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  • 00:06

    SPEAKER 1: In this chapter, we exploredhow to analyze data that has comefrom hypotheses with multiple independent variables.

  • 00:13

    SPEAKER 2: There are two basic forms of analysis--the general linear model and analysis of variance, or ANOVA.

  • 00:24

    SPEAKER 1: These two approaches are useful for estimate effectsizes.The general linear model is used to calculate direct effectsizes.ANOVA can be used to calculate unique effect sizes.This table captures the most important differencesbetween the two approaches to multiple independent variableanalysis.

  • 00:44

    SPEAKER 2: A notes on workflow--the two processes can be used on the same set of datato provide a comprehensive insight into the variables.Or you can choose the one that better suitsthe purposes of your research.

  • 00:58

    SPEAKER 1: Bear in mind that where unique effectsizes differ considerably from total effect sizes,the psychological meaning of the variables must be considered.These differences suggest that thereare more complicated things happeningthan just the influence of an independent variableon a dependent variable.This is where things get interesting.

  • 01:19

    SPEAKER 1 [continued]: For more information, see study.sagepub.com/statisticsforpsychology.

Video Info

Publisher: SAGE Publications Ltd.

Publication Year: 2019

Video Type:Tutorial

Methods: Data analysis skills, General linear models, Analysis of variance

Keywords: analysis of variance; data analysis; general linear models

Segment Info

Segment Num.: 1

Persons Discussed:

Events Discussed:

Keywords:

Abstract

Two approaches for analysis of data with multiple independent variables—general linear model and analysis of variance (ANOVA)—are reviewed.

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Analyzing Data With Two or More Independent Variables

Two approaches for analysis of data with multiple independent variables—general linear model and analysis of variance (ANOVA)—are reviewed.

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