This document contains all weeks of the MVDA SPSS course.
It features questions, steps to take to answer the questions, in which table to find the answer, and how to formulate your answer.
Week 1, Multivariate Regression Analysis
- Can the null hypothesis of no relationship between Y and X1, X2, etc. be rejected? → Perform
linear regression → From ANOVA table report: F(dfregression, dfresidual) = 12.345, p = 0.003
- How much variance of Y is explained by X1, X2, etc. together/the predictors? → Perform
linear regression → From model summary report: R square = 0.123
- Which predictor explains the most unique variance? / How much variance of Y is (uniquely)
explained by X1? → Perform linear regression with part and partial correlation → From
coefficients, correlations, part report: X1: 𝒓𝟐𝒀(𝟏∙𝟐) = (0.123)2 = 0.015
- Remove the non-significant predictors from the model → Check which X are significant in
Coefficients, Sig → Only include significant in next regression.
- What is the estimated regression equation → Perform linear regression → From
Coefficients, Unstandardized Coefficients, B report: 𝒚 ̂ = 𝒃𝟎 + 𝒃𝟏 𝒙𝟏 + 𝒃𝟐 𝒙𝟐 + ⋯ + 𝒃𝒑 𝒙𝒑
- Interpret the regression coefficients → From Coefficients, Unstandardized Coefficients, B
report: Improving one point on X1, would improve Y with b1
- Does adding X2 significantly improve the linear model? / How much variance is explained in
model 2 in comparison to model 1? → Perform linear regression, add X2 to second block and
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tick 𝑅𝑐ℎ𝑎𝑛𝑔𝑒 → From Model Summary, model 2 report: Yes, F(df1, df2) = 12.345, p = 0.003
Linear regression: ANALYZE → REGRESSION → LINEAR → drag X TO INDEPENDENT (based on) and Y
TO DEPENDENT (predict) (Often: Y on X)
• Statistics:
- Confidence interval: Coefficients table → Slope b1, intercept (constant)
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- 𝑅𝑐ℎ𝑎𝑛𝑔𝑒 → When doing a hierarchical regression
- Descriptives: Descriptive statistics → mean, standard deviation, and N
- Part and partial correlations: Coefficients table → look at …
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▪ Part for semipartial correlation → 𝑟𝑌(1∙2) → Square yourself: 𝑟𝑌(1∙2)
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▪ Zero-order correlation → 𝑟𝑌1 → Square yourself: 𝑟𝑌1
- Collinearity diagnostics: Multicollinearity
▪ Tolerance should be above 0.10
▪ VIF should be below 10
• Plots: for assumptions for significance testing
- Scatterplot → predicted values (ZPRED → X) and residuals (ZRESID → Y)
▪ Non-linearity: Is the best fit of the data a horizontal line? (Should be
randomly distributed)
▪ Homoscedasticity: Are the dots equally distributed along the best line?
- Histogram of standardized residuals → Tick box
- Normal probability plot → Tick box
▪ Normality of residuals: do the dots follow the line?
• Save: Residuals Statistics (for outliers)
- Cook’s D < 1 → Influential data points
- Leverage values: Centered leverage value max < 3(K+1)/N → Outliers on X
- Standardized residuals min and max < |3| → Outliers on Y
Remove outliers: DATA → SELECT CASES → TICK IF CONDITION IS SATISFIED → Write: Cook’s D < 1
→ Results with this number will be excluded
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, MVDA – SPSS Cheat sheet
Week 2, Analysis Of Variance
- What is sample size N, what are the group sizes, is the design balanced? → Perform Cross
Tabulation → From Case Processing Summary report: N = 50, From Crosstabulation report:
n1 = 15, n2 = 15, etc., From Crosstabulation report: Balanced if n1 = n2 = ni etc.
- Is the ANOVA F-test robust to violations of group normality → Perform Cross Tabulation →
from Crosstabulation report: n > 15: F is robust to non-normality
- Is the ANOVA F-test robust to violations of homogeneity of the group variances → Perform
Cross Tabulation → from Crosstabulation report: nmax / nmin = …/… = … < 1.5: F robust to …
- Check the assumption of normality by inspecting graphically the distribution of the
standardized residuals → Make a histogram with std. residuals on the X-axis → From Simple
Histogram of Standardized Residual for X, Report whether it is normally distributed. / Make
a Q-Q plot with standardized residuals → From Normal Q-Q Plot of Standardized Residual for
X, Report whether the variables are approx. similar to the line.
- Can the null hypothesis of equal group variances be rejected? Does the rule of thumb with
group standard deviations agrees with your first conclusion? → Perform ANOVA,
homogeneity tests and descriptive statistics → From Levene’s Test of Equality of Error
Variances, Based on Mean, report: Yes, Levene’s test is not significant: F (df1, df2) = 1.234,
p = 0.123. / From Descriptive Statistics, report: Yes, if sdmax / sdmin < 2
- Can the null hypothesis of no relationship between Y and X1 and/or X2 be rejected? (Is our
model good?) → Perform ANOVA → From Tests of Between Subject Effects, Corrected
Model report: Yes/no, F (dfmodel, dferror) = 12.345, p = 0.002
- Is effect of X1 or interaction effect significant? → Perform ANOVA → From Tests of Between
Subject Effects, Effect report: Yes/no, F(dfx1, dferror) = 12.345, p = 0.003 (So there is an effect)
- Which group has the highest estimated marginal mean (2 grp.)? → Perform ANOVA → From
Estimated Marignal Means, X1 report: Group 1 (Mean) has a higher Y than Group 2 (Mean)
- Which group has the highest estimated marginal mean? (3+ group)? → Perform ANOVA →
From Post Hoc Tests, Multiple Comparisons, report: Group A significantly higher than other
two groups (A negative Mean difference → group J higher than I).
- Interpret the interaction effect → Perform ANOVA → From Estimated Marginal Means and
Profile Plot report: Group A higher in condition I, group B reverse effect. No difference
between group A and B in condition II. (The effect of X1 can be changed by the effect of X2)
- How much variance in Y is explained by X1 → Perform ANOVA → From Tests of Between-
Subjects Effects, report: η2 = SSEffect ÷ SSTCorrected (0.01, 0.06, 0.14)
Cross tabulation: Analyze → Descriptive Statistics → Crosstabs → One variable Rows, one Columns
ANOVA: Analyze → General Linear Model → Univariate → Drag independent variables to Fixed
Factors, drag dependent variable to Dependent Variable
• Plots: Drag variable with most groups to Horizontal Axis, the other one to Separate Lines →
Click Add → Optional: Add variables the other way around.
• Options: Descriptive statistics and Homogeneity tests (Levene’s test)
• EM Means: Drag all the effects you want to Display Estimated Marginal Means for
• Save: Will make a new variable for Standardized residuals
• Post Hoc → Drag X1 to Post Hoc Tests For → Tick Tukey
Make a histogram: GRAPHS → CHART BUILDER → HISTOGRAM → Drag variable to X-AXIS
Make a Q-Q plot: ANALYZE → DESCRIPTIVE STATISTICS → Q-Q PLOTS → Move standardized
residuals to VARIABLES → Click OK
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