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.

% frequencies may look big, but they depend on the number of participants, so always look at the normal frequency to compare.

Analyze -> Descriptive Stats -> Frequencies

• move variables into box on right
• click “Statistics” to choose only the descriptives and percentiles you want along with the frequencies
• click “Charts” to choose histograms (continuous variables) or bar charts (categorical variables)
• you can split the file beforehand to look at (e.g.) gender separately

To get box-plots you have to use Analyze -> Descriptive Stats -> Explore

• put variables in the “Dependent List”
• put variable you want to split file by in “Factor List”
• click “Plots” to choose histogram too, and tick “Both” at bottom

Box-plots

• use mainly to compare the max and min values of groups
• longer horizontal line = more variation in scores (range is bigger)
•  = positive skew =
•  = negative skew = 
• can compare medians:

Descriptive stats

• distribution (skew, kurtosis)
• central tendency (mean, median mode)
• dispersion (SD, variance, range)

Analyze -> Descriptive Stats -> Descriptives

• move variables into box on right
• click “Options” and choose the descriptives that you want
• click “OK”
• you can split the file beforehand to look at (eg) gender separately

Skew

• may affect analyses because we assume normality
• positive number = 
• negative number = 
• 0 = normal distribution

Kurtosis

• no impact on results
• positive number = leptokurtic  (scores clustered tight)
• negative number = platycurtic  (scores not clustered)
• 0 = mesokurtic (normal distribution)

Define the variables – indicate the type of data, and whether the values represent something more than numbers.

• Data view = actual data: scores, ratings, gender …
  • variables in columns (participants are across rows, variable is each column

• Variable view = shows info about variables: names, types …
  • no spaces or punctuation in variable names
  • variables in rows (each variable is a row)
  • numeric variable:
    • width = number of digits (eg 3)
    • decimals = number of decimal places (eg 0)
  • SNO = subject number
  • … = opens further window for more options
  • Labels = as long as you want, with spaces and punctuation (purely for your own reference)
  • Values = labelling value
    • purely numeric = no label
    • eg Gender = need to label: 1=Male, 2=Female and width = 1

Recoding variables: eg want to reverse scale: make 1=happy into 1=unhappy

• Transform -> Recode into Different Variables
• Move variable into box on right
• Rename
• Re-label -> click “Change”
• Old and new values -> change 1 to 5, 2 to 4, 3 to 3 …

Computing variables: eg want to add scores of two variables together to make a completely new variable

• Transform -> Compute Variable
• Move variable 1 into slot on right, click +, move variable 2
• Name new variable in “Target Variable” box
• Type + Label -> label new variable

Listing data: eg want only to see data of 3 variables and top 10 participants

• Analyze -> Reports -> Case Summaries
• Move variables into box on right
• Tick “Display cases”
• Tick “Limit cases to first” -> write “10”
• To save output: save to same name as spreadsheet

Split file (to order data by groups): eg if you want to obtain separate descriptives for each gender

• Data -> Split File
• Tick “Organize output by groups”
• Move variable to box on right
• To switch off: Tick “Analyse all cases, do not create groups”