Capursi / Attanasio Statistical Methods for the Evaluation of University Systems

1. Auflage 2011
ISBN: 978-3-7908-2375-2
Verlag: Physica-Verlag
Format: PDF
Kopierschutz: Wasserzeichen (»Systemvoraussetzungen)

E-Book, Englisch, 282 Seiten

This book presents a collection of statistical methods and procedures to assess data coming from educational systems. The topics examined include: statistical methods for constructing composite indicators, applied measurements, assessment of educational systems, measurement of the performance of the students at Italian universities, and statistical modeling for questionnaire data. Other issues are the implications of introducing different assessment criteria and procedures to the Italian university system.

Autoren/Hrsg.

Capursi, Vincenza

Attanasio, Massimo

Inhaltsverzeichnis

1;Preface;4
2;Contents;8
3;Contributors;10
4;Part I Introduction: Different Perspectives of the Evaluation of the Italian University System;13
4.1;1 TES – From Impressionism to Expressionism;14
4.1.1;Lorenzo Bernardi;14
4.1.2;1.1 Foreword: Excusatio Non Petita;14
4.1.3;1.2 Pars Destruens: Accusatio Manifesta;15
4.1.4;1.3 Pars Costruens: Non nova, Sed Nove;17
4.1.4.1;1.3.1 Guiding Principles;17
4.1.4.2;1.3.2 The Proposal: A First, Almost Utopian Design;21
4.1.4.3;1.3.3 A Possible Design;23
4.2;2 The Assessment of University Teaching by Students: TheOrganizational Perspective;26
4.2.1;Luigi Enrico Golzio;26
4.2.2;2.1 Assessment in Organisations;26
4.2.3;2.2 Assessment by Students in Italian Universities;28
4.2.4;2.3 Assessment by Students as an Organisational Process;29
4.2.5;2.4 The Content of Assessment by Students;34
4.2.6;2.5 The Case of the University of Sassari;39
4.2.7;References;42
4.3;3 University League Tables;43
4.3.1;L. Bernardi, P. Bolzonello, and A. Tuzzi;43
4.3.2;3.1 Introduction;43
4.3.3;3.2 The Censis Ranking System;44
4.3.4;3.3 Indicators for Evaluation and Measurement;45
4.3.5;3.4 The Censis Data;47
4.3.5.1;3.4.1 Normalization and Aggregation;47
4.3.5.2;3.4.2 The Simple Indicators Used by Censis;48
4.3.5.3;3.4.3 Preliminary Analysis;48
4.3.6;3.5 Alternative Ways to Analyse the Data;52
4.3.7;3.6 Results;55
4.3.8;3.7 Conclusions;56
4.3.9;References;60
5;Part II The Evaluation in the Italian Universities: Student Teaching Evaluation;62
5.1;4 Structural Equation Models and Student Evaluation of Teaching: A PLS Path Modeling Study;63
5.1.1;Simona Balzano and Laura Trinchera;63
5.1.2;4.1 Introduction;63
5.1.3;4.2 PLS Approach to Structural Equation Models;64
5.1.4;4.3 Applying PLS-PM to Students Evaluation of Teaching;67
5.1.4.1;4.3.1 The Data and Model Specification;67
5.1.4.2;4.3.2 The Results;69
5.1.5;4.4 Concluding Remarks;73
5.1.6;References;73
5.2;5 A Study on University Students' Opinions about Teaching Quality: A Model Based Approach for Clustering Ordinal Data;75
5.2.1;Marcella Corduas;75
5.2.2;5.1 Introduction;75
5.2.3;5.2 A Mixture Distribution for Ordinal Data;76
5.2.4;5.3 The Kullback-Liebler Divergence;77
5.2.5;5.4 Clustering;78
5.2.6;5.5 The Analysis of Students' Opinions;79
5.2.6.1;5.5.1 The Data Set;79
5.2.6.2;5.5.2 The Results;80
5.2.7;5.6 Final Remarks;84
5.2.8;References;84
5.3;6 The Impact of Teaching Evaluation: Factors that Favour Positive Views from Student Representatives;86
5.3.1;Simone Gerzeli;86
5.3.2;6.1 Introduction;86
5.3.3;6.2 Methods;87
5.3.3.1;6.2.1 Study Design;87
5.3.3.2;6.2.2 Statistical Analysis;88
5.3.4;6.3 Results;90
5.3.4.1;6.3.1 Respondents;90
5.3.4.2;6.3.2 The Availability and Discussion of the Teaching Evaluation Results;91
5.3.4.3;6.3.3 Changes Induced by the Results of the Teaching Evaluation;93
5.3.4.4;6.3.4 The Usefulness of the Teaching Evaluation as Perceived by the Student Representatives;94
5.3.4.5;6.3.5 The Multilevel Regression Model;96
5.3.5;6.4 Concluding Remarks;97
5.3.6;References;99
5.4;7 University Teaching and Students' Perception: Models of the Evaluation Process;100
5.4.1;Maria Iannario and Domenico Piccolo;100
5.4.2;7.1 Introduction;100
5.4.3;7.2 Measurement of Students' Perception About Teaching Quality;101
5.4.4;7.3 Perception and Rating as Complex Decisions;102
5.4.5;7.4 Latent Variables and Item Response Theory;103
5.4.6;7.5 An Alternative Model for the Evaluation Process;106
5.4.6.1;7.5.1 Rationale for CUB Models;107
5.4.6.2;7.5.2 CUB Models;107
5.4.7;7.6 Empirical Evidences for University Teaching Evaluation;109
5.4.7.1;7.6.1 CUB Models Without Covariates;109
5.4.7.2;7.6.2 CUB Models with Covariates;111
5.4.8;7.7 Concluding Remarks;114
5.4.9;References;115
5.5;8 Students' Evaluation of Teaching Effectiveness: Satisfaction and Related Factors;120
5.5.1;Michele Lalla, Patrizio Frederic, and Davide Ferrari;120
5.5.2;8.1 Introduction;120
5.5.3;8.2 Literature Review;122
5.5.4;8.3 Questionnaire and Data;124
5.5.5;8.4 Models and Results;127
5.5.6;8.5 Conclusions;134
5.5.7;References;135
6;Part III The Evaluation in the Italian Universities: Statistical Methods for Careers and Services Evaluation;137
6.1;9 Modeling Ordinal Item Responses via Binary GLMMs and Alternative Link Functions: An Application to Measurement of a Perceived Service Quality;138
6.1.1;Vito M.R. Muggeo and Fabio Aiello;138
6.1.2;9.1 Introduction;138
6.1.3;9.2 Data;139
6.1.4;9.3 Methods;141
6.1.4.1;9.3.1 The GLMM Framework;141
6.1.4.2;9.3.2 Alternative Link Functions;142
6.1.5;9.4 Results;143
6.1.6;9.5 Conclusions;147
6.1.7;References;148
6.2;10 Analyzing Undergraduate Student Graduation Delay: ALongitudinal Perspective;149
6.2.1;Paola Costantini and Maria Prosperina Vitale;149
6.2.2;10.1 Introduction;149
6.2.3;10.2 The Graduation Delay Issue;150
6.2.4;10.3 Measuring and Analyzing Graduation Delay;151
6.2.5;10.4 Defining a Longitudinal Graduation Delay Indicator;153
6.2.6;10.5 Latent Curve Model to Monitor Student Careers;154
6.2.7;10.6 A Case Study: The Delay Patterns of a Cohort of Undergraduate Students;155
6.2.7.1;10.6.1 A Conditional Linear Latent Curve Model;156
6.2.8;10.7 Some Concluding Remarks;162
6.2.9;References;162
6.3;11 Assessing the Quality of the Management of Degree Programsby Latent Class Analysis;164
6.3.1;Isabella Sulis and Mariano Porcu;164
6.3.2;11.1 Introduction;164
6.3.3;11.2 Building up a Composite Indicator;164
6.3.3.1;11.2.1 A Measure of the Perceived Quality of a University Service;165
6.3.4;11.3 Methodological Issues;166
6.3.4.1;11.3.1 Sorting Latent Classes;167
6.3.5;11.4 The Application;167
6.3.5.1;11.4.1 The Data;167
6.3.5.2;11.4.2 The Analysis;169
6.3.6;11.5 Final Remarks;174
6.3.7;References;174
7;Part IV Research Design and Data for Evaluation: University Between the High School and the Labour Market;176
7.1;12 The Multicriteria Electre III Model Applied to the Evaluation of the Placement of University Graduates;177
7.1.1;Rosalinda Allegro and Ornella Giambalvo;177
7.1.2;12.1 Preliminary Remarks;177
7.1.3;12.2 The Data;178
7.1.4;12.3 The Multicriteria Electre III Model;182
7.1.5;12.4 Groups of Disciplines Ranking;191
7.1.6;12.5 Final Remarks;195
7.1.7;References;196
7.2;13 Competences and Professional Options of the Italian Graduates: Results from the Textual Analysis of the Degree Course Information Data;197
7.2.1;S.Balbi, C.Crocetta, M.F.Romano, S.Zaccarin, and E.Zavarrone;197
7.2.2;13.1 Introduction;197
7.2.3;13.2 Textual Analysis of the University Education Offer;198
7.2.4;13.3 Identification of the Competences Offeredand the Job Possibilities;200
7.2.5;13.4 Consistency of the Education Paths with Employment Perspectives;204
7.2.6;13.5 Coherence Between the Three Year Degree Courses and the Specialised Degree Programs;207
7.2.7;13.6 Final Remarks;208
7.2.8;References;209
7.3;14 After the PhD: A Study of Career Paths, Job and Training Satisfaction Among PhD Graduates from an Italian University;210
7.3.1;Stefano Campostrini;210
7.3.2;14.1 Introduction;210
7.3.3;14.2 The Survey;211
7.3.4;14.3 How PhD Graduates Evaluate Doctorate Programme Teaching;212
7.3.5;14.4 How PhD Graduates Evaluate Their Doctorate Programme Research Experiences;214
7.3.6;14.5 How PhD Graduates Evaluate Their Doctorate Programme Research Experiences;215
7.3.7;14.6 PhD Holder Levels of General Job Satisfaction;219
7.3.8;14.7 The Impact of Evaluation: A Reflection on the Use of the Evaluation;222
7.3.9;References;223
7.4;15 Secondary School Choices in Italy: Ability or Social Background?;224
7.4.1;Dalit Contini and Andrea Scagni;224
7.4.2;15.1 Introduction;224
7.4.3;15.2 The Methodology;226
7.4.4;15.3 The Analysis for Italy;228
7.4.4.1;15.3.1 Institutional Features;228
7.4.4.2;15.3.2 The Data;229
7.4.4.3;15.3.3 Final Marks in Lower Secondary School;229
7.4.5;15.4 Sample Selection;231
7.4.5.1;15.4.1 The Problem;231
7.4.5.2;15.4.2 Supporting the Assumptions;233
7.4.6;15.5 The Empirical Analysis;235
7.4.6.1;15.5.1 Semi-parametric Approach;235
7.4.6.2;15.5.2 Sample Selection Correction Factors;236
7.4.6.3;15.5.3 Results;237
7.4.7;15.6 Conclusions;244
7.4.8;References;245
7.5;16 Labour Market Outcomes for Ph.D. Graduates;247
7.5.1;Antonella D'Agostino and Giulio Ghellini;247
7.5.2;16.1 Introduction;247
7.5.3;16.2 The Data;248
7.5.4;16.3 Socio-Biographic Background of Ph.D. Graduates;249
7.5.5;16.4 Employment Status and Earnings One-Year After the Ph.D. Thesis;250
7.5.6;16.5 Modelling Earnings of Ph.D. Graduates;252
7.5.6.1;16.5.1 Methodological Background;252
7.5.6.2;16.5.2 Sensitivity Analysis;254
7.5.6.3;16.5.3 Results;255
7.5.7;16.6 Further Discussion and Conclusions ;257
7.5.8;References;259
7.6;17 Labour Market Performance of University Graduates: Evidence from Italy;261
7.6.1;B. d'Hombres, S. Tarantola, and D. Van Nijlen;261
7.6.2;17.1 Introduction;261
7.6.3;17.2 Review of the Literature on the Determinants of Labor Market Outcomes of Graduates;263
7.6.4;17.3 Labor Market Determinants of Italian Graduate Students;264
7.6.4.1;17.3.1 Data;264
7.6.4.2;17.3.2 Empirical Methodology;268
7.6.4.3;17.3.3 Empirical Results;271
7.6.5;17.4 Faculty-Performance Indicators: The Case of Economics;275
7.6.5.1;17.4.1 Unadjusted Versus Adjusted Ranking Based on Labor Market Outcomes;275
7.6.5.2;17.4.2 Uncertainty Associated with Rankings;279
7.6.6;17.5 Conclusion;281
7.6.7;References;282