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# How to Analyze Your Data

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## Content

Gwen Jenkins Psyc 4170 E

Quick Overview: Types of Data
Continuous
Ratio: e.g., height, weight Interval: e.g., temperature, shoe size

Discrete/categorical
Ordinal: e.g., first, second, third Nominal: e.g., #93, #13

How you use a scale determines the type of data!
Example. #93, #13 on NHL jerseys is nominal, but it could be ordinal in your study if you chose to make the top player #1, and so on.

What Type of Data Will You Collect?
Questionnaires
Interval rating scales: e.g.,
Please indicate the extent to which you believe the defendant’s claim Do not believe at all 1 2 3 4 5 6 7 Believe completely

Open-ended format: e.g.,
In your own words, please indicate if, and why, you believe the defendant’s claim

Close-ended format: e.g.,
Please check the box that most closely matches why you believe the defendant’s claim No scientific evidence Eye witness testimony Alibi

How Will You Analyze Your Data?
Interval rating scales: means, continuous data T-tests, ANOVA, correlation, regression Open-ended format: many types of responses (steer clear!) – data needs to be coded for reliability Closed-ended format: frequency data Chi-square

T-tests (experimental)
Independent measures,
One IV (discrete, two levels/groups), one DV (continuous) Example: Difference between men and women in levels of implicit self-esteem

Dependent (repeated) measures
One DV (continuous, measured twice) Example: Difference in men’s implicit self-esteem levels before and after a complex problem-solving task

ANOVA (experimental)
One-way ANOVA
One independent variable (discrete), one dependent variable (continuous) Single main effect only (i.e., a difference in means between at least two levels of an IV)

Two-way ANOVA
Two independent variables (discrete), one dependent variable (continuous) Two main effects, one interaction Definition: An interaction occurs when the influence of one IV on the DV variable depends on the level of the second IV.

Correlation/Regression (no causality)
Correlation
Two continuous variables
Relationship between two variables

Regression
One or more predictors, one DV (continuous)
Used for predicting one variable from one or more other variables

Chi-square (my favourite!)
Chi-square Goodness-of-Fit test
One discrete variable – observed vs. expected frequencies

Chi-square Test of Independence
Two discrete variables – observed vs. expected frequencies

How to Enter Data
You MUST identify your questionnaires with a code Choose SHORT, but meaningful, variable names (no hyphens/spaces allowed) Add label if necessary Add values (i.e., 1 = male, 2 = female) Choose number of decimal spaces

Independent T-Test
‘Analyze’
‘Compare means’
‘Independent-Samples T-Test’

Grouping Variable = IV
‘Define groups’ (enter 1, 2 where you have defined 1 and 2 in your dataset)

Test Variable = DV No options (e.g., Cohen’s d, charts) available

Repeated Measures T
‘Analyze’
‘Compare means’
‘Paired-Samples T-Test’

Paired Variables = pre-/post-test measures No options (e.g., Cohen’s d, charts) available

One-Way ANOVA
‘Analyze’ ‘Compare means’ ‘One-way ANOVA’ Factor = IV Dependent List = DV Post hoc (Tukey) No options ‘Analyze’ ‘General Linear Model’ ‘Univariate’ Fixed Factor = IV (do not use random factor) Dependent Variable = DV Post hoc = Tukey Plots Options: Estimates of effect size

Two-Way ANOVA (factorial)
‘Analyze’
‘General Linear Model’
‘Univariate’

Fixed Factor = IVs (do not use random factor) Dependent Variable = DV Post hoc = Tukey Plots Options: Estimates of effect size

Correlation
‘Analyze’
‘Correlate’
‘Bivariate’

Variables = measures Default = Pearson
Use Spearman for ranked/ordinal data

Options: Means, SDs

Chi-Square
Goodness-of-Fit ‘Analyze’
‘Non-parametric tests’
‘Chi-Square’

Test of Independence ‘Analyze’
‘Descriptive Statistics’
‘Crosstabs’

Expected Values: leave at ‘all categories equal’ unless you have a good theory for why they may be different

Row: Add one variable Column: Add second variable Statistics: Chi-Square Cells: Observed & Expected From results: Report Pearson Chi-Square from ‘Chi Square Tests’

Charts/Plots
Some tests include plots – some don’t If not, Choose
‘Graphs’ ‘Legacy Dialogs’ Whichever type of chart you want to build

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