Factor Analysis (original) (raw)

An Exploratory Study of the Three Phases Analysis of Factor

2021

In this study, we examine factor analysis as a multivariate statistical tool, starting from the origin of factor analysis with regards to Spearman's approach of 1904 to the three phases of factor analysis. This is done with a view of determining the similarities and individual contributions of each of the three phases of factor analysis. This was achieved by examining the algorithms used in parameter estimations of the three phases of factor analysis. By inputting data into the algorithms and examining their outcomes and proffering recommendations based on the respective findings.

On the Interpretation of Factor Analysis

The importance of the researcher’s interpretation of factor analysis is illustrated by means of an example. The results from this example appear to be meaningful and easily interpreted. The example omits any measure of reliability or validity. If a measure of reliability had been included, it would have indicated the worthlessness of the results. A survey of 46 recent papers from 6 journals supported the claim that the example is typical, two-thirds of the papers provide no measure of reliability. In fact, some papers did not even provide sufficient information to allow for replication. To improve the current situation some measure of factor reliability should accompany applied studies that utilize factor analysis. Three operational approaches are suggested for obtaining measures of factor reliability: use of split samples, Monte Carlo simulation, and a priori models.

Issues and recommendations in the use of factor analysis

Western Journal of Graduate Research, 1990

It is not uncornmon to encounter tl:e atUtude that staustics are "cut and dry" entiuesrather boringwithout much inherent lnterest. At some levels this may be true, but as one becomes more involved with solving research problems, it becomes clear that statistics are very useful tools. As with a carpenter, the researcher's choice of tools can idluence the quality of his/her work 1n important ways. Computers have made the most powerful muluvariate statlstics available to a wtde range of users and ltre skllls actually required to generate the statistics are mintnal. It ts the process of tnterpreting ttre results that can trouble even the most experienced of researchers.

Application of Factor Analysis

Verma/Sports Research with Analytical Solution Using SPSS®, 2016

The issue of measurement invariance commonly arises in factor-analytic contexts, with methods for assessment including likelihood ratio tests, Lagrange multiplier tests, and Wald tests. These tests all require advance definition of the number of groups, group membership, and offending model parameters. In this paper, we construct tests of measurement invariance based on stochastic processes of casewise derivatives of the likelihood function. These tests can be viewed as generalizations of the Lagrange multiplier test, and they are especially useful for: (1) isolating specific parameters affected by measurement invariance violations, and (2) identifying subgroups of individuals that violated measurement invariance based on a continuous auxiliary variable. The tests are presented and illustrated in detail, along with simulations examining the tests' abilities in controlled conditions.

Application of factor analysis....pdf

Several studies have suggested the efficacy of topological rotation as an adjunct to oblique analytical rotation in attaining improved approximation to maximum simple structure of the factor pattern matrix. Recently, using a higherorder scale factoring of the Objective Motivation Analysis Test (MAT), and the Eight State Questionnaire (8SQ), Boyle(1983) reported a 6.17% increase in the ±.10 hyperplane count after only five Rotoplot cycles. Four of the 11 extracted factors were simplified in line with Thurstone's simple structure requirements.

Indeterminacy problems and the interpretation of factor analysis results

Statistica Neerlandica, 1978

This paper reviews indeterminacy problems for the factor analysis model and their consequences for the interpretation of the results. Two types of indeterminacy are discerned: indeterminacy of the parameters in the model (the number of factors, the specific variances and the factorloadings) and the indeterminacy of the factors, given the parameters in the model. It is argued that parameter indeterminacy is partly to be overcome, provided that a strong underlying theory for the subject matter under research is present. Factor indeterminacy remains a major stumbling-block for the interpretation of results. The GUTTMAN criterion is advocated as a measure of factor indeterminacy.