Research Methods in Experimental Psychology

Handouts

Email Syllabus

STATISTICS

How to Select the Correct Statistical Test

THINGS TO CONSIDER

  • How many variables do you have
    • How many Independent Variables (IV)
    • How many Dependent Variables (DV)
  • What types of data do you have:
    • Is the IV(s) Continuous or Categorical
    • Is the DV(s) Continuous or Categorical

STEPS

  • Based on the number and types of variables you have, use the grids in the sections below (i.e., Univariate, Bivariate, & Multivariate) to find the correct tests for your data.
  • Then scroll down to find the instructional videos for those tests in the video sections below.

REVIEW

CONTINUOUS

Sometimes called quantitative variables. Called Scale data in SPSS.

Interval variables that can be measured along a continuum and they have a numerical
  • Intelligence
  • Aptitude
  • Temperature
Ratio variables are interval variables, but with the added condition that 0 (zero) of the measurement indicates that there is none of that variable.
  • Reaction time
  • Weight
  • Age
  • Frequencies of Behavior

CATEGORICAL

Sometimes called qualitative, discrete, or dichotomous variables. Called Ordinal and Nominal data in SPSS.

Ordinal data that has a distinct order. There’s a meaning to the order.
  • Two, Three, And Four Star Restaurants
  • Ranking TV Programs By Popularity
Nominal data are distinct groups with no meaning to the order.
  • Sex
  • States
  • Colors


VARIABLE TYPE TEST
Categorical Frequencies
Categorical Explore for Categorical Data
Continuous Descriptives
Continuous Explore for Continuous Data

IV

DV

TEST

Continuous Continuous Correlation
Categorical (≥ 2 groups) Categorical (≥ 2 groups) Chi-Square
Categorical (2 groups) Continuous Independent Samples T-test
Categorical (1 group 2 conditions) Continuous Paired Samples T-test
Categorical (≥ 2 groups) Continuous One-Way Between Subjects ANOVA
1 Group Continuous (≥ 3 conditions) One-Way Within Subjects ANOVA (Repeated Measures)

IV 1

IV 2

DV

TEST

Continuous Continuous Continuous Simple Linear Regression
Continuous (≥ 2) Continuous Multiple Regression
Continuous (≥ 3) Path Analysis/Structural Equation Modeling (SEM; Predictive Modeling)
Continuous Continuous or Categorical Continuous Stepwise Multiple Regression
Continuous Continuous or Categorical Continuous Hierarchical Regression
Continuous (≥ 1) Categorical (≥ 1) Categorical (2 groups) Logistic Regression
Continuous (≥ 1) Categorical (≥ 1) Categorical (≥ 3 groups) Multinomial Logistic Regression
Categorical Categorical Categorical Log-Linear Regression
Categorical Categorical Continuous Factorial ANOVA (2 X 2 ANOVA)
Categorical Categorical Continuous One-Between-One-Within ANOVA (a.k.a. Mixed Factorial)
Categorical Categorical or Continuous Continuous ANCOVA
Categorical (≥ 1) Categorical (optional) Continuous (≥ 2) MANOVA
Categorical (≥ 1) Categorical (optional) Continuous (≥ 2) MANCOVA

Introduction to the SPSS Interface

How to Create Variables in SPSS

Import, Export & Save: Excel, CSV, Text

Select Cases

Split File

Recode Variables

Compute Variables

Formatting Tips for Tables with SPSS


Frequencies

Descriptives

Explore for Categorical Data

Explore for Continuous Data


Correlation

Chi-Square

Independent Samples T-test

Paired Samples T-test

One-Way Between Subjects ANOVA

One-Way Within Subjects ANOVA (Repeated Measures)


Simple Linear Regression (Predictive Modeling)

Multiple Regression (Predictive Modeling)

Path Analysis/Structural Equation Modeling (SEM; Predictive Modeling)

Stepwise Multiple Regression

Hierarchical Regression

Logistic Regression

Multinomial Logistic Regression

Log-Linear Regression

Factorial ANOVA (2 X 2 ANOVA)

One-Between-One-Within ANOVA (a.k.a. Mixed Factorial)

ANCOVA

MANOVA

MANCOVA


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