This study aimed to investigate the effectiveness of using the fraction of missing information (FMI) to select auxiliary variables in imputing missing data in confirmatory factor analysis (CFA). This was done by conducting two studies (a simulation study and an empirical study). A Monte Carlo simulation technique was used to compare the performance and the effect of the restrictive strategy based on FMI and the inclusive strategy on parameter estimate bias and parameter estimate efficiency. The missing data mechanisms, missing data proportion, correlation strength between the analysis variables and auxiliary variables, and the inclusive and restrictive strategies were assessed in the simulation study for their impact on three dependent variables: bias, mean squared error (MSE), and confidence interval coverage of parameters. In addition, the difference between the inclusive and restrictive strategies was examined using empirical data where missing data were designed with two levels of missingness (15% and 30%) and two missingness mechanisms to assess their impact on parameter estimate bias, gain in efficiency, and power. In the simulation study, factorial ANOVAs were conducted to assess the design factors and their interactions’ effects. The results indicated that the design factors had no impact on study. The two strategies showed no impact on parameter estimate bias for the empirical data. Yet, the restrictive strategy based on the FMI outperformed the inclusive strategy in terms of gains in efficiency and power. Thus, there is an initial support of using the FMI to evaluate the auxiliary variables.
","abstract_html":"<p>This study aimed to investigate the effectiveness of using the fraction of missing information (FMI) to select auxiliary variables in imputing missing data in confirmatory factor analysis (CFA). This was done by conducting two studies (a simulation study and an empirical study). A Monte Carlo simulation technique was used to compare the performance and the effect of the restrictive strategy based on FMI and the inclusive strategy on parameter estimate bias and parameter estimate efficiency. The missing data mechanisms, missing data proportion, correlation strength between the analysis variables and auxiliary variables, and the inclusive and restrictive strategies were assessed in the simulation study for their impact on three dependent variables: bias, mean squared error (MSE), and confidence interval coverage of parameters. In addition, the difference between the inclusive and restrictive strategies was examined using empirical data where missing data were designed with two levels of missingness (15% and 30%) and two missingness mechanisms to assess their impact on parameter estimate bias, gain in efficiency, and power. In the simulation study, factorial ANOVAs were conducted to assess the design factors and their interactions’ effects. The results indicated that the design factors had no impact on study. The two strategies showed no impact on parameter estimate bias for the empirical data. Yet, the restrictive strategy based on the FMI outperformed the inclusive strategy in terms of gains in efficiency and power. Thus, there is an initial support of using the FMI to evaluate the auxiliary variables.</p>","abstract_has_math":false,"creators":["Alzahrani, Dareen Taha"],"institution":null,"degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":null,"degree_department":null,"school":null,"contributors":["Duan Zhang","Kathy Green","Jeanine Coleman","Kristina Hesbol"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-01-01T08:00:00Z","date_published":"2022-01-01T08:00:00Z","updated_at":"2026-07-24T02:03:26Z","subjects":["Confirmatory factor analysis","Fraction of missing information","Missing data","Other Statistics and Probability","Physical Sciences and Mathematics","Statistical Methodology","Statistics and Probability"],"languages":["en"],"rights":["Copyright is held by the author. User is responsible for all copyright compliance.
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"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Using the Fraction of Missing Information (FMI) in Selecting Auxiliary Variables to Impute Missingness in Confirmatory Factor Analysis (CFA)"]}]}],"canonical_facts":{"dc:contributor":["Duan Zhang","Kathy Green","Jeanine Coleman","Kristina Hesbol"],"dc:creator":["Alzahrani, Dareen Taha"],"dc:date.available":["2024-07-21T07:00:00Z"],"dc:description.abstract":["This study aimed to investigate the effectiveness of using the fraction of missing information (FMI) to select auxiliary variables in imputing missing data in confirmatory factor analysis (CFA). This was done by conducting two studies (a simulation study and an empirical study). A Monte Carlo simulation technique was used to compare the performance and the effect of the restrictive strategy based on FMI and the inclusive strategy on parameter estimate bias and parameter estimate efficiency. The missing data mechanisms, missing data proportion, correlation strength between the analysis variables and auxiliary variables, and the inclusive and restrictive strategies were assessed in the simulation study for their impact on three dependent variables: bias, mean squared error (MSE), and confidence interval coverage of parameters. In addition, the difference between the inclusive and restrictive strategies was examined using empirical data where missing data were designed with two levels of missingness (15% and 30%) and two missingness mechanisms to assess their impact on parameter estimate bias, gain in efficiency, and power. In the simulation study, factorial ANOVAs were conducted to assess the design factors and their interactions’ effects. The results indicated that the design factors had no impact on study. The two strategies showed no impact on parameter estimate bias for the empirical data. Yet, the restrictive strategy based on the FMI outperformed the inclusive strategy in terms of gains in efficiency and power. Thus, there is an initial support of using the FMI to evaluate the auxiliary variables.
"],"dc:format":["application/pdf"],"dc:identifier":["https://digitalcommons.du.edu/etd/2035"],"dc:language":["en"],"dc:rights":["Copyright is held by the author. User is responsible for all copyright compliance.
"],"dc:subject":["Confirmatory factor analysis","Fraction of missing information","Missing data","Other Statistics and Probability","Physical Sciences and Mathematics","Statistical Methodology","Statistics and Probability"],"dc:title":["Using the Fraction of Missing Information (FMI) in Selecting Auxiliary Variables to Impute Missingness in Confirmatory Factor Analysis (CFA)"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."]},"updated_at":"2026-07-24T02:03:26Z"}