Recent studies have indicated that under the proper circumstances factor anaylisis may be accurately performed in samples as small as N = 9. However, all of these studies have extracted a pre-known number of factors, leaving an examination of determining the proper number of factors to future studies. The current study uses examines the following methods for determining the proper number of factors: Monte Carlo data to examine the performance of common versions of the Kaiser Rule, minimum average partial, parallel analysis and salient loading criteria under the conditions created by all possible combinations of method, model strength, overdetermination and sample size. Method performance was compared for overall accuracy (percent correct), and average discrepancy (mean difference from correct). ANOVA revealed that item level methods, including salient loading criteria and MAP procedures, maintain accuracy when model strength is at least moderate and overdetermiantion is high. Use of selected empirical methods for determining the number of factors is possible in small sample sizes only when overdetermination and model strength are adequately high, larger sample sizes should be preferred when possible.
","abstract_html":"<p>Recent studies have indicated that under the proper circumstances factor anaylisis may be accurately performed in samples as small as N = 9. However, all of these studies have extracted a pre-known number of factors, leaving an examination of determining the proper number of factors to future studies. The current study uses examines the following methods for determining the proper number of factors: Monte Carlo data to examine the performance of common versions of the Kaiser Rule, minimum average partial, parallel analysis and salient loading criteria under the conditions created by all possible combinations of method, model strength, overdetermination and sample size. Method performance was compared for overall accuracy (percent correct), and average discrepancy (mean difference from correct). ANOVA revealed that item level methods, including salient loading criteria and MAP procedures, maintain accuracy when model strength is at least moderate and overdetermiantion is high. Use of selected empirical methods for determining the number of factors is possible in small sample sizes only when overdetermination and model strength are adequately high, larger sample sizes should be preferred when possible.</p>","abstract_has_math":false,"creators":["Porritt, Marc Thomas"],"institution":null,"degree_name":"Doctor of Philosophy (PhD)","degree_level":"Dissertation","degree_discipline":"Psychology","degree_department":null,"school":null,"contributors":["Boyd, Kendal C.","Distelberg, Brian","Herbozo, Sylvia M.","Morrell, Holly E. R."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2015,"date_issued":"2015-09-01T07:00:00Z","date_published":"2015-09-01T07:00:00Z","updated_at":"2026-07-24T02:52:27Z","subjects":["Clinical Psychology","Psychology","Social and Behavioral Sciences","Factor Analysis; Quantitative Research; Psychometrics; Variability (Psychometrics); Number Concept","Monte Carlo Data; Kaiser Rule; Minimum Average Partial; Parallel Analysis; Salient Loading Criteria"],"languages":["English"],"rights":["This title appears here courtesy of the author, who has granted Loma Linda University a limited, non-exclusive right to make this publication available to the public. The author retains all other copyrights."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsrepository.llu.edu/etd/283","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Boyd, Kendal C.","Distelberg, Brian","Herbozo, Sylvia M.","Morrell, Holly E. 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The current study uses examines the following methods for determining the proper number of factors: Monte Carlo data to examine the performance of common versions of the Kaiser Rule, minimum average partial, parallel analysis and salient loading criteria under the conditions created by all possible combinations of method, model strength, overdetermination and sample size. Method performance was compared for overall accuracy (percent correct), and average discrepancy (mean difference from correct). ANOVA revealed that item level methods, including salient loading criteria and MAP procedures, maintain accuracy when model strength is at least moderate and overdetermiantion is high. Use of selected empirical methods for determining the number of factors is possible in small sample sizes only when overdetermination and model strength are adequately high, larger sample sizes should be preferred when possible.
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