본문 바로가기
KERD
논문KCI 등재

확인적 요인분석과 구조방정식모형분석에서 다차원 척도에 대한 다양한 문항묶음방법 적용연구

The Empirical Study of Various Item Parceling Methods on Multidimensional Scales in CFA and SEM

오숙영
한국교육방법학회 (The Korean Journal Of Educational Methodology Studies)|2017.05|교육방법연구 (The Korean Journal of Educational Methodology Studies)|제29권 제2호|pp. 313-345|DOI 10.17927/tkjems.2017.29.2.313

0

조회

0

다운로드

58

인용

0

참고문헌

키워드

확인적 요인분석Confirmatory Factor AnalysisStructural Equation Model.구조방정식모형분석요인묶음방법상관묶음방법방사묶음방법다차원묶음방법Factor Item Parceling Method
주제분류교육학

초록

본 연구의 목적은 확인적 요인분석과 구조방정식모형분석에서 다차원 척도에 대하여 개별문항방법과 다양한 문항묶음방법(요인․상관․방사․다차원)을 적용한 후에, 개별문항방법 결과와 다양한 문항묶음방법 결과를 비교분석함으로서 문항묶음방법의 효과성을 파악하는 것이다. 문항묶음은 측정모형에서 여러 관측변수들을 평균이나 합산으로 묶어서 사용하는 방법론이다. 이를 실시했을 때는 자료비정규성 완화, 모형간명화, 측정오차 감소, 공통분산 증가, 모형적합도 안정성, 모수추정 안정성이라는 장점이 있게 된다. 그러나 문항묶음방법론은 다차원 척도에 적용할 때에 일차원성 가정이라는 논쟁점이 제기되며, 다양한 시뮬레이션 연구를 할 때에 모형적합도 및 모수추정의 안정성 혹은 편향성이라는 상반된 연구결과가 제기되고 있다. 따라서 본 연구는 문항묶음방법론의 논쟁점에 대한 제언을 제시하고자, 기존 연구와 차별성을 두는 다음의 연구를 진행한다. 첫째, 다차원(다요인)에 속한 하나의 요인에 대하여 일차원성검정을 한 후에, 일차원성이 검정된 하나의 요인에 대하여 문항묶음방법을 적용한다. 둘째, 다양한 상황의 가설들을 설정하여 문항묶음방법을 적용한 후에, 문항묶음방법이 어떤 경우에는 효과적이고 어떤 경우에는 비효과적인지 파악한다. 다양한 상황을 고려한 모형은 단순한 다차원(다요인)의 모형, 위계적인 다차원(다요인)의 모형, 작은 표본에 적용한 모형, 표준도구이지만 과다한 요인과 문항을 포함한 복잡한 다차원(다요인)의 모형, 그리고 다차원(다요인)의 모형들 간의 구조모형이다. 셋째, 다양한 연구모형에 개별문항과 다양한 문항묶음(요인․상관․방사․다차원)을 적용한 후에, 정규성, 확인적 요인분석의 모형적합도 및 수렴타당도, 구조방정식모형의 모형적합도 및 효과크기를 검정한다. 다음은 결과이다. 첫째, 확인적 요인분석 결과이다. 문항묶음방법은 개별문항방법과 비교해서, 정규성과 모형적합도(TLI, CFI, RMSEA) 및 수렴타당도(경로계수, 개념신뢰도, 분산추출지수)가 훨씬 더 안정적이며 좋은 수치로 추정되었다. 둘째, 구조방정식모형분석 결과이다. 문항묶음방법은 개별문항방법과 비교하여, 모형적합도와 경로계수(직접효과, 간접효과, 총효과)가 약간 더 안정적이었다. 셋째, 다양한 문항묶음방법론비교 결과이다. 확인적 요인분석에는 방사묶음, 구조방정식모형분석에는 다차원묶음이 효과적이었다. 넷째, 다양한 상황의 가설 결과이다. 문항묶음방법은 작은 표본에 적용했을 경우에, 요인과 문항이 과도하게 많은 복잡한 모형에 적용했을 경우에, 정규성과 모형적합도 및 경로계수가 매우 안정적으로 되었다. 따라서 작은 표본이면서 개별문항수보다 문항묶음수가 크게 감소하는 경우에는 모수 당 표본의 수가 증가되므로, 추정의 안정성은 더욱 향상될 것이다. 여섯째, 문항묶음방법은 정규성가정이 만족되는 자료보다는 위배되는 자료에 적용했을 경우에, 적합도 및 모수추정이 현격하게 좋아졌다. 본 연구는 문항묶음방법론에 대한 이론적인 설명 및 경험적인 결과를 제시하면서, 문항묶음방법이 개별문항방법보다는 효과적임을 검정하면서, 문항묶음방법의 효과적인 적용을 위한 조언도 제시했다.

Abstract

The purpose of this study is to apply the methods of factor․correlation․radial․multidimensional parceling to multidimensional scales in Confirmatory Factor Analysis and Structural Equation Model, and then investigate the possibility of the utility of item parceling method by comparing the results of the individual item method with the results of item parceling method. The item parceling method, defined as the sum or mean scores of individual item scores, have been widely used as observation variable in setting up the measurement model in CFA and SEM. The item parceling method is used in data: (1) that is not optimal(nonnormality); (2) that the sample size is small; (3)that has many items in factor; (4) that has hierarchical and complex factors in scale; and (5) that use the standardized scale which is failed to test CFA. The merits of item parceling method are; (1) the alleviation of nonnormality of data; (2) the simplicity of model; (3) the diminution of measure error index; (4) the increasing of communality; (5) the diminution of unique variance; (6) the stability of model fit; and (7) the stability of parameter estimates. The dispute of item parceling method is the assumption of unidimension and the results of stability or deviation of parameter estimate and model fit in simulation research. The model of research in this study are as follows. The CFA is testified in the model; (1) which has small factors in scale; (2) which has many hierarchical factors in scale; (3) which has small subject; (4) which has many and complex factors and items in standardized scale, and the SEM is testified in the model which has multidimensional factors. The item parceling methods which are composed of factor․correlation․radial․multidimensional method and the individual item method are applied to the measurement variables in models which are explained. The results are as follows. First, the item parceling method is more effective than individual item method in CFA. For example, the indexes of normality, model fit as χ2, TLI, CFI, and RMSEA, parameter estimate, and convergent validity as construct reliability and AVE in item parceling method are more stable than it. Second, the item parceling method is more effective than individual item method in SEM. For example, the indexes of model fit and parameter estimate in item parceling method are more stable. Third, the most effective item parceling method in CFA is radial parceling method and in CFA is multidimensional parceling method. Forth, the item parceling method is effective as it is applied in the model that has small subject. Fifth, the item parceling method is effective as it is applied in the model that has many, complex, and standardized items. Sixth, the item parceling method is more effective than individual item method in SEM. This study suggests the theory of item parceling method from the point of view in psychological measurement and structure equation, the variable methods of item parceling, the merits of item parceling, and the differences between item parceling method and individual item method from empirical research results. This study makes the suggestions that item parceling method can perform to its potential when; (1) the subject size is small; (2) the psychometric properties of the given data is nonnormality; (3) model is complex, (4) there are violations of the assumptions for SEM(e.g., correlated errors), and that item parceling method should not be used in the measurement model which is based on wrong theory and concept.

참고문헌 (68)

  1. 김수영, "구조방정식 모형의 기본과 확장 : mplus 예제와 함께" (2016), 학지사
  2. 이지현, "문항묶음:원리의 이해와 적용", 한국심리학회지:일반, 35(2), pp.327~353 (2016), 한국심리학회
  3. 우종필, "구조방정식모델에서 다차원성 개념의 항목묶음 편향에 대한 연구", 경영학연구, 44(4), pp.1131~1147 (2015), 한국경영학회
  4. 조현철, "구조방정식 모델에서 항목묶음이 인과 모수의 검정과 적합도 평가에 미치는 영향", Journal of Global Scholars of Marketing Science(마케팅과학연구), 17(3), pp.133~153 (2007), 한국마케팅과학회
  5. Afifi, T. D., "The chilling effect in families and the pressure to conceal secrets", Communication Monographs, 72, pp.192~216 (2005)
  6. Anderson, J. C., "Structural equation modeling in practice : A review and recommended two-step approach", Psychological Bulletin, 103(3), pp.411~423 (1988)
  7. Bagozzi, R. P., "A general approach to representing multifaceted personality constructs : Application to state self-esteem", Structural Equation Modeling, 1, pp.45~87 (1994)
  8. Bandalos, D. L., "Assessing sources of error in structural equation models : The effects of sample size, reliability, and model misspecification", Structural Equation Modeling : A Multidisciplinary Journal, 4(3), pp.177~192 (1997)
  9. Bandalos, D. L., "The effects of item parceling on goodness-of-fit and parameter estimate bias in structural equation modeling", Structural Equation Modeling, 9(1), pp.78~102 (2002)
  10. Bandalos, D. L., "Advanced structural equation modeling: New developments and techniques in structural equation modeling", pp.269~296 (2001), IEA
  11. Bass, B. M., "Leadership and performance beyond expectations" (1985), Free Press
  12. Bentler, P. M., "Comparative fit indexes in structural models", Psychological Bulletin, 107(2), pp.238~248 (1990)
  13. Bentler. P. M., "Significance tests and goodness of fit in the analysis of covariance structures", Psychological Bulletin, 88(3), pp.588~606 (1980)
  14. Bogozzi, R. P., "A general approach for representing constructs in organizational research", Organizational Research Methods, 1, pp.45~87 (1998)
  15. Bogozzi, R. P., "A general approach to representation multifaceted personality constructs : Application to state self-esteem", Structural Equation Modeling, 1, pp.35~67 (1994)
  16. Bolye, G. J., "Does item homogeneity indicate internal consistency or item redundancy in psychometric scales?", Personality and Individual Difference, 3, pp.291~294 (1991)
  17. Brown, K. S., "Situated cognition and the culture of learning", Educational Researcher, 18(1), pp.32~42 (1989)
  18. Browne, M. W., "Testing structural equation models" (1993), Sage
  19. Campbell, J. P., "Handbook of industrial and organizational psychology", pp.185~222 (1976), Rand McNally
  20. Cattell, P. B., "Validation and intensification of the sixteen personality factor questionnaire", Journal of Clinical Psychology, 12, pp.205~214 (1956)
  21. Cattell, P. B., "The radical parcel double design : A solution to the item versus parcel controversy", Multi-Variate Behavioral Research, 10(2), pp.165~179 (1975)
  22. Coffman, D. L., "Using parcels to convert path analysis methods into latent variable models", Multivariate Behavior Research, 40, pp.235~259 (2005)
  23. Corno, L., "The best-laid plans : Modern conceptions of volition", Educational Researcher, 22, pp.14~22 (1993)
  24. Daniel, A. Sass, "The effects of parceling unidimensional sales on structural parameter estimates in structural equation model", Structural Equation Modeling, 13(4), pp.566~586 (2006)
  25. Efron, B., "Boostrap methods : Another look at the jackknife", The Annals of Statistics, 7(1), pp.1~26 (1979)
  26. Enders, C. K., "The relative performance of full information maximum likelihood estimation for missing data in structural equation models", Structural Equation Modeling, 8, pp.430~457 (2001)
  27. Field, A., "Discovering statistics using SPSS statistics" (2009), UK Sage Publication Ltd
  28. Finch, J. F., "Effects of sample size and nonnormality on the estimation of mediated effects in latent variable models", Structural Equation Modeling, 4, pp.87~107 (1997)
  29. Fornell, C., "Evaluating structural equation models with unobservable variables and measurement error", Journal of Marketing Research, 18(1), pp.39~50 (1981)
  30. Gardner, H., "Frames of mind: The theory of multiple intelligence" (1983), Basic Books
  31. Gardner, H., "Multiple intelligence: The theory of multiple intelligence" (1993), Basic Books
  32. Gerbing, D. W., "An updated paradigm for scale development incorporating unidimensionality and its assessment", Journal of Marketing Research, 25, pp.186~192 (1988)
  33. Graham, J. W., "Adding missing-data-relevant variables to FILM-based structural equation models", Structural Equation Modeling, 10, pp.80~100 (2003)
  34. Hagtvet, K. A., "How well do item parcels represent conceptually defined latent constructs? A two-facet approach", Structural Equation Modeling, 11(2), pp.168~193 (2004)
  35. Hall, R. J., "Item parceling strategies in SEM : Investigating the subtle effects of unmodeled secondary constructs", Organizational Research Methods, 2, pp.233~256 (1999)
  36. Hair, J. F. Jr., "Multivariate data analysis" (2009), Person India
  37. Hair, J. F. Jr., "Multivariate data analysis" (1998), Person India
  38. Hau, K-T., "The use of item parcels in structural equation modeling : Non-normal data and small sample sizes", British Journal of Mathematical Statistical Psychology, 57, pp.327~351 (2004)
  39. Holbert, R. L., "Structural equation modeling in the complementary effects of news media use", Political Communication Research, 28, pp.531~551 (2002)
  40. Hughey, J., "16PE-E structure using radical parcel versus items", Journal of General Psychology, 107(1), pp.107~119 (1982)
  41. Kenny, D. A., "The performance of RMSEA in models with small degrees of freedom", Sociological Methods and Research, 44, pp.486~507 (2014)
  42. Kishton, J. M., "Unidimensional versus domain representative Parceling of questionnaire Items : An empirical example", Educational and Psychological Measurement, 54, pp.757~765 (1994)
  43. Kline, R. B., "Principles and practice of structural equation modeling" (2011), The Guildford Press
  44. Landis, R. S., "A comparison of approaches to forming composite measures in structural equation models", Organizational Research Method, 3, pp.186~207 (2000)
  45. Little, T. D., "To parcel or not to parcel : Exploring the question, weighting the merits", Structural Equation Modeling, 92(2), pp.151~173 (2002)
  46. Little, T. D., "Why the items versus parcels controversy needn’t be one", Psychological Methods, 18(3), pp.285~300 (2013)
  47. MacCallum, R. C., "Applications of structural equation modeling in psychological research", Annual Review of Psychology, 51, pp.201~226 (2000)
  48. MacCallum, R. C., "Power analysis and determination of sample size for covariance structure modeling", Psychological Methods, 1, pp.130~149 (1996)
  49. MacCallum, R. C., "The problem of equivalent models in applications of covariance structure analysis", Psychological Bulletin, 114, pp.185~199 (1993)
  50. MacCallum, R. C., "Sample size in factor analysis", Psychological Methods, 4(1), pp.84~99 (1999)
  51. Marsh, H. W., "Is more ever too much? The number of indicators per factor in confirmatory factor analysis", Multivariate Behavioral Research, 33, pp.181~220 (1998)
  52. Matsunaga, M., "Item parceling in structural equation modeling : A primer", Communication Method and Measures, 2(4), pp.260~293 (2008)
  53. Mayer, J. D., "Handbook of intelligence", pp.396~420 (2000), Cambridge University Press
  54. Mayer, J. D., "Relation of an ability measure of emotional intelligence to personality", Journal of Personality Assessment, 79(2), pp.306~320 (2002)
  55. Muthen, B., "A comparison of some methodologies for the factor analysis of non normal Likert variables : A note on the Size of the Model", British Journal of Mathematical and Statistical Psychology, 45(1), pp.19~30 (1992)
  56. Muthen, L. K., "How to use a Monte Carlo study to decide on sample size and determine power", Structural Equation Modeling, 9, pp.599~620 (2002)
  57. Nasser, F., "A Monte Carolo study investigating the impact of item parceling on measures of fit of the in confirmatory factor analysis", Educational and Psychological Measurement, 63, pp.729~757 (2003)
  58. Pintrich, P. R., "Motivational and self-regulated learning components of classroom academic performance", Journal of Educational Psychology, 82, pp.33~40 (1990)
  59. Raykov, T., "Can there be infinitely many models equivalent to a given covariance structure model?", Structural Equation Modeling, 8, pp.142~149 (2001)
  60. Rogers, W. M., "Parameter recovery and model fit using multidimensional composites : A comparison of four empirical parceling algorithms", Multivariate Behavioral Research, 39, pp.379~412 (2004)
  61. Steenkamp, J. E. M., "The use LISREL in validating marketing constructs", International Journal of Research in Marking, 91(8), pp.283~299 (1991)
  62. Stephenson, M. T., "A Monte Carlo simulation of observable versus latent variable structural equation modeling techniques", Communication Research, 30, pp.332~354 (2003)
  63. Thompson, B., "Using item “Testlest”", “Parcels” on confirmatory factor analysis: An example using the PPSDQ-78, 1996
  64. Tucker, L. R., "A reliability coefficient for maximum likelihood factor analysis", Psychometrika, 38, pp.1~10 (1973)
  65. Weinstein, C. E., "Handbook of research on teaching" (1986), Macmillan
  66. West, S. G., "Structural equation modeling: Concepts, issues, and applications", pp.57~75 (1995), Sage. Publications
  67. William, L. J., "Measurement models for linking latent variables and indicators : A review of human resource management research using parcels", Human Resource Management Review, 18, pp.233~242 (2008)
  68. Zimmerman, B. J., "Development of a structured interview for assessing student use of self-regulated learning strategies", American Educational Research Journal, 23, pp.614~628 (1986)

관련 논문AI추천

문항묶음:원리의 이해와 적용김수영, 이지현한국심리학회 · 한국심리학회지:일반2016 · 제35권 제2호구조방정식 모델에서 항목묶음이 인과 모수의 검정과 적합도 평가에 미치는 영향강석후, 조현철한국마케팅과학회 · Journal of Global Scholars of Marketing Science(마케팅과학연구)2007 · 제17권 제3호다차원척도의 총화값 도출 방법의 비교와 평가배세하, 한장희한국상품학회 · 상품학연구2019 · 제37권 제6호범주형 확인적 요인분석 모형의 다집단 확장김수영, 신미미한국심리학회 · 한국심리학회지:일반2020 · 제39권 제2호교육 및 심리검사 자료에 대한 탐색적 요인분석에서 복수요인에 부하되는 문항 활용을 위한 고려 사항 탐색강태훈, 오민아한국교육방법학회 · 교육방법연구2016 · 제28권 제4호다지표 잠재성장모형에서 요인동일성과 요인척도 설정의 문제: 모형 적합도와 모수 추정값에 미치는 영향박송이, 장승민한국심리학회 · 한국심리학회지:일반2018 · 제37권 제1호

이 간행물의 다른 논문 (8건)

공공연구기관 종사자의 개인적 배경, 셀프리더십, 직무환경요인이 창의적 행동에 미치는 영향임걸, 김믿음pp. 237-255성장혼합모형을 적용한 초기 청소년의 공동체의식 유형분류 및 영향요인 검증이정민, 정혜원pp. 257-283교원양성기관에서의 수업설계 교육을 통해 나타난 중등 예비교사의 수업설계 특징 탐색정한호pp. 285-312플립드러닝이 의대생의 학습접근방식에 미치는 영향시지현pp. 347-370대학생이 인식하는 “수업 중 교수-학습 활동 종류와 실행도 및 효과”에 관한 연구김주영, 박인우, 장지윤pp. 371-396
원문 보기