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lindsayevanslee

whomds:Calculate Results from WHO Model Disability Survey Data

The Model Disability Survey (MDS) <https://www.who.int/activities/collection-of-data-on-disability> is a World Health Organization (WHO) general population survey instrument to assess the distribution of disability within a country or region, grounded in the International Classification of Functioning, Disability and Health <https://www.who.int/standards/classifications/international-classification-of-functioning-disability-and-health>. This package provides fit-for-purpose functions for calculating and presenting the results from this survey, as used by the WHO. The package primarily provides functions for implementing Rasch Analysis (see Andrich (2011) <doi:10.1586/erp.11.59>) to calculate a metric scale for disability.

Maintained by Lindsay Lee. Last updated 2 years ago.

4 stars 5.56 score 15 scripts

chrgrebe

exhaustiveRasch:Item Selection and Exhaustive Search for Rasch Models

Automation of the item selection processes for Rasch scales by means of exhaustive search for suitable Rasch models (dichotomous, partial credit, rating-scale) in a list of item-combinations. The item-combinations to test can be either all possible combinations or item-combinations can be defined by several rules (forced inclusion of specific items, exclusion of combinations, minimum/maximum items of a subset of items). Tests for model fit and item fit include ordering of the thresholds, item fit-indices, likelihood ratio test, Martin-Löf test, Wald-like test, person-item distribution, person separation index, principal components of Rasch residuals, empirical representation of all raw scores or Rasch trees for detecting differential item functioning. The tests, their ordering and their parameters can be defined by the user. For parameter estimation and model tests, functions of the packages 'eRm', 'psychotools' or 'pairwise' can be used.

Maintained by Christian Grebe. Last updated 4 months ago.

4.48 score 8 scripts

cran

tcl:Testing in Conditional Likelihood Context

An implementation of hypothesis testing in an extended Rasch modeling framework, including sample size planning procedures and power computations. Provides 4 statistical tests, i.e., gradient test (GR), likelihood ratio test (LR), Rao score or Lagrange multiplier test (RS), and Wald test, for testing a number of hypotheses referring to the Rasch model (RM), linear logistic test model (LLTM), rating scale model (RSM), and partial credit model (PCM). Three types of functions for power and sample size computations are provided. Firstly, functions to compute the sample size given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha, and the power of the test. Secondly, functions to evaluate the power of the tests given a user-specified (predetermined) deviation from the hypothesis to be tested, the level alpha of the test, and the sample size. Thirdly, functions to evaluate the so-called post hoc power of the tests. This is the power of the tests given the observed deviation of the data from the hypothesis to be tested and a user-specified level alpha of the test. Power and sample size computations are based on a Monte Carlo simulation approach. It is computationally very efficient. The variance of the random error in computing power and sample size arising from the simulation approach is analytically derived by using the delta method. Draxler, C., & Alexandrowicz, R. W. (2015), <doi:10.1007/s11336-015-9472-y>.

Maintained by Clemens Draxler. Last updated 7 months ago.

3.00 score

muellermarianne

iarm:Item Analysis in Rasch Models

Tools to assess model fit and identify misfitting items for Rasch models (RM) and partial credit models (PCM). Included are item fit statistics, item characteristic curves, item-restscore association, conditional likelihood ratio tests, assessment of measurement error, estimates of the reliability and test targeting as described in Christensen et al. (Eds.) (2013, ISBN:978-1-84821-222-0).

Maintained by Marianne Mueller. Last updated 3 years ago.

3 stars 2.97 score 31 scripts

cran

mstDIF:A Collection of DIF Tests for Multistage Tests

A collection of statistical tests for the detection of differential item functioning (DIF) in multistage tests. Methods entail logistic regression, an adaptation of the simultaneous item bias test (SIBTEST), and various score-based tests. The presented tests provide itemwise test for DIF along categorical, ordinal or metric covariates. Methods for uniform and non-uniform DIF effects are available depending on which method is used.

Maintained by Rudolf Debelak. Last updated 7 months ago.

2.00 score

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