis
Under Analyze > Scale > Reliability Analysis we compute Cronbach's alpha for each subscale and review Item-Total Statistics, including Cronbach's Alpha if Item Deleted, before you decide whether any item should be dropped.
Reading SPSS output and reporting results ¶
SPSS output tables contain far more than you need to report. We show you which numbers matter and how they translate into APA 7 style:
- The Sig. column is the p value. SPSS displays .000 when p is very small; report it as p < .001.
- In the Independent Samples Test table, Levene's test decides whether you read the Equal variances assumed row or the Equal variances not assumed (Welch) row.
- Since version 27, SPSS can report Cohen's d for t tests and eta squared for one-way ANOVA. In General Linear Model, tick Estimates of effect size for partial eta squared.
- Italicize statistical symbols, give degrees of freedom in parentheses and add confidence intervals in square brackets.
APA 7 results
Workload predicted burnout, b = 0.42, 95% CI [0.25, 0.59], t(146) = 4.87, p < .001, and the model explained 18% of the variance, R² = .18, F(2, 146) = 16.02, p < .001.
We also help you turn output into publication-ready tables that follow APA table format.
How SPSS help works ¶
- Send your files: the .sav data file (anonymized), any .spv output or .sps syntax, your questionnaire and your research questions.
- Choose support or tutoring: we can work through a live project with you or teach the procedures with practice data. You receive a quote first; see pricing.
- Work together: screen-shared sessions or written walkthroughs with annotated syntax you can re-run.
- Review your write-up: your results section comes back with Track Changes and margin comments explaining each change.
- Ask follow-up questions about any comment after delivery.
Turnaround runs from 24 hours to 7 days, depending on the size of the job. Files are handled confidentially, an NDA is available on request, and we delete files when you ask. We support SPSS 25 and later on Windows and macOS.
SPSS help and academic integrity ¶
We explain, check and teach. We do not complete graded assignments, quizzes or exams, and we do not produce analyses for you to submit as your own work. Rules on outside statistical support vary, so please check your institution's policy and speak to your supervisor. Our academic integrity policy sets out what we will and will not do.