Data Analysis

SPSS Analysis: A Complete, Worked Guide for Research and Business Data

Q: What Cronbach's alpha is acceptable for a thesis?

0.70 and above is the conventional minimum for research instruments; 0.60 may be defended for short exploratory scales of three or fewer items if you state the limitation. Below 0.60 you should revise the scale or drop weak items after inspecting the 'Alpha if Item Deleted' column.

Q: What do I do when my data fails the normality test in SPSS?

First check whether the failure is driven by outliers or by an incorrect code. If the distribution is genuinely non-normal, either transform the variable (log, square root) or use the non-parametric equivalent: Mann-Whitney U instead of the independent t-test, Wilcoxon signed-rank instead of the paired t-test, and Kruskal-Wallis instead of one-way ANOVA. With large samples (n > 30 per group) the t-test and ANOVA are robust to moderate non-normality.

GOSPELTRADER Research Desk · 11 September 2026 · 12 min read

Quick answer

SPSS analysis follows a fixed order: define variables and measurement levels, clean and recode, test reliability of scales (Cronbach's alpha ≥ 0.70), run descriptives, verify assumptions for the intended test, then run the inferential procedure and report the statistic, degrees of freedom, p-value and effect size in APA format.

1. Set up the data file correctly before any analysis

Most SPSS errors are declared, not computed. In Variable View, every variable needs a type, a set of value labels, a defined missing-value code and a correct Measure setting (Nominal, Ordinal, Scale). SPSS silently allows a mean of a nominal variable; the software will not stop you from producing nonsense.

Reverse-scored questionnaire items must be recoded before scale scores are computed. For a 5-point Likert item, the recode is new = 6 - old; for a k-point scale, new = (k + 1) - old.

• Transform → Recode into Different Variables for reverse items (never overwrite the raw item).
• Transform → Compute Variable for scale totals or means.
• Data → Identify Duplicate Cases before reporting any N.
• Analyze → Descriptive Statistics → Frequencies to confirm every code is legal (no 7s in a 1-5 item).

2. Reliability: Cronbach's alpha, with the formula

For any multi-item construct you must report internal consistency before using the composite score. Menu path: Analyze → Scale → Reliability Analysis, model Alpha, and tick Scale if item deleted.

Interpretation: α ≥ 0.90 excellent, 0.80-0.89 good, 0.70-0.79 acceptable, 0.60-0.69 questionable, < 0.60 unacceptable. If alpha is low, inspect the 'Alpha if Item Deleted' column — a single miscoded reverse item is the usual culprit.

Cronbach's alpha
  α = (k / (k - 1)) * (1 - (Σ s²_i / s²_total))

  k        = number of items in the scale
  s²_i     = variance of item i
  s²_total = variance of the summed scale score

Worked example: k = 6, Σ s²_i = 7.20, s²_total = 21.60
  α = (6/5) * (1 - 7.20/21.60) = 1.2 * (1 - 0.3333) = 0.80

3. Choose the right procedure

The test follows from the measurement level of the outcome and the number and type of predictors — not from what is easiest to run.

Research question	SPSS menu path	Reported statistic
Compare 2 independent groups on a scale outcome	Analyze → Compare Means → Independent-Samples T Test	t(df), p, Cohen's d
Compare 2 paired measurements	Analyze → Compare Means → Paired-Samples T Test	t(df), p, d
Compare 3+ groups	Analyze → Compare Means → One-Way ANOVA (+ Tukey)	F(df1, df2), p, η²
Association between two scale variables	Analyze → Correlate → Bivariate (Pearson)	r, p
Predict a scale outcome	Analyze → Regression → Linear	R², F, β, t, p
Predict a binary outcome	Analyze → Regression → Binary Logistic	B, Wald, Exp(B), 95% CI
Association between two categorical variables	Analyze → Descriptive Statistics → Crosstabs → Chi-square	χ²(df), p, Cramér's V

4. Assumption checks you must run and report

A defensible SPSS chapter reports assumptions before results. Examiners and reviewers look for exactly these.

• Normality: Shapiro-Wilk (n < 50) or Kolmogorov-Smirnov, plus a Q-Q plot. Skewness and kurtosis within ±2 is generally acceptable for parametric tests.
• Homogeneity of variance: Levene's test (p > .05 supports equal variances; otherwise read the 'Equal variances not assumed' row).
• Linearity: scatterplot of the outcome against each predictor.
• Multicollinearity: Statistics → Collinearity diagnostics. VIF < 10 (conservatively < 5), Tolerance > 0.10.
• Independence of errors: Durbin-Watson between roughly 1.5 and 2.5.
• Outliers: standardised residuals beyond ±3.29 and Cook's distance > 1.

5. A worked regression example, end to end

Question: does training hours and years of experience predict staff productivity score (0-100) for n = 180 employees? Path: Analyze → Regression → Linear; enter productivity as Dependent, training hours and experience as Independents; under Statistics tick Estimates, Confidence intervals, Model fit, Collinearity diagnostics, Durbin-Watson; under Plots put ZRESID on Y and ZPRED on X.

Model summary : R = .62, R² = .384, Adj. R² = .377, Durbin-Watson = 1.94
ANOVA         : F(2, 177) = 55.16, p < .001
Coefficients  : Training hours  B = 1.42, SE = 0.18, β = .48, t = 7.89, p < .001, VIF = 1.21
                Experience      B = 0.86, SE = 0.24, β = .23, t = 3.58, p < .001, VIF = 1.21

APA sentence:
A multiple regression significantly predicted productivity,
F(2, 177) = 55.16, p < .001, R² = .38. Training hours (β = .48, p < .001)
and experience (β = .23, p < .001) were both significant predictors.

6. Reporting rules that prevent revisions

Report exact p-values to three decimals (p = .032), use p < .001 only below that threshold, and always pair a significant test with an effect size: Cohen's d for mean differences, η² or partial η² for ANOVA, r for correlation, R² for regression, Cramér's V for chi-square. State the direction of the effect in words — a supervisor reads the sentence, not the table.

Frequently asked questions

What Cronbach's alpha is acceptable for a thesis?

0.70 and above is the conventional minimum for research instruments; 0.60 may be defended for short exploratory scales of three or fewer items if you state the limitation. Below 0.60 you should revise the scale or drop weak items after inspecting the 'Alpha if Item Deleted' column.

What do I do when my data fails the normality test in SPSS?

First check whether the failure is driven by outliers or by an incorrect code. If the distribution is genuinely non-normal, either transform the variable (log, square root) or use the non-parametric equivalent: Mann-Whitney U instead of the independent t-test, Wilcoxon signed-rank instead of the paired t-test, and Kruskal-Wallis instead of one-way ANOVA. With large samples (n > 30 per group) the t-test and ANOVA are robust to moderate non-normality.

How large a sample do I need for SPSS regression?

A common working rule is n ≥ 50 + 8m for testing the overall model and n ≥ 104 + m for individual predictors, where m is the number of predictors. For five predictors this means about 90 cases for the model and 109 for the coefficients. A formal power analysis is stronger than either rule.

Is SPSS still acceptable for academic publication?

Yes. SPSS remains standard in social sciences, education, nursing and management research. Journals care about the correctness of the method, the assumption reporting and the effect sizes, not about the software brand. For reproducible pipelines or large datasets, R or Python is the better choice.

Want this analysis done for you?

We deliver cleaning, assumption testing, modelling and a reporting-ready write-up.

Start a project