Studies on confirmatory latent structure analysis 検証的潜在構造分析法に関する研究

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著者

    • 江島, 伸興, 1957- エシマ, ノブオキ

書誌事項

タイトル

Studies on confirmatory latent structure analysis

タイトル別名

検証的潜在構造分析法に関する研究

著者名

江島, 伸興, 1957-

著者別名

エシマ, ノブオキ

学位授与大学

九州大学

取得学位

博士 (理学)

学位授与番号

乙第5364号

学位授与年月日

1993-02-23

注記・抄録

博士論文

目次

  1. Contents / (0003.jp2)
  2. Introduction / p1 (0006.jp2)
  3. CHAPTER1.An Overview of Latent Structure Analysis / p8 (0013.jp2)
  4. 1.1.History of latent structure analysis / p8 (0013.jp2)
  5. 1.2.Latent structure models / p12 (0017.jp2)
  6. CHAPTER2.Extraction of Latent Ordered Classes / p18 (0023.jp2)
  7. 2.1.Introduction / p18 (0023.jp2)
  8. 2.2.Construction of a latent space for locating the extracted latent classes / p19 (0024.jp2)
  9. 2.3.Numerical example I / p23 (0028.jp2)
  10. 2.4.Latent ordered class analysis / p25 (0030.jp2)
  11. 2.5.Estimation of the parameters / p27 (0032.jp2)
  12. 2.6.Numerical example II / p31 (0036.jp2)
  13. 2.7.Conclusion / p33 (0038.jp2)
  14. CHAPTER3.Latent Scalogram Analysis / p34 (0039.jp2)
  15. 3.1.Introduction / p34 (0039.jp2)
  16. 3.2.Scaling models / p36 (0041.jp2)
  17. 3.3.Maximum likelihood estimation of the parameters in Eshima's model / p41 (0046.jp2)
  18. 3.4.Model selection procedures / p47 (0052.jp2)
  19. 3.5.A dynamic interpretation of latent scales / p49 (0054.jp2)
  20. 3.6.Evaluation of mixed rates of latent scales / p52 (0057.jp2)
  21. 3.7.Solution of the separating equations / p54 (0059.jp2)
  22. 3.8.Numerical examples / p60 (0065.jp2)
  23. 3.9. Conclusion / p74 (0079.jp2)
  24. CHAPTER4.A Latent Class Approach to Analyzing Latent Continuous Traits / p76 (0081.jp2)
  25. 4.1.Introduction / p76 (0081.jp2)
  26. 4.2.Model / p78 (0083.jp2)
  27. 4.3.The amount of information about latent traits in the present hierarchical assessment / p82 (0087.jp2)
  28. 4.4.An indicator of the correlation of latent traits / p88 (0093.jp2)
  29. 4.5.ML Estimation of the parameters / p90 (0095.jp2)
  30. 4.6.Test for the correlation of latent traits / p92 (0097.jp2)
  31. 4.7.Assignment of respondents / p94 (0099.jp2)
  32. 4.8.A numerical example / p95 (0100.jp2)
  33. 4.9.Conclusion / p101 (0106.jp2)
  34. CHAPTER5.The Latent Markov Chain Model and a Procedure for ML Estimation of the Parameters / p103 (0108.jp2)
  35. 5.1.Introduction / p103 (0108.jp2)
  36. 5.2.The latent Markov chain model / p105 (0110.jp2)
  37. 5.3.The latent Markov process model / p107 (0112.jp2)
  38. 5.4.ML Estimation of the parameters / p109 (0114.jp2)
  39. 5.5.A property of the present procedures / p114 (0119.jp2)
  40. 5.6.Confirmatory analysis / p115 (0120.jp2)
  41. 5.7.Test for goodness-of-fit / p116 (0121.jp2)
  42. 5.8.Numerical examples / p117 (0122.jp2)
  43. 5.9.Conclusion / p124 (0129.jp2)
  44. CHAPTER6.Dynamic Latent Structure Analysis in a Heterogeneous Population / p125 (0130.jp2)
  45. 6.1.Introduction / p125 (0130.jp2)
  46. 6.2.The mover-stayer model / p126 (0131.jp2)
  47. 6.3.The mixed Markov chain model / p129 (0134.jp2)
  48. 6.4.ML estimation of the models for a heterogeneous population / p132 (0137.jp2)
  49. 6.5.A numerical example / p133 (0138.jp2)
  50. 6.6.Conclusion / p136 (0141.jp2)
  51. CHAPTER7.Causal Analysis by use of Latent Class Models / p137 (0142.jp2)
  52. 7.1.Introduction / p137 (0142.jp2)
  53. 7.2.Goodman's approach to causal analysis / p138 (0143.jp2)
  54. 7.3.A multiple-indicator multiple-cause model for causal analysis / p140 (0145.jp2)
  55. 7.4.Numerical illustration I / p142 (0147.jp2)
  56. 7.5.A latent class model for assessing prerequisite relations / p146 (0151.jp2)
  57. 7.6.Numerical illustration II / p148 (0153.jp2)
  58. 7.7.Conclusion / p152 (0157.jp2)
  59. Acknowledgment / p153 (0158.jp2)
  60. References / p154 (0159.jp2)
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各種コード

  • NII論文ID(NAID)
    500000093388
  • NII著者ID(NRID)
    • 8000000952928
  • DOI(NDL)
  • NDL書誌ID
    • 000000257702
  • データ提供元
    • NDL-OPAC
    • NDLデジタルコレクション
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