Modélisation de la structure de la covariance sous Windows: Une analyse multitratsmultiméthodea utilisant Amos, Eqs, et Lisrel. Cet article étudie les trois grands logiciels d'analyse de la structure de la covariance sous Windows. Le point essentiel de la comparaison réside en une analyse d'une matrice 5x5 multitraits-multiméthodes. On montre ainsi que la possibilité de différents fonctions d'ajustement et de procédures bootstrap dans les programmes actuels d'analyse de la structure de la covariances permettent une analyse fine de données à problèmes.
The authors of this article, who are also the guest editors for this issue on multilevel analysis, give an overview and brief history of multilevel analysis and present the following four research articles. Multilevel Analysis - Overview. Methods. History.
This study applies ordinal confirmatory factor analysis for multiple groups to assess equivalence of scale, random errors and systematic (nonrandom) errors of attitudinal questions surveyed on rating scales under different survey modes (Face-to-Face [F2F], Telephone, Paper, and Web). Empirical findings from a large-scale experiment are presented. Consistent with theoretical expectations, interviewer- and self-administered surveys measured all assessed questions on systematically different scales, with different systematic bias, and with differing extents of random error. These measurement effects were absent when comparing Paper with Web or F2F with Telephone. It is concluded that modes impact primarily systematic measurement effects affecting multiple items equally. Interviewer- and self-administered modes should only be combined with great care in mixed-mode surveys that focus on attitudinal constructs. Combining Paper and Web or Telephone and F2F are the viable options. Thereby choosing the self-administered modes appears more efficient, because these modes exhibited higher indicator reliabilities (smaller random error) than the interviewer modes.
A multilevel problem concerns a population with a hierarchical structure. A sample from such a population can be described as a multistage sample. First, a sample of higher level units is drawn (e.g. schools or organizations), and next a sample of the sub‐units from the available units (e.g. pupils in schools or employees in organizations). In such samples, the individual observations are in general not completely independent. Multilevel analysis software accounts for this dependence and in recent years these programs have been widely accepted. Two problems that occur in the practice of multilevel modeling will be discussed. The first problem is the choice of the sample sizes at the different levels. What are sufficient sample sizes for accurate estimation? The second problem is the normality assumption of the level‐2 error distribution. When one wants to conduct tests of significance, the errors need to be normally distributed. What happens when this is not the case? In this paper, simulation studies are used to answer both questions. With respect to the first question, the results show that a small sample size at level two (meaning a sample of 50 or less) leads to biased estimates of the second‐level standard errors. The answer to the second question is that only the standard errors for the random effects at the second level are highly inaccurate if the distributional assumptions concerning the level‐2 errors are not fulfilled. Robust standard errors turn out to be more reliable than the asymptotic standard errors based on maximum likelihood.
The effectiveness of using a "not selling anything" introduction in encouraging people to participate in random sampling household telephone surveys is studied. Twenty-nine split run experiments conducted by multiple Dutch market research firms were performed to ascertain the efficacy of standard & nonsolicitation introductions & whether the latter introductions increased participant gain. The findings revealed that the use of the "not selling anything" introduction increased participant response rates by 2% regardless of the topic of the household telephone survey. Although the data analysis demonstrated that placement of the nonsolicitation technique at the beginning of an introduction provided the best participant response rate, it is revealed that such introduction methods worked equally well for general & special groups. The use of nonsolicitation introductions in carrying out household telephone interviews is also recommended since "not selling anything" introductions are easy & inexpensive to implement. 2 Tables, 31 References. J. W. Parker
Détecter des formes de réponses aberrantes sur des échelles multi-réponses: Des indices non-paramétriques et un logiciel. La littérature de psychologie expérimentale comprend plusieurs indices de mesurer censés identifier des formes de réponses aberrantes. Cet article examine trois indices non-paramétriques bien connus, donne des exemples d'application et présente un logiciel qui calcule ces indices.
This special issue of SMR is about the analysis of data collected at different levels of observation, such as groups and individuals within these groups, and about the methodological problems that are present when natural experimentation and observations nested within existing social groups are the object of study. The methodological problems are summarized in the term multilevel problems. A multilevel problem is a problem that inquires into the relationships between a set of variables that are measured at a number of different levels of a hierarchy. This article discusses some traditional approaches to the analysis of multilevel data and their statistical shortcomings. The random coefficient linear model is presented, which resolves many of these problems, and the currently available software is discussed. Next, some more general developments in multilevel modeling are discussed. The authors end with an overview of this special issue.
An important decision in online and mixed-mode questionnaire design is if and how to include a "do-not-know" (DK) option. Mandatory response is often a default option, but methodologists have advised against this. Several solutions for the DK category are suggested. These include (1) not explicitly offering a DK, but skipping questions is allowed, (2) explicitly offering a DK option with visual separation from the substantive responses, and (3) using the interactivity of the web to emulate interviewer probing after a DK answer. To test these solutions, experimental data were collected in a probability based online panel. Not offering DK, but allowing respondents to skip questions, followed by a polite probe when skips occurred, resulted in the lowest amount of missing information. To assess the effect of probing across different modes, a second experiment was carried out that compared explicitly and implicitly offering the DK option for web and telephone surveys.
