Summary BUS3008W SPSS Cheat Sheet

SPSS Cheat Sheet

Basic Descriptive Statistics

Reverse Scoring

Transform > Recode into different variables

To ensure respondents are reading through each question and not rushing through

Nominal/Ordinal Data

Analyse > Descriptive Statistics > Frequencies

Report percentages

Scale Data using Descriptives

Analyse > Descriptive Statistics > Descriptives

Report mean and standard deviation

Scale Data using Explore

Analyse > Descriptive Statistics > Explore

Report mean and standard deviation

Testing for Normality in SPSS

Analyse > Descriptive Statistics > Explore
Statistics > Descriptives (95%) > Outliers
Plots > Histogram > Normality plots with tests

H0: Data is normally distributed
H1: Data is not normally distributed

Three ways to test:
1. Hypothesis testing (Kolmogorov-Smirnov or Shapiro-Wilks)
2. Descriptives (Skewness or Kurtosis)
3. Histograms

Sample > 50 = Kolmogorov-Smirnov
Sample < 50 = Shapiro-Wilks

Skewness within range -1 and 1
Kurtosis within range -1.5 and 1.5




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, Crosstabs, Reliability and Validity

Crosstabs

Analyse > Descriptive Statistics > Crosstabs

Does “nominal variable x” influence “nominal variable y”?
Is there an association between “nominal variable x” and “nominal variable y”?

Measures associations between 2 nominal variables

H0: There is no association between x and y
H1: There is an association between x and y

Look at Pearson’s Chi-squared statistics

Item Reliability

Analyse > Scale > Reliability Analysis
[Put in individual items not summated; remember they need to be reverse scored]
Statistics > Scale if deleted item

Reliability is analysed to determine how a scale performs when used multiple times.
It enables us to determine whether the items in the scale consistently measure their
respective construct.

Cronbach’s alpha (scale) should always be above 0.7

Summated Scales

Transform > Compute Variable

Statistical & Mean (must be reverse scored first!)
Use the means = necessary to preserve the measure of the scale; has to accurately
represent the construct

Scale Validity

Analyse > Dimension Reduction > Factor
Extraction > Principle Components
Rotation > Varimax

Validity is analysed to determine how items load onto factors. It enables us to
determine whether items are correctly associated with their respective constructs
thus ensuring that the correct constructs are being used in the study. % of Variance
& Rotated Component Matrix

Factor Analysis:
 Exploratory – to search for latent variables when developing new scales.


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