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How to Analyze Your Data

Published on November 2016 | Categories: Documents | Downloads: 11 | Comments: 0



How to Analyze Your Data
Gwen Jenkins Psyc 4170 E

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

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?
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)
Two continuous variables
Relationship between two variables

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
‘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
‘Compare means’
‘Paired-Samples T-Test’

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

‘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)
‘General Linear Model’

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


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

Options: Means, SDs

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

Test of Independence ‘Analyze’
‘Descriptive Statistics’

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’

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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