1. Principal Components An Introduction exploratory factoring meaning & application of “principal components” Basic steps in a PC analysis PC extraction process # PCs determination Statistical approaches Mathematical approaches “Nontrivial factors” approaches

2. Exploratory vs. Confirmatory Factoring Exploratory Factoring – when we do not have RH: about... the number of factors what variables load on which factors we will “explore” the factor structure of the variables, consider multiple alternative solutions, and arrive at a post hoc solution Weak Confirmatory Factoring – when we have RH: about the # factors and factor memberships we will “test” the proposed weak a priori factor structure Strong Confirmatory Factoring – when we have RH: about relative strength of contribution to factors by variables we will “test” the proposed strong a priori factor structure

3. Meaning of “Principal Components” “Component” analyses are those that are based on the “full” correlation matrix 1.00s in the diagonal yep, there’s other kinds, more later “Principal” analyses are those for which each successive factor... accounts for maximum available variance is orthogonal (uncorrelated, independent) with all prior factors full solution (as many factors as variables) accounts for all the variance

4. Applications of PC analysis Components analysis is a kind of “data reduction” start with an inter-related set of “measured variables” identify a smaller set of “composite variables” that can be constructed from the “measured variables” and that carry as much of their information as possible A “Full components solution”... has as many PCs as variables accounts for 100% of the variables’ variance each variable has a final communality of 1.00 – all of its variance is accounted for by the full set of PCs A “Truncated components solution” … has fewer PCs than variables accounts for <100% of the variables’ variance each variable has a communality < 1.00 -- not all of its variance is accounted for by the PCs

5. The basic steps of a PC analysis Compute the correlation matrix Extract a full components solution Determine the number of components to “keep” total variance accounted for variable communalities “Rotate” the components and “interpret” (name) them Structure weights > |.3|-|.4| define which variables “load” Compute “component scores” “Apply” components solution theoretically -- understand meaning of the data reduction statistically -- use the component scores in other analyses interpretability replicability

6. PC Factor Extraction Extraction is the process of forming PCs as linear combinations of the measured variables PC 1 = b 11 X 1 + b 21 X 2 + … + b k1 X k PC 2 = b 12 X 1 + b 22 X 2 + … + b k2 X k PC f = b 1f X 1 + b 2f X 2 + … + b kf X k Here’s the thing to remember… We usually perform factor analyses to “find out how many groups of related variables there are” … however … The mathematical goal of extraction is to “reproduce the variables’ variance, efficiently”

7. PC Factor Extraction, cont. Consider R on the right Obviously there are 2 kinds of information among these 4 variables X 1 & X 2 X 3 & X 4 Looks like the PCs should be formed as, X 1 X 2 X 3 X 4 X 1 1.0 X 2.7 1.0 X 3.3.3 1.0 X 4.3.3.5 1.0 PC 1 = b 11 X 1 + b 21 X 2 -- capturing the information in X 1 & X 2 PC 2 = b 32 X 3 + b 42 X 4 -- capturing the information in X 3 & X 4 But remember, PC extraction isn’t trying to “group variables” it is trying to “reproduce variance” notice that there are “cross correlations” between the “groups” of variables !!

8. PC Factor Extraction, cont. So, because of the cross correlations, in order to maximize the variance reproduced, PC1 will be formed more like... PC 1 =.5X 1 +.5X 2 +.4X 3 +.4X 4 Notice that all the variables contribute to defining PC 1 Notice the slightly higher loadings for X 1 & X 2 Because PC 1 didn’t focus on the X 1 & X 2 variable group or X 3 & X 4 variable group, there will still be variance to account for in both, and PC 2 will be formed, probably something like … PC 2 =.3X 1 +.3X 2 -.4X 3 -.4X 4 Notice that all the variables contribute to defining PC 2 Notice the slightly higher loadings for X 3 & X 4

9. PC Factor Extraction, cont. While this set of PCs will account for lots of the variables’ variance -- it doesn’t provide a very satisfactory interpretation PC 1 has all 4 variables loading on it PC 2 has all 4 variables loading on it and 2 of then have negative weights, even though all the variables are positively correlated with each other The goal here was point out what extraction does (maximize variance accounted for) and what it doesn’t do (find groups of variables)

10. Determining the Number of PCs Determining the number of PCs is arguably the most important decision in the analysis … rotation, interpretation and use of the PCs are all influenced by the how may PCs are “kept” for those processes there are many different procedures available – none are guaranteed to work !! probably the best approach to determining the # of PCS… remember that this is an exploratory factoring -- that means you don’t have decent RH: about the number of factors So … Explore … consider different “reasonable” # PCs and “try them out” rotate, interpret &/or tryout resulting factor scores from each and then decide To get started we’ll use the SPSS “standard” of λ > 1.00

11. Statistical Procedures PC analyses are extracted from a correlation matrix PCs should only be extracted if there is “systematic covariation” in the correlation matrix This is know as the “sphericity question” Note: the test asks if there the next PC should be extracted There are two different sphericity tests Whether there is any systematic covariation in the original R Whether there is any systematic covariation left in the partial R, after a given number of factors has been extracted Both tests are called “Bartlett’s Sphericity Test”

12. Statistical Procedures, cont. Applying Bartlett’s Sphericity Tests Retaining H0: means “don’t extract another factor” Rejecting H0: means “extract the next factor” Significance tests provide a p-value, and so a known probability that the next factor is “1 too many” (a type I error) Like all significance tests, these are influenced by “N” larger N = more power = more likely to reject H0: = more likely to “keep the next factor” (& make a Type I error) Quandary?!? Samples large enough to have a stable R are likely to have “excessive power” and lead to “over factoring” Be sure to consider % variance, replication & interpretability

13. Mathematical Procedures The most commonly applied decision rule (and the default in most stats packages -- chicken & egg ?) is the > 1.00 rule … here’s the logic Part 1 Imagine a spherical R (of k variables) each variable is independent and carries unique information so, each variable has 1/k th of the information in R For a “normal” R (of k variables) each variable, on average, has 1/k th of the information in R

14. Mathematical Procedure, cont. Part 2 The “trace” of a matrix is the sum of its diagonal So, the trace of R (with 1’s in the diag) = k (# vars) tells the amount of variance in R accounted for by each extracted PC for a full PC solution  = k (accounts for all variance) Part 3 PC is about data reduction and parsimony “trading” fewer more-complex things (PCs - linear combinations of variables) for fewer more-simple things (original variables)

15. Mathematical Procedure, cont. Putting it all together (hold on tight !) Any PC with > 1.00 accounts for more variance than the average variable in that R That PC “has parsimony” -- the more complex composite has more information than the average variable Any PC with < 1.00 accounts for less variance than the average variable in that R That PC “doesn’t have parsimony” -- the more complex composite has more no information than the average variable

16. Mathematical Procedure, cont. There have been examinations the accuracy of this criterion The usual procedure is to generate a set of variables from a known number of factors (v k = b 1k *PC 1 + … +b fk *PC f, etc.) --- while varying N, # factors, # PCs & communalities Then factor those variables and see if > 1.00 leads to the correct number of factors Results -- the rule “works pretty well on the average”, which really means that it gets the # factors right some times, underestimates sometimes and overestimates sometimes No one has generated an accurate rule for assessing when which of these occurs But the rule is most accurate with k 300

17. Nontrivial Factors Procedures These “common sense” approaches became increasing common as… the limitations of statistical and mathematical procedures became better known the distinction between exploratory and confirmatory factoring developed and the crucial role of “successful exploring” became better known These procedures are more like “judgement calls” and require greater application of content knowledge and “persuasion”, but are often the basis of good factorings !!

18. Nontrivial factors Procedures, cont. Scree -- the “junk” that piles up at the foot of an glacier a “diminishing returns” approach plot the for each factor and look for the “elbow” “Old rule” -- # factors = elbow (1966; 3 below) “New rule” -- # factors = elbow - 1 (1967; 2 below) # PC 1 2 3 4 5 6 4 2 0 Sometimes there isn’t a clear elbow -- try another “rule” This approach seems to work best when combined with attention to interpretability !!

19. An Example… A buddy in graduate school wanted to build a measure of “contemporary morality”. He started with the “10 Commandments” and the “7 Deadly Sins” and created a 56-item scale with 8 subscales. His scree plot looked like… How many factors? λ 0 1 10 20 1 8 20 40 56 1? – big elbow at 2, so ’67 rule suggests a single factor, which clearly accounts for the biggest portion of variance 7? – smaller elbow at 8, so ’67 rule suggests 7 8? – smaller elbow at 8, ’66 rule gives the 8 he was looking for – also 8 th has λ > 1.0 and 9 th had λ < 1.0 Remember that these are subscales of a central construct, so.. items will have substantial correlations both within and between subscales to maximize the variance accounted for, the first factor is likely to pull in all these inter-correlated variables, leading to a large λ for the first (general) factor and much smaller λs for subsequent factors This is a common scree configuration when factoring items from a multi- subscale scale!

20. Rotation – finding “groups” in the variables Factor Rotations changing the “viewing angle” or “head tilt” of the factor space makes the groupings visible in the graph apparent in the structure matrix Unrotated Structure PC 1 PC 2 V 1.7.5 V 2.6.6 V 3.6 -.5 V 4.7 -.6 PC2 V1 V2 V3 V4 PC1 PC1’ PC2’ Rotated Structure PC 1 PC 2 V 1.7 -.1 V 2.7.1 V 3.1.5 V 4.2.6

21. Interpretation – Naming “groups” in the variables Usually interpret factors using the rotated solutions using the rotated Factors are named for the variables correlated with them Usual “cutoffs” are +/-.3 -.4 So … a variable that shares at least 9-16% of its variance with a factor is used to name that factor Variables may “load” on none, 1 or 2+ factors Rotated Structure PC 1 PC 2 V 1.7 -.1 V 2.7.1 V 3.1.5 V 4.2.6 This rotated structure is easy – PC 1 is V 1 & V 2 PC 2 is V 3 & V 4 It is seldom this easy !?!?!

22. “Kinds” of Factors General Factor all or “almost all” variables load there is a dominant underlying theme among the set of variables which can be represented with a single composite variable Group Factor some subset of the variables load there is an identifiable sub-theme in the variables that must be represented with a specific subset of the variables “smaller” vs. “larger” group factors (# vars & % variance) Unique Factor single variable loads

23. “Kinds” of Variables Univocal variable -- loads on a single factor Multivocal variable -- loads on 2+ factors Nonvocal variable -- doesn’t load on any factor You should notice a pattern here… a higher “cutoff” (e.g.,.40) tends to produce … fewer variables loading on a given factor less likely to have a general factor fewer multivocal variables more nonvocal variables a lower “cutoff” (e.g.,.30) tends to produce … more variables loading on a given factror more likely to have a general factor more multivocal variables fewer nonvocal variables