Holdings: Statistical methods for the evaluation of educational services and quality of products

Statistical methods for the evaluation of educational services and quality of products:

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Bibliographic Details
Format:	Book
Language:	English
Published:	Heidelberg Physica-Verl. 2009
Series:	Contributions to statistics
Subjects:	Educational evaluation / Statistical methods Educational indicators Bildungsökonomik Statistische Methode Theorie Educational evaluation > Statistical methods Evaluation Pädagogik Statistik
Online Access:	Inhaltsverzeichnis
Physical Description:	XV, 243 S. graph. Darst.

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adam_text	CONTENTS 1 INTRODUCTION . MATILDE BINI, PAOLA MONARI, DOMENICO PICCOLO AND LUIGI SALMASO 1.1 GENERALIZED LINEAR LATENT VARIABLE MODELS 1 1.2 MULTILEVEL MODELS :. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.2.1 MULTILEVEL MIXTURE FACTOR MODELS..................... 6 1.3 CHOICES AND CONJOINT ANALYSIS: CRITICAL ASPECTS AND RECENT DEVELOPMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.4 ROBUST DIAGNOSTIC ANALYSIS WITH FORWARD SEARCH 8 1.5 NONPARAMETRIC COMBINATION OF DEPENDENT PERMUTATION TESTS AND RANKINGS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 10 1.5.1 INTRODUCTION TO PERMUTATION TESTS 11 1.5.2 MULTIVARIATE PERMUTATION TESTS AND NONPARAMETRIC COMBINATION METHODOLOGY 12 1.5.3 NONPARAMETRIC COMBINATION OF DEPENDENT RANKINGS 14 2 LATENT VARIABLE MODELS FOR ORDINAL DATA . . . . . . . . . . . . . . . . . . . . . . . .. 17 SILVIA CAGNONE, STEFANIA MIGNANI AND IRINI MOUSTAKI 2.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 17 2.2 THE GLLVM FOR ORDINAL DATA. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 18 2.2.1 MODEL SPECIFICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 18 2.2.2 MODEL ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 19 2.3 THE GOODNESS-OF-FIT OF THE MODEL 21 2.3.1 THE PROBLEM OF SPARSENESS . . . . . . . . . . . . . . . . . . . . . . . . .. 21 2.3.2 AN OVERALL GOODNESS-OF-FIT TEST. . . . . . . . . . . . . . . . . . . . . .. 23 2.4 GLLVM FOR LONGITUDINAL ORDINAL DATA. . . . . . . . . . . . . . . . . . . . . . . .. 24 2.5 CASE STUDY: PERCEPTIONS OF PREJUDICE ON AMERICAN CAMPUS . . . . . .. 25 2.6 CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 28 3 ISSUES ON ITEM RESPONSE THEORY MODELLING. . . . . . . . . . . . . . . . . . . . . . .. 29 MARIAGIULIA MATTEUCCI, STEFANIA MIGNANI AND BEMARD P. VELDKAMP 3.1 INTRODUCTION , . . . . . . . . . . . . . . . . . . . . .. 29 VII VIII CONTENTS 3.2 BASICS OF ITEM RESPONSE THEORY 30 3.2.1 PARAMETER ESTIMATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 32 3.3 ADVANCES IN IRT: SOME ISSUES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 36 3.3.1 MULTIDIMENSIONALITY. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 37 3.3.2 INCOMPLETE DESIGN. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 40 3.3.3 INC1USIONOF PRIOR INFORMATION . . . . . . . . . . . . . . . . . . . . . .. 41 3.4 CASE STUDY: PRIOR INFORMATION IN EDUCATIONAL ASSESSMENT . . . . . . . .. 43 3.5 CONC1UDINGREMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 44 4 NONLINEARITY IN THE ANALYSIS OF LONGITUDINAL DATA . . . . . . . . . . . . . . . . .. 47 ESTELA BEE DAGUM, SILVIA BIANCONCINI AND PAOLA MONARI 4.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 47 4.2 LATENT CURVE MODELS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 48 4.2.1 ESTIMATION. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 50 4.3 MODELLING NONLINEARITY IN LATENT CURVE MODELS 50 4.3.1 POLYNOMIAL TRAJECTORIES . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 51 4.3.2 EXPONENTIAL TRAJECTORIES 52 4.3.3 COMPLEX NONLINEAR CURVES ; . . . . . . . . . . . . .. 53 4.4 CASE STUDY: ANALYSIS OF UNIVERSITY STUDENT ACHIEVEMENTS 55 4.4.1 THE DATA ... . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 55 4.4.2 RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 57 4.5 CONC1UDINGREMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 60 5 MULTILEVEL MODELS FOR THE EVALUATION OF EDUCATIONAL INSTITUTIONS: A REVIEW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 61 LEONARDO GRILLI AND CARLA RAMPICHINI 5.1 THE EVALUATION OF EDUCATIONAL INSTITUTIONS ... . . . . . . . . . . . . . . . . .. 61 5.2 EFFECTIVENESS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 62 5.2.1 THE VALUE-ADDED APPROACH. . . . . . . . . . . . . . . . . . . . . . . . .. 64 5.2.2 TYPE A AND TYPE B EFFECTIVENESS . . . . . . . . . . . . . . . . . . . .. 64 5.3 MULTILEVEL MODELS AS A TOOL FOR MEASURING EFFECTIVENESS 65 5.3.1 THE RANDOM INTERCEPT MODEL 66 5.3.2 THE RANDOM SLOPE MODEL 69 5.3.3 CROSS-LEVEL INTERACTIONS . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 69 5.3.4 FIXED VERSUS RANDOM EFFECTS. . . . . . . . . . . . . . . . . . . . . . . .. 70 5.3.5 NON-LINEAR AND MULTIVARIATE MULTILEVEL MODELS 71 5.3.6 MULTILEVEL MODELS FOR NON-HIERARCHICAL STRUCTURES . . . . . .. 72 5.4 ISSUES IN MODEL SPECIFICATION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 72 5.4.1 SIMPLE VERSUS COMPLEX MODELS . . . . . . . . . . . . . . . . . . . . .. 72 5.4.2 TO ADJUST OR NOT TO ADJUST? 73 5.4.3 ENDOGENEITY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 74 5.4.4 MODELLING THE ACHIEVEMENT PROGRESS. . . . . . . . . . . . . . . . .. 75 5.4.5 MEASUREMENT ERROR .... . . . . . . . . . . . . . . . . . . . . . . . . . . .. 76 5.5 USE OF THE MODEL RESULTS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 77 5.5.1 RANKING THE SCHOOLS 77 CONTENTS IX 5.5.2 PREDICTING THE OUTCOME . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79 5.6 CONCLUDING REMARKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 79 6 MULTILEVEL MIXTURE FACTOR MODELS FOR THE EVALUATION OF EDUCATIONAL PROGRAMS EFFECTIVENESS 81 ROBERTA VARRIA1EAND CATERINA GIUSTI 6.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 81 6.2 THE MULTILEVEL MIXTURE FACTOR MODEL. . . . . . . . . . . . . . . . . . . . . . . . .. 82 6.3 THE GENERA1IZEDLATENT VARIABLE FRAMEWORK . . . . . . . . . . . . . . . . . .. 85 6.4 LIKELIHOOD, ESTIMATION AND POSTERIOR ANALYSIS . . . . . . . . . . . . . . . . .. 87 6.5 MODEL SELECTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 90 6.6 CASE STUDY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 91 7 A DASS OF STATISTICAL MODELS FOR EVALUATING SERVICES AND PERFORMANCES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 99 MARCELLA CORDUAS, MARIA IANNARIO AND DOMENICO PICCOLO 7.1 INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 99 7.2 UNOBSERVED COMPONENTS IN THE EVALUATION PROCESS 100 7.2.1 RATIONALE FOR A NEW CLASS OF MODELS 101 7.3 SPECIFICATION AND PROPERTIES OF CUB MODELS 102 7.4 INFERENTIAL ISSUES AND NUMERICA1PROCEDURES 106 7.4.1 THE EM A1GORITHM 106 7.4.2 FITTING MEASURES 109 7.5 FIELDS OF APPLICATION 110 7.6 FURTHER DEVELOPMENTS: A CLUSTERING APPROACH 111 7.7 CASE STUDY 112 7.8 CONCLUDING REMARKS 117 8 CHOICES AND CONJOINT ANALYSIS: CRITICAL ASPECTS AND RECENT DEVELOPMENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 119 ROSSELLA BEMI AND RICCARDO RIVELLO 8.1 INTRODUCTION 119 8.2 LITERATURE REVIEW 120 8.2.1 CHOICE EXPERIMENT: THEORY AND ADVANCES 123 8.2.2 CONJOINT ANALYSIS: THEORY AND ADVANCES 128 8.3 OUR PROPOSAL: CONJOINT ANALYSIS AND RESPONSE SURFACE METHODO1OGY 130 8.3.1 THE OUTLINED THEORY 130 8.3.2 THE SEARCHING OF THE BEST PROFILE THROUGH OPTIMIZATION 132 8.4 CASE STUDY 133 8.4.1 OPTIMIZATION RESULTS 134 8.5 CONCLUDING REMARKS 137 9 ROBUST DIAGNOSTICS IN UNIVERSITY PERFORMANCE STUDIES 139 MATILDE BINI, BRUNO BERTACCINI AND SILVIA BACCI 9.1 INTRODUCTION 139 9.2 ROBUST METHODS VS DIAGNOSTIC ANALYSIS 142 X CONTENTS 9.2.1 THE FORWARD SEARCH ALGORITHM 145 9.3 THE FORWARD SEARCH FOR GENERALIZED LINEAR MODELS 146 9.3.1 ROBUST GLMS FOR THE UNIVERSITY EFFECTIVENESS EVALUATION. THE CASE OF THE FIRST YEAR COLLEGE DROP OUT RATE 147 9.4 THE FORWARD SEARCH FOR ANOVAMODELS 153 9.4.1 THE FORWARD SEARCH FOR THE FIXED EFFECTS ANOVA 154 9.4.2 THE FORWARD SEARCH FOR THE RANDOM EFFECTS ANOVA 156 9.4.3 THE USE OF THE ROBUST ANOVAFOR THE EVALUATION OF THE LTALIAN UNIVERSITY REFORM 158 9.5 CONCLUDING REMARKS 159 10 A NOVEL GLOBAL PERFORMANCE SCORE WITH AN APPLICATION TO THE EVALUATION OF NEW DETERGENTS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. 161 STEFANO BONNINI, LIVIO CORAIN, ANTONIO CORDELLINA, ANNA CRESTANA, REMIGIO MUSCI AND LUIGI SALMASO 10.1 INTRODUCTION 161 10.2 COMPOSITE INDEXES 163 10.2.1 STANDARDIZATION: DATA TRANSFORMATIONS TO OBTAIN HOMOGENEOUS VARIABLES 163 10.2.2 AGGREGATION: SYNTHESIS OF INFORMATION 165 10.3 GLOBAL PERFORMANCE SCORE 167 10.3.1 GLOBAL SCORE ON PRIMARY PERFORMANCE 167 10.3.2 GLOBAL SCORE ON SECONDARY PERFORMANCE 169 10.3.3 AGGREGATION OF GSP AND GSS 170 10.4 CASE STUDY: COMPARATIVE PERFORMANCE EVALUATIONS OF NEW DETERGENTS 170 10.5 A COMPARATIVE SIMULATION STUDY 173 10.6 CONCLUSIONS 176 11 NONPARAMETRIC TESTS FOR THE RANDOMIZED COMPLETE BLOCK DESIGN WITH ORDERED CATEGORICAL VARIABLES 181 LIVIO CORAIN AND LUIGI SALMASO 11.1 INTRODUCTION 181 11.2 OVERVIEW ON PROCEDURES PROPOSED IN THE LITERATURE FOR THE RCB DESIGN 182 11.3 PERMUTATION TESTS FOR MULTIVARIATE RCB DESIGN 185 11.4 SIMULATION STUDY 188 11.5 CASE STUDY 190 11.6 CONCLUSIONS 192 12 A PERMUTATION TEST FOR UMBRELLA ALTERNATIVES. . . . . . . . . . . . . . . . . . . .. 193 DARIO BASSO, FORTUNATO PESARIN AND LUIGI SALMASO 12.1 INTRODUCTION 193 12.2 SIMPLE STOCHASTIC ORDERING ALTERNATIVES 196 12.3 PERMUTATION TEST FOR UMBRELLA ALTERNATIVES 199 12.4 SIMULATION STUDY 201 12.5 CASE STUDY: GRADUATES IN ENGINEERING 204 CONTENTS XI 13 NONPARAMETRIE METHODS FOR MEASURING CONCORDANCE BETWEEN RANKINGS: A CASE STUDY ON THE EVALUATION OF PROFESSIONAL PROFILES OF MUNICIPAL DIRECTORS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 ROSA ARBORETTI GIANCRISTOFARO, MARIO BOLZAN AND LIVIO CORAIN 13.1 INTRODUCTION 209 13.2 SAMPIE SURVEY OF MUNICIPAL DIRECTORS PROFESSIONAL PROFILES 210 13.2.1 CONTEXT OF THE EVALUATION OF THE ROLE OF COMMUNES AND OF MUNICIPAL DIRECTORS 210 13.2.2 SAMPIE SURVEY AMONG COMMUNES OFTHE VENETO 211 13.3 ANALYSIS OF CONCORDANCE BETWEEN RANKINGS 212 13.3.1 THE CONSTRUCTION OFRANKS 212 13.3.2 HYPOTHESIS TESTING ON CONCORDANCE BETWEEN RANKINGS 213 13.3.3 CLOSED TESTING PROCEDURE 216 13.3.4 RANKINGS OF THE MUNICIPAL DIRECTOR S PROFESSIONAL PROFILE IN THE COMMUNES OF THE VENETOSURVEY 219 13.3.5 DISCUSSION 220 REFERENCES 227 INDEX 241
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spelling	Statistical methods for the evaluation of educational services and quality of products Mathilde Bini ... Ed. Heidelberg Physica-Verl. 2009 XV, 243 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Contributions to statistics Educational evaluation / Statistical methods Educational indicators Bildungsökonomik stw Statistische Methode stw Theorie stw Educational evaluation Statistical methods Evaluation (DE-588)4071034-8 gnd rswk-swf Pädagogik (DE-588)4044302-4 gnd rswk-swf Statistik (DE-588)4056995-0 gnd rswk-swf Pädagogik (DE-588)4044302-4 s Evaluation (DE-588)4071034-8 s Statistik (DE-588)4056995-0 s DE-604 Bini, Matilde Sonstige oth Digitalisierung UB Erlangen application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018865330&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis
spellingShingle	Statistical methods for the evaluation of educational services and quality of products Educational evaluation / Statistical methods Educational indicators Bildungsökonomik stw Statistische Methode stw Theorie stw Educational evaluation Statistical methods Evaluation (DE-588)4071034-8 gnd Pädagogik (DE-588)4044302-4 gnd Statistik (DE-588)4056995-0 gnd
subject_GND	(DE-588)4071034-8 (DE-588)4044302-4 (DE-588)4056995-0
title	Statistical methods for the evaluation of educational services and quality of products
title_auth	Statistical methods for the evaluation of educational services and quality of products
title_exact_search	Statistical methods for the evaluation of educational services and quality of products
title_full	Statistical methods for the evaluation of educational services and quality of products Mathilde Bini ... Ed.
title_fullStr	Statistical methods for the evaluation of educational services and quality of products Mathilde Bini ... Ed.
title_full_unstemmed	Statistical methods for the evaluation of educational services and quality of products Mathilde Bini ... Ed.
title_short	Statistical methods for the evaluation of educational services and quality of products
title_sort	statistical methods for the evaluation of educational services and quality of products
topic	Educational evaluation / Statistical methods Educational indicators Bildungsökonomik stw Statistische Methode stw Theorie stw Educational evaluation Statistical methods Evaluation (DE-588)4071034-8 gnd Pädagogik (DE-588)4044302-4 gnd Statistik (DE-588)4056995-0 gnd
topic_facet	Educational evaluation / Statistical methods Educational indicators Bildungsökonomik Statistische Methode Theorie Educational evaluation Statistical methods Evaluation Pädagogik Statistik
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=018865330&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT binimatilde statisticalmethodsfortheevaluationofeducationalservicesandqualityofproducts

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