Rasch's Logistic Model Applied to Growth.

Rasch's logistic model for growth is explained by reviewing his analysis of piglet growth. An early formulation was given in India when Rasch visited with Rao to describe the statistic metameter as the distinguishing characteristic for determining the rate of growth. Next, several examples are given demonstrating growth with plots of growth over time using characteristic time with truncated data. The results of these growth plots and analyses are summarized given their implications and restraints for using this approach in determining rate of growth.

Loevinger on Unidimensional Tests with Reference to Guttman, Rasch, and Wright.

Loevinger's specifications for a unidimensional test are discussed. The implications are reviewed using commentary from Guttman's and Rasch's specification for specific objectivity. A large population is sampled to evaluate the implications of this approach in light of Wright's early presentation regarding data analysis. The results of this analysis show the sample follows the specifications of Loevinger and those of Rasch for a unidimensional test.

Rasch modeling and confirmatory factor analysis of the systemizing quotient-revised (SQ-R) scale.

This study assessed the dimensionality of the Systemizing Quotient-Revised (SQ-R), a measure of how strong a person's interest is in systems, using two statistical approaches: Rasch modeling and Confirmatory Factor Analysis (CFA). Participants included N = 675 with an autism spectrum condition (ASC), N = 1369 family members of people with ASC, and N = 2014 typical controls. Data were applied to the Rasch model (Rating Scale) using WINSTEPS. The data fit the Rasch model quite well lending support to the idea that systemizing could be seen as unidimensional. Reliability estimates were .99 for items and .92 for persons. A CFA parceling approach confirmed that a unidimensional model fit the data. There was, however, differential functioning by sex in some of these items. An abbreviated 44-item version of the scale, consisting of items without differential item functioning by sex was developed. This shorter scale also was tested from a Rasch perspective and confirmed through CFA. All measures showed differences on total scale scores between those participants with and without ASC (d = 0.71, p < .005), and between sexes (d = 0.53, p < .005). We conclude that the SQ-R is an appropriate measure of systemizing which can be measured along a single dimension.

Rasch modeling and confirmatory factor analysis of the systemizing quotient-revised (SQ-R) scale

This study assessed the dimensionality of the Systemizing Quotient-Revised (SQ-R), a measure of how strong a person's interest is in systems, using two statistical approaches: Rasch modeling and Confirmatory Factor Analysis (CFA). Participants included N = 675 with an autism spectrum condition (ASC), N = 1369 family members of people with ASC, and N = 2014 typical controls. Data were applied to the Rasch model (Rating Scale) using WINSTEPS. The data fit the Rasch model quite well lending support to the idea that systemizing could be seen as unidimensional. Reliability estimates were .99 for items and .92 for persons. A CFA parceling approach confirmed that a unidimensional model fit the data. There was, however, differential functioning by sex in some of these items. An abbreviated 44-item version of the scale, consisting of items without differential item functioning by sex was developed. This shorter scale also was tested from a Rasch perspective and confirmed through CFA. All measures showed differences on total scale scores between those participants with and without ASC (d = 0.71, p < .005), and between sexes (d = 0.53, p < .005). We conclude that the SQ-R is an appropriate measure of systemizing which can be measured along a single dimension (AU)

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Comparison is key.

Several concepts from Georg Rasch's last papers are discussed. The key one is comparison because Rasch considered the method of comparison fundamental to science. From the role of comparison stems scientific inference made operational by a properly developed frame of reference producing specific objectivity. The exact specifications Rasch outlined for making comparisons are explicated from quotes, and the role of causality derived from making comparisons is also examined. Understanding causality has implications for what can and cannot be produced via Rasch measurement. His simple examples were instructive, but the implications are far reaching upon first establishing the key role of comparison.

Causal Rasch models.

Rasch's unidimensional models for measurement show how to connect object measures (e.g., reader abilities), measurement mechanisms (e.g., machine-generated cloze reading items), and observational outcomes (e.g., counts correct on reading instruments). Substantive theory shows what interventions or manipulations to the measurement mechanism can be traded off against a change to the object measure to hold the observed outcome constant. A Rasch model integrated with a substantive theory dictates the form and substance of permissible interventions. Rasch analysis, absent construct theory and an associated specification equation, is a black box in which understanding may be more illusory than not. Finally, the quantitative hypothesis can be tested by comparing theory-based trade-off relations with observed trade-off relations. Only quantitative variables (as measured) support such trade-offs. Note that to test the quantitative hypothesis requires more than manipulation of the algebraic equivalencies in the Rasch model or descriptively fitting data to the model. A causal Rasch model involves experimental intervention/manipulation on either reader ability or text complexity or a conjoint intervention on both simultaneously to yield a successful prediction of the resultant observed outcome (count correct). We conjecture that when this type of manipulation is introduced for individual reader text encounters and model predictions are consistent with observations, the quantitative hypothesis is sustained.

The efficacy of adult christian support groups in coping with the death of a significant loved one.

Psychologists sometimes minimize important resources such as religion and spiritual beliefs for coping with bereavement. Alienation of therapeutic psychology from religious values contrasts to professional and public interest in religious experience and commitment. A supportive viewpoint has come about partially as a result of recognizing important values which clinicians have found absent in many of their clients. Until spiritual belief systems become integrated into the work of clinicians, clients may not be fully integrative in coping with loss. The key finding of this study was that individuals who participated in Christian and secular support groups showed no statistically significant difference in their mean endorsement of negative criteria on the BHS, and no statistically significant difference for their mean score endorsement of positive criteria on the RCOPE. However, a Christian-oriented approach was no less effective than a psychological-oriented one. In both groups, a spiritual connection to a specific or generalized higher power was frequently identified which clients ascribed to facilitating the management of their coping.

Fisher's information function and Rasch measurement.

Fisher's information function is reviewed with respect to an example he used for explication. A contemporary example continues the discussion with application to a rating scale instrument. The relationship of information to precision and measurement error is presented and discussed with respect to the analysis of fit. Targeting the instrument and the best test design for measuring a person with respect to information and item-person fit is discussed. The idealization of information and precision for making measures appears most effectively realized when computerized assisted testing can be employed to implement a best test design.

Substantive scale construction.

Variable construction requires careful attention to substantive issues; a theory guiding its development, a hierarchy of illustrative items constructed to define the variable, the subsequent production of item difficulties and person measures, and the analysis of fit. Rasch measurement practitioners should give careful attention to these matters so practical suggestions are given for designing variables based on theory, item construction, and Rasch models for the analysis of data. Variable maps are emphasized to guide variable construction and interpret the results.