Reliability indicates the internal consistency of a test. In educational studies, reliability is a key feature for a test. Researchers have proposed many traditional reliability estimates, such as coefficient alpha and coefficient omega. However, traditional reliability indices do not deal with the data hierarchy, even though the multilevel…

The primary objective of this study was to examine the effect of missing data on goodness of fit statistics in confirmatory factor analysis (CFA). For this aim, four missing data handling methods; listwise deletion, full information maximum likelihood, regression imputation and expectation maximization (EM) imputation were examined in terms of…

Utilizing planned missing data (PMD) designs (ex. 3-form surveys) enables researchers to ask participants fewer questions during the data collection process. An important question, however, is just how few participants are needed to effectively employ planned missing data designs in research studies. This article explores this question by using…

The study aims to gain a better understanding of the national large-scale curriculum process in terms of the used implementation strategies, the function of the reform, and the curriculum coherence perceived by the stakeholders accountable in constructing the national core curriculum in Finland. A large body of school reform literature has shown…

In this study, we reanalyzed the classic Hawai'i Family Study of Cognition (HFSC) data using contemporary multilevel modeling techniques. We used the HFSC baseline data ("N" = 6,579) and reexamined the factorial structure of 16 cognitive variables using confirmatory (restricted) measurement models in an explicit sequence. These models…

Normal theory maximum likelihood (ML) is by far the most popular estimation and testing method used in structural equation modeling (SEM), and it is the default in most SEM programs. Even though this approach assumes multivariate normality of the data, its use can be justified on the grounds that it is fairly robust to the violations of the…

This article introduces a bootstrap generalization to the Modified Parallel Analysis (MPA) method of test dimensionality assessment using factor analysis. This methodology, based on the use of Marginal Maximum Likelihood nonlinear factor analysis, provides for the calculation of a test statistic based on a parametric bootstrap using the MPA…

A jackknife-like procedure is developed for producing standard errors of estimate in maximum likelihood factor analysis. Unlike earlier methods based on information theory, the procedure developed is computationally feasible on larger problems. Examples are given to demonstrate the feasibility of the method. (Author/JKS)

In maximum likelihood factor analysis, there arises a situation whereby improper solutions occur. The causes of those improper solution are discussed and illustrated. (JKS)

Factor analysis, image analysis, and principal component analysis are compared with respect to the factor patterns they would produce under various conditions. The general conclusion that is reached is that the three methods produce results that are equivalent. (Author/JKS)

Three alternative estimation procedures for factor analysis based on the instrumental variables method are presented. Least squares estimation procedures are compared to maximum likelihood procedures. The conclusion, based on the data used in this study, is that two of the procedures seem to work well. (Author/JKS)

Nonlinear common factor models with polynomial regression functions, including interaction terms, are fitted by simultaneously estimating the factor loadings and common factor scores, using maximum likelihood and least squares methods. A Monte Carlo study gives support to a conjecture about the form of the distribution of the likelihood ratio…

A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…

This paper examines the construct generality of five self-report measures of anxiety across male and female samples, and illustrates the use of confirmatory maximum likelihood techniques for examining factorial invariance. (Author/BW)