16000統計を深く知る 平均値versus中央値 : 代表値を巡って

vol.67, no.1, pp.42-47, 2016-01

5000OA構造方程式モデリングは,因子分析,分散分析,パス解析のすべてにとって代わるのか?(<特集>討論:共分散構造分析)

vol.29, no.2, pp.138-159, 2002-12-25

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It is well-known that structural equation modeling (SEM) can represent a variety of traditional multivariate statistical models. This fact does not necessarily mean that SEM should be used for the traditional models. It is often said that a general model is more difficult to handle than a specific model developed for a given situation. In this paper, we shall clarify relative advantages between SEM and several traditional statistical models. Rather than comparison in mathematical properties, we shall discuss how and when SEM outperforms corresponding traditional models in practical situations. Special attention is paid to statistical analysis of a scale score, the sum of indicator variables determined by factor analysis. In particular, we shall study relative advantages between (i) confirmatory factor analysis and exploratory factor analysis, (ii) multiple indicator analysis and correlational and regression analysis of scale scores, (iii) analysis of factor means and analysis of variance of scale scores, and (iv) path analysis and multiple regression analysis.

2000OA構造方程式モデリングは,因子分析,分散分析,パス解析の すべてにとって代わるのか?

vol.29, no.2, pp.138-159, 2002 (Released:2009-04-07)

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It is well-known that structural equation modeling (SEM) can represent a variety of traditional multivariate statistical models. This fact does not necessarily mean that SEM should be used for the traditional models. It is often said that a general model is more difficult to handle than a specific model developed for a given situation. In this paper, we shall clarify relative advantages between SEM and several traditional statistical models. Rather than comparison in mathematical properties, we shall discuss how and when SEM outperforms corresponding traditional models in practical situations. Special attention is paid to statistical analysis of a scale score, the sum of indicator variables determined by factor analysis.In particular, we shall study relative advantages between (i) confirmatory factor analysis and exploratory factor analysis, (ii) multiple indicator analysis and correlational and regression analysis of scale scores, (iii) analysis of factor means and analysis of variance of scale scores, and (iv) path analysis and multiple regression analysis.

1000OA再討論:誤差共分散の利用と特殊因子の役割(<特集>討論:共分散構造分析)

vol.29, no.2, pp.182-197, 2002-12-25

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In this rejoinder, special attentions are paid to error covariances and specific factors in the comparison between SEM and traditional methods. When a factor analysis model receives a poor fit, it does not make sense to simply remove important variables although inconsistent with the factor analysis model, as pointed out by the discussants. It is to be emphasized that a better way than removing the variables is to allow for error covariances, in order to overcome the inconsistency problem. The model with error covariances guarantees the invariance of estimation results over item selection. The discussants pointed out that an important difference between a scale score (sum of items) and a measurement model by effect indicators in SEM is that a scale score includes specific factors whereas a measurement model excludes them. Practitioners could use scale scores when they are interested in effects of specific factors as well as a common factor. It is argued, however, that appropriate use of error terms and a common factor in SEM can make better inference than the use of unidimensional scale scores, because the error terms of effect indicators contain information on specific factors and they can individually evaluate the effects of the common factor and the specific factors in SEM. Other related topics are also discussed.

1000OA不適解の原因と処理 :探索的因子分析

vol.24, pp.303-327, 1998-03