On the Interpretation of Factor Analysis

  title={On the Interpretation of Factor Analysis},
  author={J. Scott Armstrong and Peer Soelberg},
  journal={Econometrics eJournal},
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… 
On the Extraction of Components and the Applicability of the Factor Model
It is well known that the routine use of principal component analysis with correlation matrices may cause problems in the interpretation of results. Armstrong and Soelberg (1968) illustrated how one
Evaluating the use of exploratory factor analysis in psychological research.
Despite the widespread use of exploratory factor analysis in psychological research, researchers often make questionable decisions when conducting these analyses. This article reviews the major
Although factor analysis has been a major contributing factor in advancing psychological research, a systematic assessment of how it has been applied is lacking. For this review we examined the
A Simulation of Factor Analytic Reliability Varying Sample Size and Number of Variables
An examination of four factor analytic models employing random sampling experiments is undertaken using a methodology and hypothetical population factor structure first employed by Browne (2). The
Assessing Sampling Variation Relative to Number-of-Factors Criteria
Employment of the bootstrap method to approximate the sampling variation of eigenvalues is explicated, and its usefulness is amplified by an illustration in conjunction with two commonly used
The Effect of Common Variance and Structure Pattern on Random Data Eigenvalues: Implications for the Accuracy of Parallel Analysis
Selecting the correct number of factors to retain in a factor analysis is a crucial step in developing psychometric tools or developing theories. The present study assessed the accuracy of parallel
An Empirical Comparison of Two Methods for Testing the Significance of a Correlation Matrix
The present paper reports on a "Monte Carlo" comparison of two methods of testing the significance of a correlation matrix to determine the suitability and appropriateness of factor analysis.
An Empirical Test of the Utility of the Observations-To-Variables Ratio in Factor and Components Analysis
Many researchers have proposed a minimum ratio of observations to variables or an absolute minimum of observations in order to obtain stable factor config urations. However, hardly any empirical
Using Bartlett's Significance Test to Determine the Number of Factors to Extract
ranted. Successive application of the test of residuals after each factor is extracted provides a basis for determining the number of significant components; the extraction process is continued until
Treating factor interpretations as hypotheses
Factor analysis is used extensively in the construction of psychometric scales. It may appear to be a mathematically precise and objective technique, but it involves many subjective choices and its


On Subjectivity in Factor Analysis
ROTATION to simple structure in factor analysis was devised by Thurstone (1935) as a means for solving the indeterminacy problem. I n the usual factor analysis based upon a matrix of
The Application of Electronic Computers to Factor Analysis
A survey of available computer programs for factor analytic computations and a analysis of the problems of the application of computers to factor analysis.
Some necessary conditions for common-factor analysis
LetR be any correlation matrix of ordern, with unity as each main diagonal element. Common-factor analysis, in the Spearman-Thurstone sense, seeks a diagonal matrixU2 such thatG = R − U2 is Gramian
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A Million Random Digits with 100,000 Normal Deviates.
Psychological measurement a hundred and twenty-five years later
The study of sampling errors in factor analysis by means of artificial experiments.