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L'indagine campionaria : metodi, disegni e tecniche di campionamento / Luigi Fabbris
| L'indagine campionaria : metodi, disegni e tecniche di campionamento / Luigi Fabbris |
| Autore | Fabbris, Luigi <1947- > |
| Pubbl/distr/stampa | Roma : NIS, 1989 |
| Descrizione fisica | 261 p. : tab., fig. ; 24 cm |
| Disciplina |
519.52
11220 |
| Collana | Biblioteca di statistica |
| Soggetto non controllato | Statistica - Campionamento |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNINA-990003778880403321 |
Fabbris, Luigi <1947- >
|
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| Roma : NIS, 1989 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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L'indagine campionaria : metodi, disegni e tecniche di campionamento / Luigi Fabbris
| L'indagine campionaria : metodi, disegni e tecniche di campionamento / Luigi Fabbris |
| Autore | Fabbris, Luigi |
| Pubbl/distr/stampa | Roma, : NIS, 1989 |
| Descrizione fisica | 261 p. : ill. ; 24 cm |
| Disciplina | 519.52 |
| Collana | Biblioteca di statistica |
| Soggetto topico | Probabilità e matematica applicata - Teoria del campionamento |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Titolo uniforme | |
| Record Nr. | UNICAS-CFI0153699 |
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Fabbris, Luigi
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| Roma, : NIS, 1989 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Cassino | ||
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Large sample techniques for statistics / / Jiming Jiang
| Large sample techniques for statistics / / Jiming Jiang |
| Autore | Jiang Jiming |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (689 pages) |
| Disciplina | 519.52 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Mathematical statistics
Sampling (Statistics) Mostreig (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030916954
9783030916947 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 The ε-δ Arguments -- 1.1 Introduction -- 1.2 Getting used to the ε-δ arguments -- 1.3 More examples -- 1.4 Case study: Consistency of MLE in the i.i.d. case -- 1.5 Some useful results -- 1.5.1 Infinite sequence -- 1.5.2 Infinite series -- 1.5.3 Topology -- 1.5.4 Continuity, differentiation, and integration -- 1.6 Exercises -- 2 Modes of Convergence -- 2.1 Introduction -- 2.2 Convergence in probability -- 2.3 Almost sure convergence -- 2.4 Convergence in distribution -- 2.5 Lp convergence and related topics -- 2.6 Case study: χ2-test -- 2.7 Summary and additional results -- 2.8 Exercises -- 3 Big O, Small o, and the Unspecified c -- 3.1 Introduction -- 3.2 Big O and small o for sequences and functions -- 3.3 Big O and small o for vectors and matrices -- 3.4 Big O and small o for random quantities -- 3.5 The unspecified c and other similar methods -- 3.6 Case study: The baseball problem -- 3.7 Case study: Likelihood ratio for a clustering problem -- 3.8 Exercises -- 4 Asymptotic Expansions -- 4.1 Introduction -- 4.2 Taylor expansion -- 4.3 Edgeworth expansion -- method of formal derivation -- 4.4 Other related expansions -- 4.4.1 Fourier series expansion -- 4.4.2 Cornish-Fisher expansion -- 4.4.3 Two time series expansions -- 4.5 Some elementary expansions -- 4.6 Laplace approximation -- 4.7 Case study: Asymptotic distribution of the MLE -- 4.8 Case study: The Prasad-Rao method -- 4.9 Exercises -- 5 Inequalities -- 5.1 Introduction -- 5.2 Numerical inequalities -- 5.2.1 The convex function inequality -- 5.2.2 Hölder's and related inequalities -- 5.2.3 Monotone functions and related inequalities -- 5.3 Matrix inequalities -- 5.3.1 Nonnegative definite matrices -- 5.3.2 Characteristics of matrices -- 5.4 Integral/moment inequalities -- 5.5 Probability inequalities.
5.6 Case study: Some problems on existence of moments -- 5.7 Case study: A variance inequality -- 5.8 Exercises -- 6 Sums of Independent Random Variables -- 6.1 Introduction -- 6.2 The weak law of large numbers -- 6.3 The strong law of large numbers -- 6.4 The central limit theorem -- 6.5 The law of the iterated logarithm -- 6.6 Further results -- 6.6.1 Invariance principles in CLT and LIL -- 6.6.2 Large deviations -- 6.7 Case study: The least squares estimators -- 6.8 Exercises -- 7 Empirical Processes -- 7.1 Introduction -- 7.2 Glivenko-Cantelli theorem and statistical functionals -- 7.3 Weak convergence of empirical processes -- 7.4 LIL and strong approximation -- 7.5 Bounds and large deviations -- 7.6 Non-i.i.d. observations -- 7.7 Empirical processes indexed by functions -- 7.8 Case study: Estimation of ROC curve and ODC -- 7.9 Exercises -- 8 Martingales -- 8.1 Introduction -- 8.2 Examples and simple properties -- 8.3 Two important theorems of martingales -- 8.3.1 The optional stopping theorem -- 8.3.2 The martingale convergence theorem -- 8.4 Martingale laws of large numbers -- 8.4.1 A weak law of large numbers -- 8.4.2 Some strong laws of large numbers -- 8.5 A martingale central limit theorem and related topic -- 8.6 Convergence rate in SLLN and LIL -- 8.7 Invariance principles for martingales -- 8.8 Case study: CLTs for quadratic forms -- 8.9 Case study: Martingale approximation -- 8.10 Exercises -- 9 Time and Spatial Series -- 9.1 Introduction -- 9.2 Autocovariances and autocorrelations -- 9.3 The information criteria -- 9.4 ARMA model identification -- 9.5 Strong limit theorems for i.i.d. spatial series -- 9.6 Two-parameter martingale differences -- 9.7 Sample ACV and ACR for spatial series -- 9.8 Case study: Spatial AR models -- 9.9 Exercises -- 10 Stochastic Processes -- 10.1 Introduction -- 10.2 Markov chains -- 10.3 Poisson processes. 10.4 Renewal theory -- 10.5 Brownian motion -- 10.6 Stochastic integrals and diffusions -- 10.7 Case study: GARCH models and financial SDE -- 10.8 Exercises -- 11 Nonparametric Statistics -- 11.1 Introduction -- 11.2 Some classical nonparametric tests -- 11.3 Asymptotic relative efficiency -- 11.4 Goodness-of-fit tests -- 11.5 U-statistics -- 11.6 Density estimation -- 11.7 Exercises -- 12 Mixed Effects Models -- 12.1 Introduction -- 12.2 REML: Restricted maximum likelihood -- 12.3 Linear mixed model diagnostics -- 12.4 Inference about GLMM -- 12.5 Mixed model selection -- 12.6 Exercises -- 13 Small-Area Estimation -- 13.1 Introduction -- 13.2 Empirical best prediction with binary data -- 13.3 The Fay-Herriot model -- 13.4 Nonparametric small-area estimation -- 13.5 Model selection for small-area estimation -- 13.6 Exercises -- 14 Jackknife and Bootstrap -- 14.1 Introduction -- 14.2 The jackknife -- 14.3 Jackknifing the MSPE of EBP -- 14.4 The bootstrap -- 14.5 Bootstrapping time series -- 14.6 Bootstrapping mixed models -- 14.7 Exercises -- 15 Markov-Chain Monte Carlo -- 15.1 Introduction -- 15.2 The Gibbs sampler -- 15.3 The Metropolis-Hastings algorithm -- 15.4 Monte Carlo EM algorithm -- 15.5 Convergence rates of Gibbs samplers -- 15.6 Exercises -- 16 Random Matrix Theory -- 16.1 Introduction -- 16.2 Fundamental theorems of RMT -- 16.3 Large covariance matrices -- 16.4 High-dimensional linear models -- 16.5 Genome-wide association study -- 16.6 Application to time series -- 16.7 Exercises -- Appendix A -- A.1 Matrix algebra -- A.1.1 Numbers associated with a matrix -- A.1.2 Inverse of a matrix -- A.1.3 Kronecker products -- A.1.4 Matrix differentiation -- A.1.5 Projection -- A.1.6 Decompositions of matrices and eigenvalues -- A.2 Measure and probability -- A.2.1 Measures -- A.2.2 Measurable functions -- A.2.3 Integration. A.2.4 Distributions and random variables -- A.2.5 Conditional expectations -- A.2.6 Conditional distributions -- A.3 Some results in statistics -- A.3.1 The multivariate normal distribution -- A.3.2 Maximum likelihood -- A.3.3 Exponential family and generalized linear models -- A.3.4 Bayesian inference -- A.3.5 Stationary processes -- A.4 List of notation and abbreviations -- References -- Index. |
| Record Nr. | UNISA-996472039303316 |
|
Jiang Jiming
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Large sample techniques for statistics / / Jiming Jiang
| Large sample techniques for statistics / / Jiming Jiang |
| Autore | Jiang Jiming |
| Edizione | [2nd ed.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (689 pages) |
| Disciplina | 519.52 |
| Collana | Springer Texts in Statistics |
| Soggetto topico |
Mathematical statistics
Sampling (Statistics) Mostreig (Estadística) |
| Soggetto genere / forma | Llibres electrònics |
| ISBN |
9783030916954
9783030916947 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Contents -- 1 The ε-δ Arguments -- 1.1 Introduction -- 1.2 Getting used to the ε-δ arguments -- 1.3 More examples -- 1.4 Case study: Consistency of MLE in the i.i.d. case -- 1.5 Some useful results -- 1.5.1 Infinite sequence -- 1.5.2 Infinite series -- 1.5.3 Topology -- 1.5.4 Continuity, differentiation, and integration -- 1.6 Exercises -- 2 Modes of Convergence -- 2.1 Introduction -- 2.2 Convergence in probability -- 2.3 Almost sure convergence -- 2.4 Convergence in distribution -- 2.5 Lp convergence and related topics -- 2.6 Case study: χ2-test -- 2.7 Summary and additional results -- 2.8 Exercises -- 3 Big O, Small o, and the Unspecified c -- 3.1 Introduction -- 3.2 Big O and small o for sequences and functions -- 3.3 Big O and small o for vectors and matrices -- 3.4 Big O and small o for random quantities -- 3.5 The unspecified c and other similar methods -- 3.6 Case study: The baseball problem -- 3.7 Case study: Likelihood ratio for a clustering problem -- 3.8 Exercises -- 4 Asymptotic Expansions -- 4.1 Introduction -- 4.2 Taylor expansion -- 4.3 Edgeworth expansion -- method of formal derivation -- 4.4 Other related expansions -- 4.4.1 Fourier series expansion -- 4.4.2 Cornish-Fisher expansion -- 4.4.3 Two time series expansions -- 4.5 Some elementary expansions -- 4.6 Laplace approximation -- 4.7 Case study: Asymptotic distribution of the MLE -- 4.8 Case study: The Prasad-Rao method -- 4.9 Exercises -- 5 Inequalities -- 5.1 Introduction -- 5.2 Numerical inequalities -- 5.2.1 The convex function inequality -- 5.2.2 Hölder's and related inequalities -- 5.2.3 Monotone functions and related inequalities -- 5.3 Matrix inequalities -- 5.3.1 Nonnegative definite matrices -- 5.3.2 Characteristics of matrices -- 5.4 Integral/moment inequalities -- 5.5 Probability inequalities.
5.6 Case study: Some problems on existence of moments -- 5.7 Case study: A variance inequality -- 5.8 Exercises -- 6 Sums of Independent Random Variables -- 6.1 Introduction -- 6.2 The weak law of large numbers -- 6.3 The strong law of large numbers -- 6.4 The central limit theorem -- 6.5 The law of the iterated logarithm -- 6.6 Further results -- 6.6.1 Invariance principles in CLT and LIL -- 6.6.2 Large deviations -- 6.7 Case study: The least squares estimators -- 6.8 Exercises -- 7 Empirical Processes -- 7.1 Introduction -- 7.2 Glivenko-Cantelli theorem and statistical functionals -- 7.3 Weak convergence of empirical processes -- 7.4 LIL and strong approximation -- 7.5 Bounds and large deviations -- 7.6 Non-i.i.d. observations -- 7.7 Empirical processes indexed by functions -- 7.8 Case study: Estimation of ROC curve and ODC -- 7.9 Exercises -- 8 Martingales -- 8.1 Introduction -- 8.2 Examples and simple properties -- 8.3 Two important theorems of martingales -- 8.3.1 The optional stopping theorem -- 8.3.2 The martingale convergence theorem -- 8.4 Martingale laws of large numbers -- 8.4.1 A weak law of large numbers -- 8.4.2 Some strong laws of large numbers -- 8.5 A martingale central limit theorem and related topic -- 8.6 Convergence rate in SLLN and LIL -- 8.7 Invariance principles for martingales -- 8.8 Case study: CLTs for quadratic forms -- 8.9 Case study: Martingale approximation -- 8.10 Exercises -- 9 Time and Spatial Series -- 9.1 Introduction -- 9.2 Autocovariances and autocorrelations -- 9.3 The information criteria -- 9.4 ARMA model identification -- 9.5 Strong limit theorems for i.i.d. spatial series -- 9.6 Two-parameter martingale differences -- 9.7 Sample ACV and ACR for spatial series -- 9.8 Case study: Spatial AR models -- 9.9 Exercises -- 10 Stochastic Processes -- 10.1 Introduction -- 10.2 Markov chains -- 10.3 Poisson processes. 10.4 Renewal theory -- 10.5 Brownian motion -- 10.6 Stochastic integrals and diffusions -- 10.7 Case study: GARCH models and financial SDE -- 10.8 Exercises -- 11 Nonparametric Statistics -- 11.1 Introduction -- 11.2 Some classical nonparametric tests -- 11.3 Asymptotic relative efficiency -- 11.4 Goodness-of-fit tests -- 11.5 U-statistics -- 11.6 Density estimation -- 11.7 Exercises -- 12 Mixed Effects Models -- 12.1 Introduction -- 12.2 REML: Restricted maximum likelihood -- 12.3 Linear mixed model diagnostics -- 12.4 Inference about GLMM -- 12.5 Mixed model selection -- 12.6 Exercises -- 13 Small-Area Estimation -- 13.1 Introduction -- 13.2 Empirical best prediction with binary data -- 13.3 The Fay-Herriot model -- 13.4 Nonparametric small-area estimation -- 13.5 Model selection for small-area estimation -- 13.6 Exercises -- 14 Jackknife and Bootstrap -- 14.1 Introduction -- 14.2 The jackknife -- 14.3 Jackknifing the MSPE of EBP -- 14.4 The bootstrap -- 14.5 Bootstrapping time series -- 14.6 Bootstrapping mixed models -- 14.7 Exercises -- 15 Markov-Chain Monte Carlo -- 15.1 Introduction -- 15.2 The Gibbs sampler -- 15.3 The Metropolis-Hastings algorithm -- 15.4 Monte Carlo EM algorithm -- 15.5 Convergence rates of Gibbs samplers -- 15.6 Exercises -- 16 Random Matrix Theory -- 16.1 Introduction -- 16.2 Fundamental theorems of RMT -- 16.3 Large covariance matrices -- 16.4 High-dimensional linear models -- 16.5 Genome-wide association study -- 16.6 Application to time series -- 16.7 Exercises -- Appendix A -- A.1 Matrix algebra -- A.1.1 Numbers associated with a matrix -- A.1.2 Inverse of a matrix -- A.1.3 Kronecker products -- A.1.4 Matrix differentiation -- A.1.5 Projection -- A.1.6 Decompositions of matrices and eigenvalues -- A.2 Measure and probability -- A.2.1 Measures -- A.2.2 Measurable functions -- A.2.3 Integration. A.2.4 Distributions and random variables -- A.2.5 Conditional expectations -- A.2.6 Conditional distributions -- A.3 Some results in statistics -- A.3.1 The multivariate normal distribution -- A.3.2 Maximum likelihood -- A.3.3 Exponential family and generalized linear models -- A.3.4 Bayesian inference -- A.3.5 Stationary processes -- A.4 List of notation and abbreviations -- References -- Index. |
| Record Nr. | UNINA-9910559398903321 |
|
Jiang Jiming
|
||
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Large sample techniques for statistics / Jiming Jiang
| Large sample techniques for statistics / Jiming Jiang |
| Autore | JIANG, Jiming |
| Pubbl/distr/stampa | New York : Springer, 2010 |
| Descrizione fisica | XVII, 609 p. : 24 cm |
| Disciplina | 519.52(Teoria del campionamento) |
| Collana | Springer texts in statistics |
| Soggetto topico | campionamento statistico |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-990005555910203316 |
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JIANG, Jiming
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| New York : Springer, 2010 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Lezioni di inferenza statistica / Luigi D'Ambra
| Lezioni di inferenza statistica / Luigi D'Ambra |
| Autore | D'Ambra, Luigi |
| Edizione | [4. ed] |
| Pubbl/distr/stampa | Napoli : RCE, 2007 |
| Descrizione fisica | 431 p. ; 30 cm |
| Disciplina |
519.5
519.52 519.54 |
| Collana | Dipartimento di matematica e statistica. Università "Federico II" Napoli, Serie didattica |
| Soggetto non controllato | Inferenza statistica |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNINA-990008594480403321 |
|
D'Ambra, Luigi
|
||
| Napoli : RCE, 2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Lezioni di inferenza statistica / Luigi D'Ambra
| Lezioni di inferenza statistica / Luigi D'Ambra |
| Autore | D'Ambra, Luigi |
| Edizione | [4. ed.] |
| Pubbl/distr/stampa | Napoli : RCE, 2000 |
| Descrizione fisica | 416 p. ; 24 cm |
| Disciplina |
519.5
519.52 519.54 |
| Collana | Serie didattica |
| Soggetto non controllato |
Inferenza statistica
Inferenza statistica - Manuali |
| ISBN | 88-8399-018-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNINA-990003369730403321 |
|
D'Ambra, Luigi
|
||
| Napoli : RCE, 2000 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Lezioni di inferenza statistica / Luigi D'Ambra
| Lezioni di inferenza statistica / Luigi D'Ambra |
| Autore | D'Ambra, Luigi |
| Edizione | [3. ed.] |
| Pubbl/distr/stampa | Napoli, : Rocco Curto, 1997 |
| Descrizione fisica | 517 p. ; 23 cm |
| Disciplina |
519.52
519.54 519.5 |
| Collana | Serie didattica |
| Soggetto non controllato |
Inferenza statistica
Inferenza statistica - Manuali |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | ita |
| Record Nr. | UNINA-990003366160403321 |
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D'Ambra, Luigi
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| Napoli, : Rocco Curto, 1997 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Living standards analytics : development through the lens of household survey data / Dominique Haughton, Jonathan Haughton
| Living standards analytics : development through the lens of household survey data / Dominique Haughton, Jonathan Haughton |
| Autore | Haughton, Dominique |
| Pubbl/distr/stampa | New York : Springer, 2011 |
| Descrizione fisica | XXII, 314 p. : fig., tab. |
| Disciplina |
300.72
519.52 |
| Altri autori (Persone) | Haughton, Jonathan |
| Collana | Statistics for social and behavioral sciences |
| Soggetto non controllato | Scienze sociali - Ricerca - Analisi dei dati - Metodi statistici |
| ISBN | 9781461430001 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-990010089330403321 |
| Haughton, Dominique | ||
| New York : Springer, 2011 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Margins of error [[electronic resource] ] : a study of reliability in survey measurement / / Duane F. Alwin
| Margins of error [[electronic resource] ] : a study of reliability in survey measurement / / Duane F. Alwin |
| Autore | Alwin Duane F (Duane Francis), <1944-> |
| Pubbl/distr/stampa | Hoboken, N.J., : Wiley-Interscience, c2007 |
| Descrizione fisica | 1 online resource (410 p.) |
| Disciplina |
001.4/33
001.433 519.52 |
| Collana | Wiley series in survey methodology |
| Soggetto topico |
Surveys
Error analysis (Mathematics) |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-280-93516-2
9786610935161 0-470-14631-1 0-470-14630-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Margins of Error: A Study of Reliability in Survey Measurement; Contents; Preface; Acknowledgments; Foreword; 1. Measurement Errors in Surveys; 1.1 Why Study Survey Measurement Error?; 1.2 Survey Errors; 1.3 Survey Measurement Errors; 1.4 Standards of Measurement; 1.5 Reliability of Measurement; 1.6 The Need for Further Research; 1.7 The Plan of this Book; 2. Sources of Survey Measurement Error; 2.1 The Ubiquity of Measurement Errors; 2.2 Sources of Measurement Error in Survey Reports; 2.3 Consequences of Measurement Error; 3. Reliability Theory for Survey Measures; 3.1 Key Notation
3.2 Basic Concepts of Classical Reliability Theory3.3 Nonrandom Measurement Error; 3.4 The Common-Factor Model Representation of CTST; 3.5 Scaling of Variables; 3.6 Designs for Reliability Estimation; 3.7 Validity and Measurement Error; 3.8 Reliability Models for Composite Scores; 3.9 Dealing with Nonrandom or Systematic Error; 3.10 Sampling Considerations; 3.11 Conclusions; 4. Reliability Methods for Multiple Measures; 4.1 Multiple Measures versus Multiple Indicators; 4.2 Multitrait-Multimethod Approaches; 4.3 Common-Factor Models of the MTMM Design 4.4 Classical True-Score Representation of the MTMM Model4.5 The Growing Body of MTMM Studies; 4.6 An Example; 4.7 Critique of the MTMM Approach; 4.8 Where Are We?; 5. Longitudinal Methods for Reliability Estimation; 5.1 The Test-Retest Method; 5.2 Solutions to the Problem; 5.3 Estimating Reliability Using the Quasi-Markov Simplex Model; 5.4 Contributions of the Longitudinal Approach; 5.5 Components of the Survey Response; 5.6 Where to from Here?; 6. Using Longitudinal Data to Estimate Reliability Parameters; 6.1 Rationale for the Present Study; 6.2 Samples and Data 6.3 Domains of Measurement6.4 Statistical Estimation Strategies; 6.5 Comparison of Methods of Reliability Estimation; 6.6 The Problem of Attrition; 6.7 Which Reliability Estimates?; 6.8 Conclusions; 7. The Source and Content of Survey Questions; 7.1 Source of Information; 7.2 Proxy Reports; 7.3 Content of Questions; 7.4 Summary and Conclusions; 8. Survey Question Context; 8.1 The Architecture of Survey Questionnaires; 8.2 Questions in Series versus Questions in Batteries; 8.3 Location in the Questionnaire; 8.4 Unit Length and Position in Series and Batteries 8.5 Length of Introductions to Series and Batteries8.6 Conclusions; 9. Formal Properties of Survey Questions; 9.1 Question Form; 9.2 Types of Closed-Form Questions; 9.3 Number of Response Categories; 9.4 Unipolar versus Bipolar Scales; 9.5 Don't Know Options; 9.6 Verbal Labeling of Response Categories; 9.7 Survey Question Length; 9.8 Conclusions; 10. Attributes of Respondents; 10.1 Reliability as a Population Parameter; 10.2 Respondent Attributes and Measurement Error; 10.3 Age and Reliability of Measurement; 10.4 Schooling and Reliability of Measurement 10.5 Controlling for Schooling Differences |
| Record Nr. | UNINA-9910143684903321 |
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Alwin Duane F (Duane Francis), <1944->
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| Hoboken, N.J., : Wiley-Interscience, c2007 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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