Regression can be used to predict variables:

• IV: x axis (predictor)
• DV: y axis (criterion / outcome)

Analyze -> Regression -> Linear

•  move variables into corresponding boxes
• ensure “Method = Enter”
• in SPSS
  • raw equation: DV = (B [slope] x IV) + constant [intercept]
  • standardised: Z_DV = β [beta] x Z_IV (standardised variables to Z scores)
  • standardised is a better indicator of strength and measure independent of units
• in multiple regression (more than one IV) β measures unique effect of IV – no shared variance
• look in last table for β value (the correlation) and B and constant
• look in ‘model summary’ table for R² (proportion of variance explained by all IVs)
• look in ‘ANOVA’ table for F value (to check if R² predicts DV better than chance – if F is significant then R² is better)
• R²: need to multiply by 100 to get %

Bivariate = linear relationship between TWO variables.

Pearson correlation = r = parametric, assume normal distribution (more powerful)

Spearman’s = ρ (rho) = nonparametric ranked data

Analyze -> Correlations -> Bivariate

• Move variables into box on right
• Tick “Pearson” or “Spearman”
• In output look at the 2 numbers that are the same and significant.

Scattergram: to check it’s linear

Graphs -> Legacy Dialogs -> Scatter/Dot

• Click “Simple Scatter” and move the variables to the axes (it doesn’t matter which one goes where)
• To get line of best fit, right-click -> edit content -> Elements -> fit line at total

Phi and Cramer’s V = only look at phi (φ) = both variables dichotomous (2 categories)

Analyze -> Descriptive Stats -> Crosstabs

• Click “Stats” and choose “Phi and Cramer’s V”
• Click “Cells” and tick “Observed” and “expected”
• Check how variables are coded because a negative correlation could mean a positive relationship if coded non-intuitively (low numbers = high levels of variable).

Chart Builder – doesn’t calculate mean scores for ordinal variables, so you have to change “measure” to “scale”, but it is easier to use quickly.

Graphs -> Legacy Dialogs -> (choose graph)

Simple Bar Chart: tick “summaries of separate variables”

• Right-click on chart in output, click “Edit content”
• Can add title (above the chart), footer (below the chart), and change pattern of bars by clicking properties -> double-clicking on the bar -> “fill and border”

Clustered Bar Chart: tick “summaries of groups of cases”

• Tick what the bars represent (eg mean) and put in the variable
• Move that variable to “Category axis”
• Move the variable you want to split the file by to “Define clusters by”

Multiple Line Graph: tick “summaries for groups of cases”

• Tick what the lines represent (N for frequency/mean) and put in the variable (if mean)
• Move the variable to “Category axis”
• Move the variable you want to split the file by to “Define lines by”
• Can edit in output through right-clicking

Analyze -> Table -> Custom Tables

If trying to find mean of categorical variables, need to change measure to “Scale” in variable view.

• Move categorical variables to left (rows)
• Move variables you want to split the file by to top (columns)
• Click variable on left -> click “Summary stats” at bottom left
• Change Format to “nnnn” and Decimals to “2”
• To add SD or anything else: “Summary stats” -> Move SD from “Statistics” box to box on the right
• Same with cumulative %: Move “Column %”
• To add Total: Click on the variable you want the total for -> Click “Categories and Totals” at bottom left -> Tick “Total” on right

Categorical Tables: frequencies, so change measure back to “ordinal”

• Need to use custom tables when splitting files by TWO variables.

Pivot Tables: Put the second splitting variable into the “Layers” box on the right.

• Can move things around by double-clicking on the table in the output.