Behaviormetrika
Online ISSN : 1349-6964
Print ISSN : 0385-7417
ISSN-L : 0385-7417
34 巻 , 2 号
選択された号の論文の4件中1~4を表示しています
Articles
  • Taichi Okumura
    2007 年 34 巻 2 号 p. 79-93
    発行日: 2007年
    公開日: 2008/03/22
    ジャーナル フリー
    A new simulation-based approach to sample size determination for hierarchical linear models (HLM) which uses preliminary data is proposed. This approach acknowledges uncertainty in parameter values associated with the estimates obtained from a preliminary data. Taking samples repeatedly, via computer simulation, from the posterior predictive distribution for future observations, we can estimate statistical power and the mean range of confidence interval (CI) numerically, and find sample size with which a desired level of the power or the mean range of CI is achieved. The proposed approach is applicable to any specific model in HLM, as long as a computer program is appropriately adapted to the model.
  • Heungsun Hwang, Yoshio Takane, Naresh Malhotra
    2007 年 34 巻 2 号 p. 95-109
    発行日: 2007年
    公開日: 2008/03/22
    ジャーナル フリー
    Generalized structured component analysis has been proposed as an alternative to partial least squares for path analysis with latent variables. In practice, observed and latent variables may often be hierarchically structured in that their individual-level scores are grouped within higher-level units. The observed and latent variable scores nested within the same higher-level group are likely to be more similar than those in different groups, thereby giving rise to the interdependence of the scores within the same group. Unless this interdependence is taken into account, obtained solutions are likely to be biased. In this paper, generalized structured component analysis is extended so as to account for the nested structures of both observed and latent variables. An alternating least-squares procedure is developed for parameter estimation. An empirical application concerning the measurements of customer-level customer satisfaction nested within different companies is presented to illustrate the usefulness of the proposed method.
  • Kazuhisa Takemura, Marcus Selart
    2007 年 34 巻 2 号 p. 111-130
    発行日: 2007年
    公開日: 2008/03/22
    ジャーナル フリー
    The present study examined the influence of information search constraints both on the information search pattern and on the perceived inner states during the decision making process. We arranged the following three information search constraints conditions: (1) An upper-limited-search (UL) condition in which a decision maker could not examine the same piece of information for the decision task more than once, (2) A lower-limited-search (LL) condition in which a decision maker had to examine every piece of information for the decision task more than once, and (3) A non-limited-search (NL) condition in which a decision maker could examine any number of information. Participants consisted of 76 female and male university students, which were randomly assigned into one out of three conditions. In line with the simplifying and the mobilizing hypotheses, the participants in the UL condition more often used non-compensatory simplifying decision strategies and more slowly checked for information than participants in the LL and NL conditions.
  • Kojiro Shojima, Tatsuo Otsu, Shin-ichi Mayekawa, Masaaki Taguri, Haruo ...
    2007 年 34 巻 2 号 p. 131-156
    発行日: 2007年
    公開日: 2008/03/22
    ジャーナル フリー
    We analyzed data from the National Center Test for University Admissions (NCT) administered in January 2005 by applying three different factor analysis (FA) models: an exploratory FA model, a confirmatory FA model, and a hierarchical FA model. The data was collected from 385,494 students and included 17 variables of the 15 principal subjects.
    Two difficulties were experienced in applying FA models to the NCT data: structural and nonstructural missing data patterns. The structural difficulty is derived from the administration schedule, and the non-structural difficulty is caused by the a la carte system of the NCT. Consequently, very complicated missing data patterns exist in the NCT data. We solved the problems of the missing data patterns by using the pseudo-maximum likelihood method and the full-information maximum likelihood method.
    We extracted two factors by using the exploratory FA model. One factor was for linguistic and social studies, and the other was for mathematics and sciences. These factors were then examined by using the confirmatory FA model. We then confirmed the strong influence of the general factor by using the hierarchical FA model. Furthermore, we performed a multi-group analysis on the confirmatory and hierarchical FA models, focusing on the distinction of sex.
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