In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory and confirmatory analysis, the distinction between complementary approaches for theory testing versus predictive application, and some developments in estimation methods also are discussed.

/pdf/structural-equation-modeling-in-practice-a-review-and-1ue2omj492.pdf

Structural equation modeling in practice: a review and recommended two-step approach

When large multivariate datasets are analyzed, it is often desirable to reduce their dimensionality. Principal component analysis is one technique for doing this. It replaces the p original variables by a smaller number, q, of derived variables, the principal components, which are linear combinations of the original variables. Often, it is possible to retain most of the variability in the original variables with q very much smaller than p. Despite its apparent simplicity, principal component analysis has a number of subtleties, and it has many uses and extensions. A number of choices associated with the technique are briefly discussed, namely, covariance or correlation, how many components, and different normalization constraints, as well as confusion with factor analysis. Various uses and extensions are outlined.


Keywords:

dimension reduction;
factor analysis;
multivariate analysis;
variance maximization

Principal Component Analysis

(1966). The Scree Test For The Number Of Factors. Multivariate Behavioral Research: Vol. 1, No. 2, pp. 245-276.

The scree test for the number of factors

This survey provides an overview of higher-order tensor decompositions, their applications, and available software. A tensor is a multidimensional or $N$-way array. Decompositions of higher-order tensors (i.e., $N$-way arrays with $N \geq 3$) have applications in psycho-metrics, chemometrics, signal processing, numerical linear algebra, computer vision, numerical analysis, data mining, neuroscience, graph analysis, and elsewhere. Two particular tensor decompositions can be considered to be higher-order extensions of the matrix singular value decomposition: CANDECOMP/PARAFAC (CP) decomposes a tensor as a sum of rank-one tensors, and the Tucker decomposition is a higher-order form of principal component analysis. There are many other tensor decompositions, including INDSCAL, PARAFAC2, CANDELINC, DEDICOM, and PARATUCK2 as well as nonnegative variants of all of the above. The N-way Toolbox, Tensor Toolbox, and Multilinear Engine are examples of software packages for working with tensors.

/pdf/tensor-decompositions-and-applications-46vl2ih5gn.pdf

Tensor Decompositions and Applications

This study investigated the relation of the “Big Five” personality dimensions (Extraversion, Emotional Stability, Agreeableness, Conscientiousness, and Openness to Experience) to three job performance criteria (job proficiency, training proficiency, and personnel data) for five occupational groups (professionals, police, managers, sales, and skilled/semi-skilled). Results indicated that one dimension of personality, Conscientiousness, showed consistent relations with all job performance criteria for all occupational groups. For the remaining personality dimensions, the estimated true score correlations varied by occupational group and criterion type. Extraversion was a valid predictor for two occupations involving social interaction, managers and sales (across criterion types). Also, both Openness to Experience and Extraversion were valid predictors of the training proficiency criterion (across occupations). Other personality dimensions were also found to be valid predictors for some occupations and some criterion types, but the magnitude of the estimated true score correlations was small (ρ < .10). Overall, the results illustrate the benefits of using the 5-factor model of personality to accumulate and communicate empirical findings. The findings have numerous implications for research and practice in personnel psychology, especially in the subfields of personnel selection, training and development, and performance appraisal.

The big five personality dimensions and job performance: a meta-analysis

http://psycnet.apa.org/journals/edu/54/1/1.pdf

Theory of fluid and crystallized intelligence: A critical experiment.

analysis of a certain other primary drive. The serious researcher may discover something that he does not yet know, but that is not too likely. The obese will not discover the 'cure' that has so far eluded them. But the curious reader, he who wishes to gain a fairly detailed understanding of what 'really' drives him to or from the dinner table, and the aspiring researcher who craves an area abounding with hypotheses begging to be tested these will both find highly palatable food for thought. C. Peter Herman, Northwestern University

Abilities: their structure, growth, and action.

Abilities: Their Structure, Growth, and Action

Download PDF Ebook and Read OnlineThe Scientific Use Of Factor Analysis In Behavioral And Life Sciences%0D. Get The Scientific Use Of Factor Analysis In Behavioral And Life Sciences%0D Well, publication the scientific use of factor analysis in behavioral and life sciences%0D will make you closer to exactly what you are eager. This the scientific use of factor analysis in behavioral and life sciences%0D will be consistently good buddy at any time. You could not forcedly to always complete over reviewing a publication basically time. It will be simply when you have leisure and spending few time to make you really feel pleasure with just what you read. So, you could get the meaning of the notification from each sentence in guide. the scientific use of factor analysis in behavioral and life sciences%0D. In what situation do you like reviewing a lot? What concerning the kind of the book the scientific use of factor analysis in behavioral and life sciences%0D The should read? Well, everyone has their own reason should read some books the scientific use of factor analysis in behavioral and life sciences%0D Primarily, it will relate to their need to obtain expertise from the publication the scientific use of factor analysis in behavioral and life sciences%0D as well as want to check out simply to get home entertainment. Books, story book, and various other enjoyable books end up being so preferred today. Besides, the scientific e-books will also be the very best need to select, especially for the pupils, educators, physicians, entrepreneur, and also other professions that enjoy reading. Do you recognize why you must read this site and also just what the relation to checking out book the scientific use of factor analysis in behavioral and life sciences%0D In this contemporary age, there are numerous methods to get guide and they will be considerably less complicated to do. One of them is by obtaining the publication the scientific use of factor analysis in behavioral and life sciences%0D by online as just what we tell in the web link download. Guide the scientific use of factor analysis in behavioral and life sciences%0D can be a choice because it is so appropriate to your necessity now. To obtain guide online is really simple by simply downloading them. With this chance, you could read the publication wherever as well as whenever you are. When taking a train, hesitating for checklist, and awaiting a person or other, you could review this on the internet e-book the scientific use of factor analysis in behavioral and life sciences%0D as a good close friend again.

Raymond B. Cattell

Papers

The scree test for the number of factors

Theory of fluid and crystallized intelligence: A critical experiment.

Abilities: their structure, growth, and action.

Abilities: Their Structure, Growth, and Action

The Scientific Use of Factor Analysis in Behavioral and Life Sciences