20241103 - many decisions in factor analysis

15-Factor-Analysis-PCA.Rmd

@@ -10,7 +10,7 @@ It aims to identify the optimal latent structure for a group of variables.\index
 [*Confirmatory factor analysis*](#cfa) (CFA) is a latent variable modeling approach that is used when a researcher wants to evaluate how well a hypothesized model fits, and the model can be examined in comparison to alternative models.\index{factor analysis!confirmatory}\index{latent variable}
 Using a [CFA](#cfa) approach, the researcher can pit models representing two theoretical frameworks against each other to see which better accounts for the observed data.\index{factor analysis!confirmatory}
 
-[Factor analysis](#factorAnalysis) is considered to be a "pure" data-driven method for identifying the structure of the data, but the "truth" that we get depends heavily on the decisions we make regarding the parameters of our [factor analysis](#factorAnalysis).\index{factor analysis!confirmatory}
+[Factor analysis](#factorAnalysis) is considered to be a "pure" data-driven method for identifying the structure of the data, but the "truth" that we get depends heavily on the decisions we make regarding the parameters of our [factor analysis](#factorAnalysis) [@Floyd1995; @Sarstedt2024].\index{factor analysis!confirmatory}
 The goal of [factor analysis](#factorAnalysis) is to identify simple, parsimonious factors that underlie the "junk" (i.e., scores filled with measurement error) that we observe.\index{parsimony}\index{factor analysis!confirmatory}
 
 It used to take a long time to calculate a [factor analysis](#factorAnalysis) because it was computed by hand.\index{factor analysis!confirmatory}
@@ -53,7 +53,7 @@ Another class of [factor analysis](#factorAnalysis) models are [higher-order](#h
 Guidelines in using [higher-order factor](#higherOrderModel) and [bifactor](#bifactorModel) models are discussed by @Markon2019.\index{factor analysis}\index{factor analysis!higher-order}\index{factor analysis!bifactor}
 
 [Factor analysis](#factorAnalysis) is a powerful technique to help identify the factor structure that underlies a measure or construct.\index{factor analysis}
-As discussed in Section \@ref(factorAnalysisDecisions), however, there are many decisions to make in [factor analysis](#factorAnalysis), in addition to questions about which variables to use, how to scale the variables, etc.\index{factor analysis}
+As discussed in Section \@ref(factorAnalysisDecisions), however, there are many decisions to make in [factor analysis](#factorAnalysis), in addition to questions about which variables to use, how to scale the variables, etc. [@Floyd1995; @Sarstedt2024].\index{factor analysis}
 If the variables going into a [factor analysis](#factorAnalysis) are not well assessed, [factor analysis](#factorAnalysis) will not rescue the factor structure.\index{factor analysis}
 In such situations, there is likely to be the problem of garbage in, garbage out.\index{factor analysis}
 Factor analysis depends on the covariation among variables.\index{factor analysis}
@@ -230,7 +230,7 @@ Recommendations for [factor analysis](#factorAnalysis) are described by @Sellbom
 
 ### Decisions to Make in Factor Analysis {#factorAnalysisDecisions}
 
-There are many decisions to make in [factor analysis](#factorAnalysis).\index{factor analysis!decisions}
+There are many decisions to make in [factor analysis](#factorAnalysis) [@Floyd1995; @Sarstedt2024].\index{factor analysis!decisions}
 These decisions can have important impacts on the resulting solution.\index{factor analysis!decisions}
 Decisions include things such as:\index{factor analysis!decisions}