Christine E DeMars
Biographic Data
| ID | 9251555 |
|---|---|
| NAME | Christine E DeMars |
| GIVEN NAMES | Christine E |
| FAMILY NAME | DeMars |
| SIGNATURE | DEMARS C E |
| AFFILIATIONS | James Madison University, Harrisonburg, VA, USA |
| ORCID | 0000-0003-0050-3655 |
| VERIFIED | Yes |
| TOTAL WORKS | 14 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 14 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2001 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Treating Noneffortful Responses as Missing
This study investigates the treatment of rapid-guess (RG) responses as missing data within the context of the effort-moderated model. Through a series of illustrations, this study demonstrates that the effort-moderated model assumes missing at random (MAR) rather than missing completely at random (MCAR), explaining the conditions necessary for MAR. These examples show that RG responses, when treated as missing under the effort-moderated model, do…
Item Parameter Recovery
Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal maximum estimation. In this study, using sample sizes of 1,000 or smaller, not using priors often led to…
Revised Parallel Analysis With Nonnormal Ability and a Guessing Parameter
Previous work showing that revised parallel analysis can be effective with dichotomous items has used a two-parameter model and normally distributed abilities. In this study, both two- and three-parameter models were used with normally distributed and skewed ability distributions. Relatively minor skew and kurtosis in the underlying ability distribution had almost no effect on Type I error for unidimensional data and reduced power for two-dimensi…
Partially Compensatory Multidimensional Item Response Theory Models
Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were then estimated for both models and for the compensatory model. Either the model used to simulate the da…
The Interaction of Ability Differences and Guessing When Modeling Differential Item Functioning With the Rasch Model
In educational testing, differential item functioning (DIF) statistics must be accurately estimated to ensure the appropriate items are flagged for inspection or removal. This study showed how using the Rasch model to estimate DIF may introduce considerable bias in the results when there are large group differences in ability (impact) and the data follow a three-parameter logistic model. With large group ability differences, difficult non-DIF ite…
An Investigation of Sample Size Splitting on Atfind and Dimtest
Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for empirically deriving a subtest composed of items that potentially measure a second dimension versus DIMT…
Differential Item Functioning Detection With Latent Classes
There is a long history of differential item functioning (DIF) detection methods for known, manifest grouping variables, such as sex or ethnicity. But if the experiences or cognitive processes leading to DIF are not perfectly correlated with the manifest groups, it would be more informative to uncover the latent groups underlying DIF. The use of item response theory (IRT) mixture models to detect latent groups and estimate the DIF caused by these…
Item Response Theory
Item Response Theory describes the application of mathematical models to data from questionnaires and tests as a basis for measuring abilities, attitudes, or other variables. It is used for statistical analysis and development of assessments, often for high stakes tests such as the Graduate Record Examination.
Type I Error Inflation for Detecting DIF in the Presence of Impact
In this brief explication, two challenges for using differential item functioning (DIF) measures when there are large group differences in true proficiency are illustrated. Each of these difficulties may lead to inflated Type I error rates, for very different reasons. One problem is that groups matched on observed score are not necessarily well matched on true proficiency, which may result in the false detection of DIF due to inaccurate matching.…
Polytomous Differential Item Functioning and Violations of Ordering of the Expected Latent Trait by the Raw Score
The graded response (GR) and generalized partial credit (GPC) models do not imply that examinees ordered by raw observed score will necessarily be ordered on the expected value of the latent trait (OEL). Factors were manipulated to assess whether increased violations of OEL also produced increased Type I error rates in differential item functioning (DIF) procedures conditioned on the raw score. Shorter tests and greater variance in item slope par…
Guessing'' Parameter Estimates for Multidimensional Item Response Theory Models
Two software packages commonly used for multidimensional item response theory (IRT) models require the user to input values for the lower asymptotes of the item response functions. One way of selecting these values is to estimate lower asymptotes with a one-dimensional IRT model and use those estimates as fixed values in the multidimensional model. This procedure was compared to simply setting the asymptotes to a reasonable value. For two-factor …
Low Examinee Effort in Low-Stakes Assessment
Student test-taking motivation in low-stakes assessment testing is examined in terms of both its relationship to test performance and the implications of low student effort for test validity. A theoretical model of test-taking motivation is presented, with a synthesis of previous research indicating that low student motivation is associated with a substantial decrease in test performance. A number of assessment practices and data analytic procedu…
Type I Error Rates for Parscale’s Fit Index
Type I error rates for PARSCALE’s fit statistic were examined. Data were generated to fit the partial credit or graded response model, with test lengths of 10 or 20 items. The ability distribution was simulated to be either normal or uniform. Type I error rates were inflated for the shorter test length and, for the graded-response model, also for the longer test length when the ability distribution was uniform. In conditions in which α was inflat…
Group Differences Based on Irt Scores
In this study, effect sizes based on simulated groups were compared for the one-parameter and three-parameter logistic IRT models. Data were generated based on a three-parameter model, and item estimates were obtained from the simulated data based on both the one-parameter and three-parameter models. Abilities were estimated using both maximum likelihood and expected a posterior methods. The data fit the three-parameter model much better, but the…
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Group Differences Based on Irt Scores
In this study, effect sizes based on simulated groups were compared for the one-parameter and three-parameter logistic IRT models. Data were generated based on a three-parameter model, and item estimates were obtained from the simulated data based on both the one-parameter and three-parameter models. Abilities were estimated using both maximum likelihood and expected a posterior methods. The data fit the three-parameter model much better, but the…
Low Examinee Effort in Low-Stakes Assessment
Student test-taking motivation in low-stakes assessment testing is examined in terms of both its relationship to test performance and the implications of low student effort for test validity. A theoretical model of test-taking motivation is presented, with a synthesis of previous research indicating that low student motivation is associated with a substantial decrease in test performance. A number of assessment practices and data analytic procedu…
Type I Error Rates for Parscale’s Fit Index
Type I error rates for PARSCALE’s fit statistic were examined. Data were generated to fit the partial credit or graded response model, with test lengths of 10 or 20 items. The ability distribution was simulated to be either normal or uniform. Type I error rates were inflated for the shorter test length and, for the graded-response model, also for the longer test length when the ability distribution was uniform. In conditions in which α was inflat…
Guessing'' Parameter Estimates for Multidimensional Item Response Theory Models
Two software packages commonly used for multidimensional item response theory (IRT) models require the user to input values for the lower asymptotes of the item response functions. One way of selecting these values is to estimate lower asymptotes with a one-dimensional IRT model and use those estimates as fixed values in the multidimensional model. This procedure was compared to simply setting the asymptotes to a reasonable value. For two-factor …
Polytomous Differential Item Functioning and Violations of Ordering of the Expected Latent Trait by the Raw Score
The graded response (GR) and generalized partial credit (GPC) models do not imply that examinees ordered by raw observed score will necessarily be ordered on the expected value of the latent trait (OEL). Factors were manipulated to assess whether increased violations of OEL also produced increased Type I error rates in differential item functioning (DIF) procedures conditioned on the raw score. Shorter tests and greater variance in item slope par…
Item Response Theory
Item Response Theory describes the application of mathematical models to data from questionnaires and tests as a basis for measuring abilities, attitudes, or other variables. It is used for statistical analysis and development of assessments, often for high stakes tests such as the Graduate Record Examination.
Type I Error Inflation for Detecting DIF in the Presence of Impact
In this brief explication, two challenges for using differential item functioning (DIF) measures when there are large group differences in true proficiency are illustrated. Each of these difficulties may lead to inflated Type I error rates, for very different reasons. One problem is that groups matched on observed score are not necessarily well matched on true proficiency, which may result in the false detection of DIF due to inaccurate matching.…
Differential Item Functioning Detection With Latent Classes
There is a long history of differential item functioning (DIF) detection methods for known, manifest grouping variables, such as sex or ethnicity. But if the experiences or cognitive processes leading to DIF are not perfectly correlated with the manifest groups, it would be more informative to uncover the latent groups underlying DIF. The use of item response theory (IRT) mixture models to detect latent groups and estimate the DIF caused by these…
An Investigation of Sample Size Splitting on Atfind and Dimtest
Modeling multidimensional test data with a unidimensional model can result in serious statistical errors, such as bias in item parameter estimates. Many methods exist for assessing the dimensionality of a test. The current study focused on DIMTEST. Using simulated data, the effects of sample size splitting for use with the ATFIND procedure for empirically deriving a subtest composed of items that potentially measure a second dimension versus DIMT…
The Interaction of Ability Differences and Guessing When Modeling Differential Item Functioning With the Rasch Model
In educational testing, differential item functioning (DIF) statistics must be accurately estimated to ensure the appropriate items are flagged for inspection or removal. This study showed how using the Rasch model to estimate DIF may introduce considerable bias in the results when there are large group differences in ability (impact) and the data follow a three-parameter logistic model. With large group ability differences, difficult non-DIF ite…
Partially Compensatory Multidimensional Item Response Theory Models
Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were then estimated for both models and for the compensatory model. Either the model used to simulate the da…
Revised Parallel Analysis With Nonnormal Ability and a Guessing Parameter
Previous work showing that revised parallel analysis can be effective with dichotomous items has used a two-parameter model and normally distributed abilities. In this study, both two- and three-parameter models were used with normally distributed and skewed ability distributions. Relatively minor skew and kurtosis in the underlying ability distribution had almost no effect on Type I error for unidimensional data and reduced power for two-dimensi…
Item Parameter Recovery
Marginal maximum likelihood, a common estimation method for item response theory models, is not inherently a Bayesian procedure. However, due to estimation difficulties, Bayesian priors are often applied to the likelihood when estimating 3PL models, especially with small samples. Little focus has been placed on choosing the priors for marginal maximum estimation. In this study, using sample sizes of 1,000 or smaller, not using priors often led to…
Treating Noneffortful Responses as Missing
This study investigates the treatment of rapid-guess (RG) responses as missing data within the context of the effort-moderated model. Through a series of illustrations, this study demonstrates that the effort-moderated model assumes missing at random (MAR) rather than missing completely at random (MCAR), explaining the conditions necessary for MAR. These examples show that RG responses, when treated as missing under the effort-moderated model, do…
Psychometric Methodologies and Testing (13 works) · Mathematics (12 works) · Statistics (12 works) · Psychometrics (10 works) · Advanced Statistical Modeling Techniques (9 works) · Item response theory (9 works) · Econometrics (8 works) · Psychology (7 works) · Computer Science (6 works) · Cognitive Abilities and Testing (5 works)