{"product_id":"rasch-models-in-health-hardback-9781848212220","title":"Rasch Models in Health (Hardback) 9781848212220","description":"\u003cfont face=\"Georgia\"\u003e\r\n\u003cp\u003e\u003cfont size=\"6\"\u003eRasch Models in Health\u003c\/font\u003e\u003cbr\u003e\r\n\r\n\r\n\r\n\r\n\r\n\u003c\/p\u003e\n\u003cp\u003e\u003cfont size=\"4\"\u003eKarl Bang Christensen (Edited by), KB Christensen (Author), Svend Kreiner (Edited by), Mounir Mesbah (Edited by)\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e9781848212220, Wiley\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eHardback, published 14 December 2012\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e384 pages\u003cbr\u003e23.9 x 16.3 x 2.5 cm, 0.703 kg\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\r\n\u003cp align=\"justify\"\u003e\u003cem\u003e\u003cfont size=\"3\"\u003e\u003cp\u003e\"This book contains a comprehensive overview of the statistical theory of Rasch models.\" (\u003ci\u003eZentralblatt MATH\u003c\/i\u003e 2016)\u003c\/p\u003e\u003c\/font\u003e\u003c\/em\u003e\u003c\/p\u003e\r\n\r\n\u003cp align=\"justify\"\u003e\u003cstrong\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eThe family of statistical models known as Rasch models started with a simple model for responses to questions in educational tests presented together with a number of related models that the Danish mathematician Georg Rasch referred to as models for measurement. Since the beginning of the 1950s the use of Rasch models has grown and has spread from education to the measurement of health status. This book contains a comprehensive overview of the statistical theory of Rasch models.\u003c\/p\u003e \u003cp\u003ePart 1 contains the probabilistic definition of Rasch models, Part 2 describes the estimation of item and person parameters, Part 3 concerns the assessment of the data-model fit of Rasch models, Part 4 contains applications of Rasch models, Part 5 discusses how to develop health-related instruments for Rasch models, and Part 6 describes how to perform Rasch analysis and document results.\u003c\/p\u003e\u003c\/font\u003e\u003c\/strong\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003e\u003cp\u003eI Probabilistic models 1\u003c\/p\u003e \u003cp\u003e1 The Rasch model for dichotomous items 3\u003c\/p\u003e \u003cp\u003e1.1 Introduction 4\u003c\/p\u003e \u003cp\u003e1.1.1 original formulation of the model  4\u003c\/p\u003e \u003cp\u003e1.1.2 Modern formulations of the model  7\u003c\/p\u003e \u003cp\u003e1.2 Psychometric properties 8\u003c\/p\u003e \u003cp\u003e1.2.1 Requirements of IRT models 9\u003c\/p\u003e \u003cp\u003e1.2.2 Item Characteristic Curves 10\u003c\/p\u003e \u003cp\u003e1.2.3 Guttman errors 10\u003c\/p\u003e \u003cp\u003e1.2.4 Implicit assumptions  11\u003c\/p\u003e \u003cp\u003e1.3 Statistical properties 11\u003c\/p\u003e \u003cp\u003e1.3.1 The distribution of the total score  12\u003c\/p\u003e \u003cp\u003e1.3.2 Symmetrical polynomials 13\u003c\/p\u003e \u003cp\u003e1.3.3 Test characteristic curve (TCC)  14\u003c\/p\u003e \u003cp\u003e1.3.4 Partial credit model parametrization of the score distribution 14\u003c\/p\u003e \u003cp\u003e1.3.5 Rasch models for subscores 15\u003c\/p\u003e \u003cp\u003e1.4 Inference frames  15\u003c\/p\u003e \u003cp\u003e1.5 Specic objectivity 18\u003c\/p\u003e \u003cp\u003e1.6 Rasch models as graphical models 19\u003c\/p\u003e \u003cp\u003e1.7 Summary 20\u003c\/p\u003e \u003cp\u003e2 Rasch models for ordered polytomous items 25\u003c\/p\u003e \u003cp\u003e2.1 Introduction 26\u003c\/p\u003e \u003cp\u003e2.1.1 Example  26\u003c\/p\u003e \u003cp\u003e2.1.2 Ordered categories  26\u003c\/p\u003e \u003cp\u003e2.1.3 Properties of the Polytomous Rasch model   30\u003c\/p\u003e \u003cp\u003e2.1.4 Assumptions 32\u003c\/p\u003e \u003cp\u003e2.2 Derivation from the dichotomous model  32\u003c\/p\u003e \u003cp\u003e2.3 Distributions derived from Rasch models  37\u003c\/p\u003e \u003cp\u003e2.3.1 The score distribution  37\u003c\/p\u003e \u003cp\u003e2.3.2 Interpretation of thresholds in partial credit items and Rasch\u003c\/p\u003e \u003cp\u003escores  39\u003c\/p\u003e \u003cp\u003e2.3.3 Conditional distribution of item responses given the total score 39\u003c\/p\u003e \u003cp\u003e2.4 Conclusion 39\u003c\/p\u003e \u003cp\u003e2.4.1 Frames of inference for Rasch models    40\u003c\/p\u003e \u003cp\u003eII Inference in the Rasch model 45\u003c\/p\u003e \u003cp\u003e3 Estimation of item parameters 47\u003c\/p\u003e \u003cp\u003e3.1 Introduction 48\u003c\/p\u003e \u003cp\u003e3.2 Estimation of item parameters  50\u003c\/p\u003e \u003cp\u003e3.2.1 Estimation using the conditional likelihood function  50\u003c\/p\u003e \u003cp\u003e3.2.2 Pairwise conditional estimation  52\u003c\/p\u003e \u003cp\u003e3.2.3 Marginal likelihood function 54\u003c\/p\u003e \u003cp\u003e3.2.4 Extended likelihood function 55\u003c\/p\u003e \u003cp\u003e3.2.5 Reduced rank parametrization 56\u003c\/p\u003e \u003cp\u003e3.2.6 Parameter estimation in more general Rasch models  56\u003c\/p\u003e \u003cp\u003e4 Person parameter estimation and measurement in Rasch models 59\u003c\/p\u003e \u003cp\u003e4.1 Introduction and notation  60\u003c\/p\u003e \u003cp\u003e4.2 Maximum likelihood estimation of person parameters   61\u003c\/p\u003e \u003cp\u003e4.3 Item and test information functions 62\u003c\/p\u003e \u003cp\u003e4.4 Weighted likelihood estimation of person parameters   63\u003c\/p\u003e \u003cp\u003e4.5 Example 63\u003c\/p\u003e \u003cp\u003e4.6 Measurement quality 65\u003c\/p\u003e \u003cp\u003e4.6.1 Reliability in classical test theory  66\u003c\/p\u003e \u003cp\u003e4.6.2 Reliability in Rasch models 67\u003c\/p\u003e \u003cp\u003e4.6.3 Expected measurement precision  69\u003c\/p\u003e \u003cp\u003e4.6.4 Targeting  69\u003c\/p\u003e \u003cp\u003eIII Checking the Rasch model 75\u003c\/p\u003e \u003cp\u003e5 Itemt statistics 77\u003c\/p\u003e \u003cp\u003e5.1 Introduction 78\u003c\/p\u003e \u003cp\u003e5.2 Rasch model residuals 79\u003c\/p\u003e \u003cp\u003e5.2.1 Notation  79\u003c\/p\u003e \u003cp\u003e5.2.2 Individual response residuals: outts and ints   80\u003c\/p\u003e \u003cp\u003e5.2.3 Group residuals 85\u003c\/p\u003e \u003cp\u003e5.2.4 Group residuals for analysis of homogeneity   85\u003c\/p\u003e \u003cp\u003e5.3 Molenaar's U  87\u003c\/p\u003e \u003cp\u003e5.4 Analysis of item { restscore association  88\u003c\/p\u003e \u003cp\u003e5.5 Group residuals and analysis of DIF 89\u003c\/p\u003e \u003cp\u003e5.6 Kelderman's conditional likelihood ratio test of no DIF   90\u003c\/p\u003e \u003cp\u003e5.7 Test for conditional independence in three-way tables   92\u003c\/p\u003e \u003cp\u003e5.8 Discussion and recommendations 93\u003c\/p\u003e \u003cp\u003e5.8.1 Technical issues 93\u003c\/p\u003e \u003cp\u003e5.8.2 What to do when items do not agree with the Rasch model 95\u003c\/p\u003e \u003cp\u003e6 Over-all tests of the Rasch model 99\u003c\/p\u003e \u003cp\u003e6.1 Introduction 100\u003c\/p\u003e \u003cp\u003e6.2 The conditional likelihood ratio test 100\u003c\/p\u003e \u003cp\u003e6.3 Example: Diabetes and Eating habits 102\u003c\/p\u003e \u003cp\u003e6.4 Other over-all tests of t 104\u003c\/p\u003e \u003cp\u003e7 Local dependence 107\u003c\/p\u003e \u003cp\u003e7.1 Introduction 108\u003c\/p\u003e \u003cp\u003e7.1.1 Reduced rank parametrization model for sub tests  108\u003c\/p\u003e \u003cp\u003e7.1.2 Reliability indexes  109\u003c\/p\u003e \u003cp\u003e7.2 Local dependence in Rasch Models 109\u003c\/p\u003e \u003cp\u003e7.2.1 Response dependence  110\u003c\/p\u003e \u003cp\u003e7.3 E\u003c\/p\u003e \u003cp\u003eects of response dependence on measurement    111\u003c\/p\u003e \u003cp\u003e7.4 Diagnosing and detecting response dependence    114\u003c\/p\u003e \u003cp\u003e7.4.1 Item t  114\u003c\/p\u003e \u003cp\u003e7.4.2 Item residual correlations 116\u003c\/p\u003e \u003cp\u003e7.4.3 Sub tests and reliability 118\u003c\/p\u003e \u003cp\u003e7.4.4 Estimating the magnitude of response dependence  118\u003c\/p\u003e \u003cp\u003e7.4.5 Illustration 119\u003c\/p\u003e \u003cp\u003e7.5 Summary 124\u003c\/p\u003e \u003cp\u003e8 Two tests of local independence 131\u003c\/p\u003e \u003cp\u003e8.1 Introduction 132\u003c\/p\u003e \u003cp\u003e8.2 Kelderman's conditional likelihood ratio test of local independence 132\u003c\/p\u003e \u003cp\u003e8.3 Simple conditional independence tests 134\u003c\/p\u003e \u003cp\u003e8.4 Discussion and recommendations 136\u003c\/p\u003e \u003cp\u003e9 Dimensionality 139\u003c\/p\u003e \u003cp\u003e9.1 Introduction 140\u003c\/p\u003e \u003cp\u003e9.1.1 Background 140\u003c\/p\u003e \u003cp\u003e9.1.2 Multidimensionality in health outcome scales   141\u003c\/p\u003e \u003cp\u003e9.1.3 Consequences of multidimensionality    142\u003c\/p\u003e \u003cp\u003e9.1.4 Motivating example: the HADS data    142\u003c\/p\u003e \u003cp\u003e9.2 Multidimensional models 143\u003c\/p\u003e \u003cp\u003e9.2.1 Marginal likelihood function 144\u003c\/p\u003e \u003cp\u003e9.2.2 Conditional likelihood function  144\u003c\/p\u003e \u003cp\u003e9.3 Diagnostics for detection of multidimensionality    144\u003c\/p\u003e \u003cp\u003e9.3.1 Analysis of residuals  145\u003c\/p\u003e \u003cp\u003e9.3.2 Observed and expected counts 145\u003c\/p\u003e \u003cp\u003e9.3.3 Observed and expected correlations    147\u003c\/p\u003e \u003cp\u003e9.3.4 The t-test approach  148\u003c\/p\u003e \u003cp\u003e9.3.5 Using reliability estimates as diagnostics of multidimensionality 149\u003c\/p\u003e \u003cp\u003e9.3.6 Tests of unidimensionality 150\u003c\/p\u003e \u003cp\u003e9.4 Estimating the magnitude of multidimensionality   152\u003c\/p\u003e \u003cp\u003e9.5 Implementation  153\u003c\/p\u003e \u003cp\u003e9.6 Summary 153\u003c\/p\u003e \u003cp\u003eIV Applying the Rasch model 161\u003c\/p\u003e \u003cp\u003e10 The polytomous Rasch model and the equating of two instruments163\u003c\/p\u003e \u003cp\u003e10.1 Introduction 164\u003c\/p\u003e \u003cp\u003e10.2 The polytomous Rasch model  165\u003c\/p\u003e \u003cp\u003e10.2.1 Conditional probabilities 166\u003c\/p\u003e \u003cp\u003e10.2.2 Conditional estimates of the instrument parameters  167\u003c\/p\u003e \u003cp\u003e10.2.3 An illustrative small example 169\u003c\/p\u003e \u003cp\u003e10.3 Reparametrization of the thresholds 170\u003c\/p\u003e \u003cp\u003e10.3.1 Thresholds reparametrized to two parameters for each instrument170\u003c\/p\u003e \u003cp\u003e10.3.2 Thresholds reparametrized with more than two parameters 174\u003c\/p\u003e \u003cp\u003e10.3.3 A reparametrization with four parameters   174\u003c\/p\u003e \u003cp\u003e10.4 Tests of Fit 176\u003c\/p\u003e \u003cp\u003e10.4.1 The conditional test of fit based on cell frequencies  176\u003c\/p\u003e \u003cp\u003e10.4.2 The conditional test of fit based on class intervals  177\u003c\/p\u003e \u003cp\u003e10.4.3 Graphical test of fit based on total scores    178\u003c\/p\u003e \u003cp\u003e10.4.4 Graphical test of fit based on person estimates   179\u003c\/p\u003e \u003cp\u003e10.5 Equating procedures 179\u003c\/p\u003e \u003cp\u003e10.5.1 Equating using conditioning on total scores   180\u003c\/p\u003e \u003cp\u003e10.5.2 Equating through person estimates    180\u003c\/p\u003e \u003cp\u003e10.6 Example 180\u003c\/p\u003e \u003cp\u003e10.6.1 Person threshold distribution 182\u003c\/p\u003e \u003cp\u003e10.6.2 The test of \u003c\/p\u003e \u003cp\u003et between the data and the model   182\u003c\/p\u003e \u003cp\u003e10.6.3 Further analysis with the parametrization with two moments\u003c\/p\u003e \u003cp\u003efor each instrument  184\u003c\/p\u003e \u003cp\u003e10.6.4 Equated scores based on the parametrization with two moments\u003c\/p\u003e \u003cp\u003eof the thresholds 190\u003c\/p\u003e \u003cp\u003e10.7 Discussion 194\u003c\/p\u003e \u003cp\u003e11 A multidimensional latent class Rasch model for the assessment of\u003c\/p\u003e \u003cp\u003ethe Health-related Quality of Life 199\u003c\/p\u003e \u003cp\u003e11.1 Introduction 200\u003c\/p\u003e \u003cp\u003e11.2 The dataset 202\u003c\/p\u003e \u003cp\u003e11.3 The multidimensional latent class Rasch model    205\u003c\/p\u003e \u003cp\u003e11.3.1 Model assumptions  205\u003c\/p\u003e \u003cp\u003e11.3.2 Maximum likelihood estimation and model selection  208\u003c\/p\u003e \u003cp\u003e11.3.3 Software details 209\u003c\/p\u003e \u003cp\u003e11.3.4 Concluding remarks about the model    210\u003c\/p\u003e \u003cp\u003e11.4 Inference on the correlation between latent traits   211\u003c\/p\u003e \u003cp\u003e11.5 Application results 214\u003c\/p\u003e \u003cp\u003e12 Analysis of Rater Agreement by Rasch and IRT models 223\u003c\/p\u003e \u003cp\u003e12.1 Introduction 224\u003c\/p\u003e \u003cp\u003e12.2 An IRT model for modelling inter-rater agreement   224\u003c\/p\u003e \u003cp\u003e12.3 Umbilical artery Doppler velocimetry and perinatal mortality  226\u003c\/p\u003e \u003cp\u003e12.4 Quantifying the rater agreement in the Rasch model   227\u003c\/p\u003e \u003cp\u003e12.4.1 Fixed Effects Approach  227\u003c\/p\u003e \u003cp\u003e12.4.2 Random Effects approach and the median odds ratio  229\u003c\/p\u003e \u003cp\u003e12.5 Doppler velocimetry and perinatal mortality    231\u003c\/p\u003e \u003cp\u003e12.6 Quantifying the rater agreement in the IRT model   232\u003c\/p\u003e \u003cp\u003e12.7 Discussion 233\u003c\/p\u003e \u003cp\u003e13 From Measurement to Analysis: two steps or latent regression? 241\u003c\/p\u003e \u003cp\u003e13.1 Introduction 242\u003c\/p\u003e \u003cp\u003e13.2 Likelihood 243\u003c\/p\u003e \u003cp\u003e13.2.1 Two-step model 244\u003c\/p\u003e \u003cp\u003e13.2.2 Latent regression model 244\u003c\/p\u003e \u003cp\u003e13.3 First step: Measurement models 245\u003c\/p\u003e \u003cp\u003e13.4 Statistical Validation of Measurement Instrument   248\u003c\/p\u003e \u003cp\u003e13.5 Construction of Scores 251\u003c\/p\u003e \u003cp\u003e13.6 Two-step method to Analyze Change between Groups   253\u003c\/p\u003e \u003cp\u003e13.6.1 Health related Quality of Life and Housing in Europe  253\u003c\/p\u003e \u003cp\u003e13.6.2 Use of Surrogate in an Clinical Oncology trial   254\u003c\/p\u003e \u003cp\u003e13.7 Latent Regression to Analyze Change between Groups   257\u003c\/p\u003e \u003cp\u003e13.8 Conclusion 259\u003c\/p\u003e \u003cp\u003e14 Analysis with repeatedly measured binary item response data byad\u003c\/p\u003e \u003cp\u003ehoc Rasch scales 265\u003c\/p\u003e \u003cp\u003e14.1 Introduction 266\u003c\/p\u003e \u003cp\u003e14.2 The generalized multilevel Rasch model  268\u003c\/p\u003e \u003cp\u003e14.2.1 The multilevel form of the conventional Rasch model for binary\u003c\/p\u003e \u003cp\u003eitems 268\u003c\/p\u003e \u003cp\u003e14.2.2 Group comparison and repeated measurement   269\u003c\/p\u003e \u003cp\u003e14.2.3 Differential item functioning and local dependence  270\u003c\/p\u003e \u003cp\u003e14.3 The analysis of an ad hoc scale 272\u003c\/p\u003e \u003cp\u003e14.4 Simulation study  277\u003c\/p\u003e \u003cp\u003e14.5 Discussion 283\u003c\/p\u003e \u003cp\u003eV Creating, translating, improving Rasch scales 287\u003c\/p\u003e \u003cp\u003e15 Writing Health-Related Items for Rasch Models - Patient Reported\u003c\/p\u003e \u003cp\u003eOutcome Scales for Health Sciences: From Medical Paternalism to\u003c\/p\u003e \u003cp\u003ePatient Autonomy 289\u003c\/p\u003e \u003cp\u003e15.1 Introduction 290\u003c\/p\u003e \u003cp\u003e15.1.1 The emergence of the biopsychosocial model of illness  290\u003c\/p\u003e \u003cp\u003e15.1.2 Changes in the consultation process in general medicine  291\u003c\/p\u003e \u003cp\u003e15.2 The use of patient reported outcome questionnaires   292\u003c\/p\u003e \u003cp\u003e15.2.1 Defining PRO constructs 293\u003c\/p\u003e \u003cp\u003e15.2.2 Quality requirements for PRO questionnaires   298\u003c\/p\u003e \u003cp\u003e15.3 Writing new Health-Related Items for new PRO scales   301\u003c\/p\u003e \u003cp\u003e15.3.1 Consideration of measurement issues    302\u003c\/p\u003e \u003cp\u003e15.3.2 Questionnaire Development 302\u003c\/p\u003e \u003cp\u003e15.4 Selecting PROs for a clinical setting 305\u003c\/p\u003e \u003cp\u003e15.5 Conclusions 305\u003c\/p\u003e \u003cp\u003e16 Adapting patient-reported outcome measures for use in new lan-\u003c\/p\u003e \u003cp\u003eguages and cultures 313\u003c\/p\u003e \u003cp\u003e16.1 Introduction 314\u003c\/p\u003e \u003cp\u003e16.1.1 Background 314\u003c\/p\u003e \u003cp\u003e16.1.2 Aim of the adaptation process 315\u003c\/p\u003e \u003cp\u003e16.2 Suitability for adaptation 315\u003c\/p\u003e \u003cp\u003e16.3 Translation Process 315\u003c\/p\u003e \u003cp\u003e16.3.1 Linguistic Issues 316\u003c\/p\u003e \u003cp\u003e16.3.2 Conceptual Issues 316\u003c\/p\u003e \u003cp\u003e16.3.3 Technical Issues 316\u003c\/p\u003e \u003cp\u003e16.4 Translation Methodology 317\u003c\/p\u003e \u003cp\u003e16.4.1 Forward-backward translation 317\u003c\/p\u003e \u003cp\u003e16.5 Dual-Panel translation 318\u003c\/p\u003e \u003cp\u003e16.6 Assessment of psychometric and scaling properties   320\u003c\/p\u003e \u003cp\u003e16.6.1 Cognitive debriefing interviews  320\u003c\/p\u003e \u003cp\u003e16.6.2 Determining the psychometric properties of the new language\u003c\/p\u003e \u003cp\u003eversion of the measure  322\u003c\/p\u003e \u003cp\u003e16.6.3 Practice Guidelines  323\u003c\/p\u003e \u003cp\u003e17 Improving items that do not fit the Rasch model 329\u003c\/p\u003e \u003cp\u003e17.1 Introduction 330\u003c\/p\u003e \u003cp\u003e17.2 The Rasch model and the graphical log linear Rasch model  330\u003c\/p\u003e \u003cp\u003e17.3 The scale improvement strategy 332\u003c\/p\u003e \u003cp\u003e17.3.1 Choice of modificational action  335\u003c\/p\u003e \u003cp\u003e17.3.2 Result of applying the scale improvement strategy  339\u003c\/p\u003e \u003cp\u003e17.4 Application of the strategy to the Physical Functioning Scale of the\u003c\/p\u003e \u003cp\u003eSF-36 340\u003c\/p\u003e \u003cp\u003e17.4.1 Results of the GLLRM  340\u003c\/p\u003e \u003cp\u003e17.4.2 Results of the subject matter analysis    341\u003c\/p\u003e \u003cp\u003e17.4.3 Suggestions according to the strategy    342\u003c\/p\u003e \u003cp\u003e17.5 Closing remark  345\u003c\/p\u003e \u003cp\u003eVI Analyzing and reporting Rasch models 349\u003c\/p\u003e \u003cp\u003e18 Software and program for Rasch Analysis 351\u003c\/p\u003e \u003cp\u003e18.1 Introduction 352\u003c\/p\u003e \u003cp\u003e18.2 Stand alone softwares packages 352\u003c\/p\u003e \u003cp\u003e18.2.1 WINSTEPS 352\u003c\/p\u003e \u003cp\u003e18.2.2 RUMM  353\u003c\/p\u003e \u003cp\u003e18.2.3 Conquest  353\u003c\/p\u003e \u003cp\u003e18.2.4 DIGRAM  354\u003c\/p\u003e \u003cp\u003e18.3 Implementations in standard software 355\u003c\/p\u003e \u003cp\u003e18.3.1 SAS macro for MML estimation: %ANAQOL   355\u003c\/p\u003e \u003cp\u003e18.3.2 SAS Macros based on CML 356\u003c\/p\u003e \u003cp\u003e18.3.3 eRm : an R Package  356\u003c\/p\u003e \u003cp\u003e18.4 Fitting the Rasch model in SAS 356\u003c\/p\u003e \u003cp\u003e18.4.1 Simulation of Rasch dichotomous items    356\u003c\/p\u003e \u003cp\u003e18.4.2 MML Estimation of Rasch parameters using Proc NLMIXED 357\u003c\/p\u003e \u003cp\u003e18.4.3 MML Estimation of Rasch parameters using Proc GLIMMIX 358\u003c\/p\u003e \u003cp\u003e18.4.4 CML Estimation of Rasch parameters using Proc GENMOD 358\u003c\/p\u003e \u003cp\u003e18.4.5 JML Estimation of Rasch parameters using Proc LOGISTIC 359\u003c\/p\u003e \u003cp\u003e18.4.6 Loglinear Rasch model Estimation of Rasch parameters using\u003c\/p\u003e \u003cp\u003eProc Logistic 360\u003c\/p\u003e \u003cp\u003e18.4.7 Results  360\u003c\/p\u003e \u003cp\u003e19 Reporting a Rasch analysis 363\u003c\/p\u003e \u003cp\u003e19.1 Introduction 364\u003c\/p\u003e \u003cp\u003e19.1.1 Objectives  364\u003c\/p\u003e \u003cp\u003e19.1.2 Factors impacting a Rasch analysis report   364\u003c\/p\u003e \u003cp\u003e19.1.3 The role of the substantive theory of the latent variable  366\u003c\/p\u003e \u003cp\u003e19.1.4 The frame of reference  367\u003c\/p\u003e \u003cp\u003e19.2 Suggested Elements 367\u003c\/p\u003e \u003cp\u003e19.2.1 Construct: definition and operationalisation of the latent variable367\u003c\/p\u003e \u003cp\u003e19.2.2 Response format and scoring 368\u003c\/p\u003e \u003cp\u003e19.2.3 Sample and sampling design 368\u003c\/p\u003e \u003cp\u003e19.2.4 Data 369\u003c\/p\u003e \u003cp\u003e19.2.5 Measurement model and technical aspects   370\u003c\/p\u003e \u003cp\u003e19.2.6 Fit analysis 370\u003c\/p\u003e \u003cp\u003e19.2.7 Response scale suitability 371\u003c\/p\u003e \u003cp\u003e19.2.8 Item fit assessment  372\u003c\/p\u003e \u003cp\u003e19.2.9 Person fit assessment  372\u003c\/p\u003e \u003cp\u003e19.2.10 Information 373\u003c\/p\u003e \u003cp\u003e19.2.11Validated scale 374\u003c\/p\u003e \u003cp\u003e19.2.12 Application and usefulness 375\u003c\/p\u003e \u003cp\u003e19.2.13Further issues 376\u003c\/p\u003e\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\u003cp\u003e\u003cfont size=\"3\"\u003eSubject Areas: Mathematics [\u003ca title=\"See our other books on Mathematics\" href=\"https:\/\/freshlyprintedbooks.co.uk\/search?q=%22Mathematics%20%5BPB%5D%22\"\u003ePB\u003c\/a\u003e]\u003c\/font\u003e\u003c\/p\u003e\r\n\r\n\r\n\u003c\/font\u003e","brand":"Wiley-ISTE","offers":[{"title":"Brand 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