Rasch is a mathematical model that allows researchers to compare data that measure a unidimensional trait or ability (Bond & Fox, 2007). When data fit the Rasch model, it is mathematically proven that the item difficulty estimates are independent of the sample of respondents. The purpose of this study was to test the robustness of the Rasch model with regards to its ability to maintain invariant item difficulty estimates when real (data that does not perfectly fit the Rasch model), polytomous scored data is used. The data used in this study comes from a university alumni questionnaire that was collected over a period of five years. The analysis tests for significant variation between (a) small samples taken from a larger sample, (b) a base sample and subsequent (longitudinal) samples and (c) variation over time with confounding variables. The confounding variables studied include (a) the gender of the respondent and (b) the respondent's type of major at the time of graduation. The study used three methods to assess variation: (a) the between-fit statistic, (b) confidence intervals around the mean of the estimates and (c) a general linear model. The general linear model used the person residual statistic from the Winsteps' person output file as a dependent variable with year, gender and type of major as independent variables. Results of the study support the invariant nature of the item difficulty estimates when polytomous data from the alumni questionnaire is used. The analysis found comparable results (within sampling error) for the between-fit statistics and the general linear model. The confidence interval method was limited in its usefulness due to small confidence bands and the limitation of the plots. The linear model offered the most valuable data in that it provides methods to not only detect the existence of variation but to assess the relative magnitude of the variation from different sources. Recommendations for future research include studies regarding the impact of sample size on the between-fit statistic and confidence intervals as well as the impact of large amounts of systematic missing data on the item parameter estimates.
","abstract_html":"<p>Rasch is a mathematical model that allows researchers to compare data that measure a unidimensional trait or ability (Bond & Fox, 2007). When data fit the Rasch model, it is mathematically proven that the item difficulty estimates are independent of the sample of respondents. The purpose of this study was to test the robustness of the Rasch model with regards to its ability to maintain invariant item difficulty estimates when real (data that does not perfectly fit the Rasch model), polytomous scored data is used. The data used in this study comes from a university alumni questionnaire that was collected over a period of five years. The analysis tests for significant variation between (a) small samples taken from a larger sample, (b) a base sample and subsequent (longitudinal) samples and (c) variation over time with confounding variables. The confounding variables studied include (a) the gender of the respondent and (b) the respondent's type of major at the time of graduation. The study used three methods to assess variation: (a) the between-fit statistic, (b) confidence intervals around the mean of the estimates and (c) a general linear model. The general linear model used the person residual statistic from the Winsteps' person output file as a dependent variable with year, gender and type of major as independent variables. Results of the study support the invariant nature of the item difficulty estimates when polytomous data from the alumni questionnaire is used. The analysis found comparable results (within sampling error) for the between-fit statistics and the general linear model. The confidence interval method was limited in its usefulness due to small confidence bands and the limitation of the plots. The linear model offered the most valuable data in that it provides methods to not only detect the existence of variation but to assess the relative magnitude of the variation from different sources. Recommendations for future research include studies regarding the impact of sample size on the between-fit statistic and confidence intervals as well as the impact of large amounts of systematic missing data on the item parameter estimates.</p>","abstract_has_math":false,"creators":["Curtin, Joseph A."],"institution":"Brigham Young University - Provo","degree_name":"PhD","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:28:54Z","subjects":["Rasch","Invariance","Polytomous","Rating Scale","Item Difficulties","Educational Psychology"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsarchive.byu.edu/etd/1168","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Curtin, Joseph A."]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2007-08-20T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Brigham Young University - Provo"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Rasch","Invariance","Polytomous","Rating Scale","Item Difficulties","Educational Psychology"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsarchive.byu.edu/etd/1168","https://scholarsarchive.byu.edu/context/etd/article/2167/viewcontent/ETD_CISOPTR_1172.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["David O. McKay School of Education; Instructional Psychology and Technology"]},{"key":"dc:description.abstract","label":"Abstract","values":["Rasch is a mathematical model that allows researchers to compare data that measure a unidimensional trait or ability (Bond & Fox, 2007). When data fit the Rasch model, it is mathematically proven that the item difficulty estimates are independent of the sample of respondents. The purpose of this study was to test the robustness of the Rasch model with regards to its ability to maintain invariant item difficulty estimates when real (data that does not perfectly fit the Rasch model), polytomous scored data is used. The data used in this study comes from a university alumni questionnaire that was collected over a period of five years. The analysis tests for significant variation between (a) small samples taken from a larger sample, (b) a base sample and subsequent (longitudinal) samples and (c) variation over time with confounding variables. The confounding variables studied include (a) the gender of the respondent and (b) the respondent's type of major at the time of graduation. The study used three methods to assess variation: (a) the between-fit statistic, (b) confidence intervals around the mean of the estimates and (c) a general linear model. The general linear model used the person residual statistic from the Winsteps' person output file as a dependent variable with year, gender and type of major as independent variables. Results of the study support the invariant nature of the item difficulty estimates when polytomous data from the alumni questionnaire is used. The analysis found comparable results (within sampling error) for the between-fit statistics and the general linear model. The confidence interval method was limited in its usefulness due to small confidence bands and the limitation of the plots. The linear model offered the most valuable data in that it provides methods to not only detect the existence of variation but to assess the relative magnitude of the variation from different sources. Recommendations for future research include studies regarding the impact of sample size on the between-fit statistic and confidence intervals as well as the impact of large amounts of systematic missing data on the item parameter estimates.
"]},{"key":"dc:format","label":"Dc Format","values":["application:pdf"]},{"key":"dc:source","label":"Dc Source","values":["Brigham Young University - Provo"]},{"key":"dc:title","label":"Title","values":["Testing the Assumption of Sample Invariance of Item Difficulty Parameters in the Rasch Rating Scale Model"]}]}],"canonical_facts":{"dc:creator":["Curtin, Joseph A."],"dc:date":["2007-08-20T07:00:00Z"],"dc:description":["David O. McKay School of Education; Instructional Psychology and Technology"],"dc:description.abstract":["Rasch is a mathematical model that allows researchers to compare data that measure a unidimensional trait or ability (Bond & Fox, 2007). When data fit the Rasch model, it is mathematically proven that the item difficulty estimates are independent of the sample of respondents. The purpose of this study was to test the robustness of the Rasch model with regards to its ability to maintain invariant item difficulty estimates when real (data that does not perfectly fit the Rasch model), polytomous scored data is used. The data used in this study comes from a university alumni questionnaire that was collected over a period of five years. The analysis tests for significant variation between (a) small samples taken from a larger sample, (b) a base sample and subsequent (longitudinal) samples and (c) variation over time with confounding variables. The confounding variables studied include (a) the gender of the respondent and (b) the respondent's type of major at the time of graduation. The study used three methods to assess variation: (a) the between-fit statistic, (b) confidence intervals around the mean of the estimates and (c) a general linear model. The general linear model used the person residual statistic from the Winsteps' person output file as a dependent variable with year, gender and type of major as independent variables. Results of the study support the invariant nature of the item difficulty estimates when polytomous data from the alumni questionnaire is used. The analysis found comparable results (within sampling error) for the between-fit statistics and the general linear model. The confidence interval method was limited in its usefulness due to small confidence bands and the limitation of the plots. The linear model offered the most valuable data in that it provides methods to not only detect the existence of variation but to assess the relative magnitude of the variation from different sources. Recommendations for future research include studies regarding the impact of sample size on the between-fit statistic and confidence intervals as well as the impact of large amounts of systematic missing data on the item parameter estimates.
"],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/1168","https://scholarsarchive.byu.edu/context/etd/article/2167/viewcontent/ETD_CISOPTR_1172.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["Rasch","Invariance","Polytomous","Rating Scale","Item Difficulties","Educational Psychology"],"dc:title":["Testing the Assumption of Sample Invariance of Item Difficulty Parameters in the Rasch Rating Scale Model"],"dc:type":["Dissertation"],"thesis:degree_name":["PhD"]},"updated_at":"2026-07-24T01:28:54Z"}