Old wine in a new bottle? Re-examining speeded performance in mental arithmetic using linear mixed models

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Creator: 

Ma, Chunyun

Date: 

2017

Abstract: 

Theory construction in cognitive psychology relies on choosing a suitable statistical model for data analyses. I hypothesized that the general linear model (GLM), which is currently used for analyzing data in the field of mental arithmetic by most researchers, is not suitable for analyzing data that is typical in this field. In particular, mental arithmetic data have three features that make them unsuitable for GLM analyses: repeated measurements from individual participants, a mixture of categorical and continuous predictors, and unevenly distributed observations across levels of predictors. I proposed an alternative approach using linear mixed models (LMM), as a better candidate. I tested the hypothesis by applying GLM and LMM to three archival datasets typical in the field of mental arithmetic. Across the re-analyses, LMM consistently showed advantages over GLM. LMM was used successfully on unbalanced designs (Chapter 5) and better preserved the continuous nature of independent variables (Chapters 3, 4, and 5). I discussed the findings and provided advice for researchers who want to adopt LMM in their studies.

Subject: 

Psychology - Cognitive

Language: 

English

Publisher: 

Carleton University

Thesis Degree Name: 

Doctor of Philosophy: 
Ph.D.

Thesis Degree Level: 

Doctoral

Thesis Degree Discipline: 

Psychology

Parent Collection: 

Theses and Dissertations

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