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**Factor Analysis with SAS**

Karl L. Wuensch Dept of Psychology East Carolina University

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What is a Common Factor? It is an abstraction, a hypothetical construct that affects at least two of our measurement variables. We want to estimate the common factors that contribute to the variance in our variables. Is this an act of discovery or an act of invention?

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What is a Unique Factor? It is a factor that contributes to the variance in only one variable. There is one unique factor for each variable. The unique factors are unrelated to one another and unrelated to the common factors. We want to exclude these unique factors from our solution.

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**Iterated Principal Factors Analysis**

The most common type of FA. Also known as principal axis FA. We eliminate the unique variance by replacing, on the main diagonal of the correlation matrix, 1’s with estimates of communalities. Initial estimate of communality = R2 between one variable and all others.

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FactBeer.sas Download FactBeer.sas from . Bring it into SAS. Edit the FILE statement in the second DATA step so that it points to a folder on your computer. Run the program. Look at the output

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**Look at the Initial Communalities**

Page 1 of the output. For each variable, these are the R2 predicting that variable from all others. They sum to We have eliminated 7 – = units of unique variance.

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**Iterate! Using the estimated communalities, obtain a solution.**

Take the communalities from the first solution and insert them into the main diagonal of the correlation matrix. Solve again. Take communalities from this second solution and insert into correlation matrix.

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Solve again. Repeat this, over and over, until the changes in communalities from one iteration to the next are trivial. Our final communalities sum to 5.6. After excluding 1.4 units of unique variance, we have extracted 5.6 units of common variance. That is 5.6 / 7 = 80% of the total variance in our seven variables.

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Final Communalities

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**We have packaged those 5.6 units of common variance into two factors (page 4 of output):**

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**Reproduced and Residual Correlation Matrices**

Correlations between variables result from their sharing common underlying factors. Try to reproduce the original correlation matrix from the correlations between factors and variables (the loadings). The difference between the reproduced correlation matrix and the original correlation matrix is the residual matrix.

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**We want these residuals to be small, and they are.**

Look at “Residual Correlations With Uniqueness on the Diagonal” on page 2 of the output Uniqueness = 1 – Commonality. The Root Mean Squares show no problems with any of the variables. Reputation does have high uniqueness.

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**Partial Correlations Controlling Factors**

Tells us for each pair of variables how much variance they share that has NOT been captured by the factors. See page 3 of the output. Note that Size and Color share a good bit of variance that was not captured by the factors.

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**Nonorthogonal (Oblique) Rotation**

The axes will not be perpendicular, the factors will be correlated with one another. the factor loadings (in the pattern matrix) will no longer be equal to the correlation between each factor and each variable. They will still equal the beta weights, the A’s in

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**Oblique solutions make me uncomfortable.**

but I did one just for you – a Promax rotation. First a Varimax rotation is performed. Then the axes are rotated obliquely. See the output in the handout, about page 5.

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Exact Factor Scores You can compute, for each subject, estimated factor scores. SAS gives you the standardized scoring coefficients used in computing factor scores. You must use NFACTORS= to specify the number of factors to retain. See page 5 of the output.

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**Standardized Scoring Coefficients**

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**Computing Factor Scores**

Multiply each standardized variable score by the corresponding standardized scoring coefficient. For our first subject, Factor 1 = (-.294)(.41) + (.955)(.40) + (-.036)(.22) + (1.057)(-.07) + (.712)(.04) + (1.219)(.03) + (-1.14)(.01) = 0.23. What a pain !

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**Outputting the Factor Scores**

OUT=name will create a new data set with all of the original variables and with factor scores You can PUT the ones you wish to save in a plain text data file. Take a look at the FactBeer.dat file which was written to your computer. Later we shall use this file for additional analysis.

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**R2 of the Variables With Each Factor**

See page 5 of the output. These statistics are equal to the variance of the factor scores.

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True Factor Scores Now imagine that there exist two true factors which are the cause of our observed scores. The factors that we have created are just estimates of these true factors. Are they good estimates? Are they well related to the observed variables? With values of .97 and .95, I’d say yes.

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**Unit-Weighted Factor Scores**

Define subscale 1 as simple sum or mean of scores on all items loading well (> .4) on Factor 1. Likewise for Factor 2, etc. Suzie Cue’s answers are Color, Taste, Aroma, Size, Alcohol, Cost, Reputation 80, 100, 40, 30, 75, 60, Aesthetic Quality = = 210 Cheap Drunk = = 155

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It may be better to use factor scoring coefficients (rather than loadings) to determine unit weights. Grice (2001) evaluated several techniques and found the best to be assigning a unit weight of 1 to each variable that has a scoring coefficient at least 1/3 as large as the largest for that factor. Using this rule, we would not include Reputation on either subscale and would drop Cost from the second subscale.

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**Item Analysis and Cronbach’s Alpha**

Are our subscales reliable? Test-Retest reliability Cronbach’s Alpha – internal consistency Mean split-half reliability With correction for attenuation Is a conservative estimate of reliability

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**AQ = Color + Taste + Aroma – Reputation**

Is the AQ subscale reliable? Must negatively weight Reputation prior to item analysis. DATA alpha; SET drinkme; NegRep = -1*reputat; Look at the output, page 6.

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**Shoot for an alpha of at least .70 for research instruments.**

Our alpha, .88, is excellent. But reputation has a low item-total correlation. Removing it would raise alpha to .96.

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**Comparing Two Groups’ Factor Structure**

Eyeball Test Same number of well defined factors in both groups? Same variables load well on same factors in both groups?

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Pearson r Just correlate the loadings for one factor in one group with those for the corresponding factor in the other group. If there are many small loadings, r may be large due to the factors being similar on small loadings despite lack of similarity on the larger loadings.

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**CC, Tucker’s coefficient of congruence**

Follow the instructions in the document Comparing Two Groups’ Factor Structures: Pearson r and the Coefficient of Congruence CC of .85 to .94 corresponds to similar factors, and .95 to 1 as essentially identical factors.

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**Cross-Scoring Obtain scoring coefficients for each group.**

For each group, compute factor scores using coefficients obtained from the analysis for that same group (SG) and using coefficients obtained from the analysis for the other group (OG). Correlate SG factor scores with OG factor scores.

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**Catell’s Salient Similarity Index**

Factors (one from one group, one from the other group) are compared in terms of similarity of loadings. Catell’s Salient Similarity Index, s, can be transformed to a p value testing the null that the factors are not related to one another. See my document Cattell’s s for details.

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**Required Number of Subjects and Variables**

Rules of Thumb (not very useful) 100 or more subjects. at least 10 times as many subjects as you have variables. as many subjects as you can, the more the better. It depends – see the references in the handout.

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**Start out with at least 6 variables per expected factor.**

Each factor should have at least 3 variables that load well. If loadings are low, need at least 10 variables per factor. Need at least as many subjects as variables. The more of each, the better. When there are overlapping factors (variables loading well on more than one factor), need more subjects than when structure is simple.

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**If communalities are low, need more subjects.**

If communalities are high (> .6), you can get by with fewer than 100 subjects. With moderate communalities (.5), need subjects. With low communalities and only 3-4 high loadings per factor, need over 300 subjects. With low communalities and poorly defined factors, need over 500 subjects.

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**Use the FactBeer.dat File**

Download Factor-MR-T.sas from my SAS programs page. Edit the Infile statement so it correctly points to factbeer.data on your computer. Run it. Look at the output.

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**Multiple Regression: Factor Scores as Predictors**

Notice that the factor scores, AesthetQ and CheapDr, are standardized to mean 0, variance 1. Notice that they are not correlated with one another. We certainly can predict SES will from these two factors !

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**T-Test on Factor Scores**

We compare two groups of students. One group is students at Pitt Community College. The other is graduate students at ECU. Notice that they differ significantly on both factors.

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