# Load required packages only if installed
if (requireNamespace("rosetta", quietly = TRUE)) library(rosetta)
if (requireNamespace("dplyr", quietly = TRUE)) library(dplyr)
if (requireNamespace("psych", quietly = TRUE)) library(psych)
if (requireNamespace("psychTools", quietly = TRUE)) library(psychTools)
if (requireNamespace("knitr", quietly = TRUE)) library(knitr)
if (requireNamespace("kableExtra", quietly = TRUE)) library(kableExtra)

library(LikertMakeR)

LikertMakeR package

LikertMakeR is a package for the R statistical programming language [@winzar2022]. It’s purpose is to synthesise and correlate Likert-scale and similar rating-scale data with predefined first & second moments (mean and standard deviation), Cronbach’s Alpha, Factor Loadings, and other summary statistics.

The makeCorrLoadings() function

The makeCorrLoadings() function generates a correlation matrix from factor loadings and factor correlations as might be published in the results of Exploratory Factor Analysis (EFA) or a Structural Equation Model (SEM). The resulting correlation matrix then can be applied to the makeItems() function to generate a synthetic data set of rating-scale items that closely resemble the original data that created the factor loadings summary table.

This paper tests how well the makeCorrLoadings() function achieves that goal.

Study design

A valid makeCorrLoadings() function should be able to produce a correlation matrix that is identical to the original correlation matrix. Further, subsequent treatment with makeItems() should produce a dataframe that appears to come from the same population as the original sample.

So we need an original, True, dataframe to test the function. Preferably, we should have several different original dataframes to ensure that results are generalisable.

Study #1: Party Panel 2015

Original data

We use the pp15 dataset from the rosetta package [@rosetta]. This is a subset of the Party Panel 2015 dataset. Party Panel is an annual semi-panel study among Dutch nightlife patrons, where every year, the determinants of another nightlife-related risk behavior is mapped. In 2015, determinants were measured of behaviours related to using highly dosed ecstasy pills.

Nine items are relevant to this study. Each is a 7-point likert-style question scored in the range -3 to +3.

The questions and the item labels are presented as follows:

Questions	Labels
Expectation that a high dose results in a longer trip	long
Expectation that a high dose results in a more intense trip	intensity
Expectation that a high dose makes you more intoxicated	intoxicated
Expectation that a high dose provides more energy	energy
Expectation that a high dose produces more euphoria	euphoria
Expectation that a high dose yields more insight	insight
Expectation that a high dose strengthens your connection with others	connection
Expectation that a high dose facilitates making contact with others	contact
Expectation that a high dose improves sex	sex

Extract and clean the original data file

## variable names
item_list <- c(
  "highDose_AttBeliefs_long",
  "highDose_AttBeliefs_intensity",
  "highDose_AttBeliefs_intoxicated",
  "highDose_AttBeliefs_energy",
  "highDose_AttBeliefs_euphoria",
  "highDose_AttBeliefs_insight",
  "highDose_AttBeliefs_connection",
  "highDose_AttBeliefs_contact",
  "highDose_AttBeliefs_sex"
)

## read the data/ select desired variables/ remove obs with missing values
dat <- read.csv2(file = "data/pp15.csv") |>
  select(all_of(item_list)) |>
  na.omit()

## give variables shorter names
names(dat) <- itemLabels

sampleSize <- nrow(dat)

Target correlation matrix

The correlations among these nine items should be reproducable by the makeCorrLoadings() function.

## correlation matrix
pp15_cor <- cor(dat)

	long	intensity	intoxicated	energy	euphoria	insight	connection	contact	sex
long	1.00	0.33	0.34	0.19	0.24	0.17	0.17	0.10	0.01
intensity	0.33	1.00	0.69	0.12	0.26	-0.01	0.14	0.12	0.00
intoxicated	0.34	0.69	1.00	0.15	0.13	-0.03	-0.01	-0.02	-0.11
energy	0.19	0.12	0.15	1.00	0.32	0.29	0.28	0.31	0.10
euphoria	0.24	0.26	0.13	0.32	1.00	0.50	0.61	0.56	0.29
insight	0.17	-0.01	-0.03	0.29	0.50	1.00	0.57	0.54	0.30
connection	0.17	0.14	-0.01	0.28	0.61	0.57	1.00	0.78	0.30
contact	0.10	0.12	-0.02	0.31	0.56	0.54	0.78	1.00	0.35
sex	0.01	0.00	-0.11	0.10	0.29	0.30	0.30	0.35	1.00

Test cases

We shall produce the following options for factor correlation reporting:

Full information: factor loadings and factor correlations to five decimal places, plus uniquenesses.
Full information - No uniquenesses: factor loadings and factor correlations to 5 decimal places but without uniquenesses.
Rounded loadings: factor loadings and factor correlations to two decimal places, plus uniquenesses.
Rounded loadings - No uniquenesses: factor loadings and factor correlations to two decimal places but without uniquenesses.
Censored loadings: factor loadings to two decimal places, with loadings less than some arbitrary value removed for clarity of presentation, with uniquenesses.
Censored loadings - No uniqueness: factor loadings to two decimal places, with loadings less some arbitrary value removed for clarity, but without uniquenesses.
Censored loadings, no uniqueness, no factor correlations: factor loadings to two decimal places, with loadings less than some arbitrary value removed for clarity, no uniquenesses or factor correlations. Functionally, this is the equivalent of claiming orthogonal factors even with factor loadings from non-orthogonal rotation.

Evaluation

To compare the True correlation matrix with a Synthetic matrix, we employ the cortest.jennrich() function from the psych package [@psych]. This is a Chi-square test of whether a pair of matrices are equal [@Jennrich1970]. We report the raw \(\chi^2\) statistic and corresponding p-value for each test.

Exploratory Factor Analysis

Some pretesting suggest that two factors are appropriate for this sample. And we’re confident that the factors will be correlated, so we use promax rotation.

## factor analysis from `rosetta` package
rfaDose <- rosetta::factorAnalysis(
  data = dat,
  nfactors = 2,
  rotate = "promax"
)

factorLoadings <- rfaDose$output$loadings
factorCorrs <- rfaDose$output$correlations

Factor Loadings

	Factor 1	Factor 2	Uniqueness
long	0.11	0.41	0.80
intensity	-0.04	0.79	0.39
intoxicated	-0.21	0.91	0.22
energy	0.34	0.15	0.83
euphoria	0.68	0.17	0.44
insight	0.70	-0.07	0.53
connection	0.86	-0.02	0.26
contact	0.85	-0.05	0.30
sex	0.42	-0.13	0.83

Factor Correlations

	Factor 1	Factor 2
Factor 1	1.00	0.26
Factor 2	0.26	1.00

Test Case #1: Full information

## round input values to 5 decimal places
# factor loadings
fl1 <- factorLoadings[, 1:2] |>
  round(5) |>
  as.matrix()
# item uniquenesses
un1 <- factorLoadings[, 3] |> round(5)
# factor correlations
fc1 <- round(factorCorrs, 5) |> as.matrix()
# run makeCorrLoadings() function
itemCors_1 <- makeCorrLoadings(
  loadings = fl1,
  factorCor = fc1,
  uniquenesses = un1
)
## Compare the two matrices
chiSq_1 <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_1,
  n1 = sampleSize, n2 = sampleSize
)

Test case #2: Full information - No uniquenesses

factor loadings and factor correlations to 5 decimal places but without uniquenesses

## round input values to 2 decimal places
# factor loadings
fl2 <- factorLoadings[, 1:2] |>
  round(5) |>
  as.matrix()
# factor correlations
fc2 <- factorCorrs |>
  round(5) |>
  as.matrix()
itemCors_2 <- makeCorrLoadings(
  loadings = fl2,
  factorCor = fc2,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_2 <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_2,
  n1 = sampleSize, n2 = sampleSize
)

Test case #3: Rounded loadings

factor loadings and factor correlations to two decimal places.

## round input values to 2 decimal places
# factor loadings
fl3 <- factorLoadings[, 1:2] |>
  round(2) |>
  as.matrix()
# item uniquenesses
un3 <- factorLoadings[, 3] |>
  round(2)
## factor correlations
fc3 <- factorCorrs |>
  round(2) |>
  as.matrix()
## Compare the two matrices
itemCors_3 <- makeCorrLoadings(
  loadings = fl3,
  factorCor = fc3,
  uniquenesses = un3
)
## Compare the two matrices
chiSq_3 <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_3,
  n1 = sampleSize, n2 = sampleSize
)

Test case #4: Rounded loadings - No uniquenesses

Factor loadings and factor correlations to two decimal places, and no uniquenesses

## round input values to 2 decimal places
# factor loadings
fl4 <- factorLoadings[, 1:2] |>
  round(2) |>
  as.matrix()
## factor correlations
fc4 <- factorCorrs |>
  round(2) |>
  as.matrix()
# apply the function
itemCors_4 <- makeCorrLoadings(
  loadings = fl4,
  factorCor = fc4,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_4 <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_4,
  n1 = sampleSize, n2 = sampleSize
)

Censored loadings

Often, item-factor loadings are presented with lower values removed for ease of reading. I’m calling these “Censored” loadings. This is usually acceptable, because the purpose is to show the reader where the larger loadings are. But such missing information may affect the results of reverse-engineering that we’re employing with the makeCorrLoadings() function.

In this study, we set the level of hidden loadings at values less than ‘0.1’, ‘0.2’ and ‘0.3’.

Item	all values		< 0.1 out		< 0.2 out		<0.3 out
	f1	f2	f1	f2	f1	f2	f1	f2
long	0.11	0.41	0.11	0.41		0.41		0.41
intensity	-0.04	0.79		0.79		0.79		0.79
intoxicated	-0.21	0.91	-0.21	0.91	-0.21	0.91		0.91
energy	0.34	0.15	0.34	0.15	0.34		0.34
euphoria	0.68	0.17	0.68	0.17	0.68		0.68
insight	0.70	-0.07	0.7		0.7		0.7
connection	0.86	-0.02	0.86		0.86		0.86
contact	0.85	-0.05	0.85		0.85		0.85
sex	0.42	-0.13	0.42	-0.13	0.42		0.42

Test case #5: Censored loadings

Factor loadings less than ‘0.1’, ‘0.2’, ‘0.3’ are removed for clarity, presented to two decimal places.

## round input values to 2 decimal places
# factor loadings
fl5a <- factorLoadings[, 1:2] |>
  round(2)
# convert factor loadings < '0.1' to '0'
fl5a[abs(fl5a) < 0.1] <- 0
fl5a <- as.matrix(fl5a)
# item uniquenesses
un5 <- factorLoadings[, 3] |>
  round(2)
# factor correlations
fc5 <- factorCorrs |>
  round(2) |>
  as.matrix()
# apply the function
itemCors_5a <- makeCorrLoadings(
  loadings = fl5a,
  factorCor = fc5,
  uniquenesses = un5
)
## Compare the two matrices
chiSq_5a <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_5a,
  n1 = sampleSize, n2 = sampleSize
)

# factor loadings
fl5b <- factorLoadings[, 1:2] |>
  round(2)
# convert factor loadings < '0.2' to '0'
fl5b[abs(fl5b) < 0.2] <- 0
fl5b <- as.matrix(fl5b)
# apply the function
itemCors_5b <- makeCorrLoadings(
  loadings = fl5b,
  factorCor = fc5,
  uniquenesses = un5
)
## Compare the two matrices
chiSq_5b <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_5b,
  n1 = sampleSize, n2 = sampleSize
)

# factor loadings
fl5c <- factorLoadings[, 1:2] |>
  round(2)
# convert factor loadings < '0.2' to '0'
fl5c[abs(fl5c) < 0.3] <- 0
fl5c <- as.matrix(fl5c)
# apply the function
itemCors_5c <- makeCorrLoadings(
  loadings = fl5c,
  factorCor = fc5,
  uniquenesses = un5
)
## Compare the two matrices
chiSq_5c <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_5c,
  n1 = sampleSize, n2 = sampleSize
)
# kable(itemCors_5, digits = 2)

Test case #6: Censored loadings - no uniquenesses

With no declared uniquenesses, they are inferred from estimated communalities.

itemCors_6a <- makeCorrLoadings(
  loadings = fl5a,
  factorCor = fc5,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_6a <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_6a,
  n1 = sampleSize, n2 = sampleSize
)
# apply the function
itemCors_6b <- makeCorrLoadings(
  loadings = fl5b,
  factorCor = fc5,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_6b <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_6b,
  n1 = sampleSize, n2 = sampleSize
)
# apply the function
itemCors_6c <- makeCorrLoadings(
  loadings = fl5c,
  factorCor = fc5,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_6c <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_6c,
  n1 = sampleSize, n2 = sampleSize
)

Test case #7 Censored loadings, no uniqueness, no factor correlations

No uniquenesses. That is, Uniquenesses are estimated from 1-communalities, where communalities = sum(factor-loadings^2). And no factor correlations. That is, we assume orthogonal factors.

itemCors_7a <- makeCorrLoadings(
  loadings = fl5a,
  factorCor = NULL,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_7a <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_7a,
  n1 = sampleSize, n2 = sampleSize
)
# apply the function
itemCors_7b <- makeCorrLoadings(
  loadings = fl5b,
  factorCor = NULL,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_7b <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_7b,
  n1 = sampleSize, n2 = sampleSize
)
# apply the function
itemCors_7c <- makeCorrLoadings(
  loadings = fl5c,
  factorCor = NULL,
  uniquenesses = NULL
)
## Compare the two matrices
chiSq_7c <- cortest.jennrich(
  R1 = pp15_cor, R2 = itemCors_7c,
  n1 = sampleSize, n2 = sampleSize
)

Summary results

Treatment	chi2	p
Full information	21.08	0.977
Full information - No uniqueness	22.22	0.965
Rounded loadings	21.07	0.978
Rounded loadings - No uniqueness	22.20	0.965
Censored loadings <0.1	24.53	0.926
Censored loadings <0.2	44.08	0.167
Censored loadings <0.3	74.23	0.000
Censored loadings <0.1 - no uniqueness	26.18	0.886
Censored loadings <0.2 - no uniqueness	46.00	0.123
Censored loadings <0.3 - no uniqueness	78.88	0.000
Censored loadings <0.1 - no uniqueness, factor cors	30.51	0.727
Censored loadings <0.2 - no uniqueness, factor cors	49.63	0.065
Censored loadings <0.3 - no uniqueness, factor cors	65.82	0.002

Conclusion

The makeCorrLoadings function works quite well when it has information on factor loadings, but much less well when “summary” (censored) factor loadings are given.

Study #2: Big Five (bfi personality items)

Original data: bfi 25 Personality items representing 5 factors

25 personality self report items taken from the International Personality Item Pool (ipip.ori.org) were included as part of the Synthetic Aperture Personality Assessment (SAPA) web based personality assessment project. The data are available through the psychTools package [@psychTools].

2800 subjects are included as a demonstration set for scale construction, factor analysis, and Item Response Theory analysis. Three additional demographic variables (sex, education, and age) are also included.

For purposes of this study, we confine ourselves to women (sex==2) with postgraduate qualifications (education==5), giving us a sample size of 229 subjects.

The dataset contains the following 28 variables. (The q numbers are the SAPA item numbers).

A1 Am indifferent to the feelings of others. (q_146)
A2 Inquire about others’ well-being. (q_1162)
A3 Know how to comfort others. (q_1206)
A4 Love children. (q_1364)
A5 Make people feel at ease. (q_1419)
C1 Am exacting in my work. (q_124)
C2 Continue until everything is perfect. (q_530)
C3 Do things according to a plan. (q_619)
C4 Do things in a half-way manner. (q_626)
C5 Waste my time. (q_1949)
E1 Don’t talk a lot. (q_712)
E2 Find it difficult to approach others. (q_901)
E3 Know how to captivate people. (q_1205)
E4 Make friends easily. (q_1410)
E5 Take charge. (q_1768)
N1 Get angry easily. (q_952)
N2 Get irritated easily. (q_974)
N3 Have frequent mood swings. (q_1099
N4 Often feel blue. (q_1479)
N5 Panic easily. (q_1505)
O1 Am full of ideas. (q_128)
O2 Avoid difficult reading material.(q_316)
O3 Carry the conversation to a higher level. (q_492)
O4 Spend time reflecting on things. (q_1738)
O5 Will not probe deeply into a subject. (q_1964)
gender (Male=1, Female=2)
education (1=HS, 2=finished_HS, 3=some_college, 4=college_graduate 5=graduate_degree)
age age in years

Second target correlation matrix

The correlations among these 25 items should be reproducable by the makeCorrLoadings() function.

## download data
data(bfi)
## filter for highly-educated women
bfi_short <- bfi |>
  filter(education == 5 & gender == 2) |>
  na.omit()
## keep just the 25 items
bfi_short <- bfi_short[, 1:25]
sampleSize <- nrow(bfi_short)
## derive correlation matrix
bfi_cor <- cor(bfi_short)

Test cases and Evaluation

As in the first study, we shall produce the following options for factor correlation reporting:

Full information
Full information - No uniquenesses
Rounded loadings
Rounded loadings - No uniquenesses
Censored loadings
Censored loadings - No uniqueness
Censored loadings - no uniqueness or factor cors

And, as in the first study, we compare the True correlation matrix with a Synthetic matrix, employing a Jennrich test.

Exploratory Factor Analysis

Five correlated factors are appropriate for this sample, so we use promax rotation.

## factor analysis from `rosetta` package is a less messy version of the `psych::fa()` function

fa_bfi <- rosetta::factorAnalysis(
  data = bfi_short,
  nfactors = 5,
  rotate = "promax"
)

bfiLoadings <- fa_bfi$output$loadings
bfiCorrs <- fa_bfi$output$correlations

item-factor loadings & uniquenesses

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Uniqueness
A1	0.07	0.14	0.03	-0.55	-0.15	0.70
A2	0.06	0.13	0.06	0.55	0.13	0.59
A3	0.16	0.12	0.17	0.63	0.05	0.50
A4	-0.11	0.06	0.07	0.37	-0.08	0.81
A5	-0.08	0.21	-0.05	0.53	0.01	0.57
C1	0.03	-0.06	0.64	0.01	0.12	0.55
C2	0.18	0.00	0.79	0.01	-0.07	0.44
C3	0.08	-0.11	0.65	0.10	-0.07	0.63
C4	0.15	0.06	-0.60	-0.04	-0.12	0.54
C5	0.27	-0.04	-0.53	-0.02	0.08	0.59
E1	0.01	-0.56	0.11	-0.07	0.13	0.67
E2	0.18	-0.75	0.09	-0.11	0.14	0.35
E3	0.14	0.49	-0.04	0.17	0.14	0.62
E4	-0.05	0.54	-0.09	0.38	-0.18	0.44
E5	0.07	0.54	0.25	-0.17	0.14	0.56
N1	0.73	0.16	0.11	-0.14	-0.15	0.47
N2	0.80	0.14	0.18	-0.16	-0.11	0.36
N3	0.78	-0.01	-0.07	0.03	0.10	0.34
N4	0.60	-0.17	-0.11	-0.05	0.16	0.51
N5	0.58	-0.17	-0.04	0.27	-0.16	0.58
O1	0.02	0.42	-0.10	-0.10	0.44	0.59
O2	0.21	-0.02	-0.10	0.02	-0.57	0.60
O3	0.12	0.46	0.01	0.05	0.35	0.55
O4	0.07	-0.13	-0.01	0.11	0.48	0.76
O5	0.08	0.01	0.02	-0.10	-0.74	0.45

factor correlations

	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5
Factor 1	1.00	-0.14	-0.24	-0.19	0.11
Factor 2	-0.14	1.00	0.21	0.47	0.32
Factor 3	-0.24	0.21	1.00	0.01	0.35
Factor 4	-0.19	0.47	0.01	1.00	0.12
Factor 5	0.11	0.32	0.35	0.12	1.00

Test Case #1: Full information

## round input values to 5 decimal places
# factor loadings
fl1 <- bfiLoadings[, 1:5] |>
  round(5) |>
  as.matrix()
# item uniquenesses
un1 <- bfiLoadings[, 6] |> round(5)
# factor correlations
fc1 <- round(bfiCorrs, 5) |> as.matrix()
# run makeCorrLoadings() function
itemCors_1 <- makeCorrLoadings(
  loadings = fl1,
  factorCor = fc1,
  uniquenesses = un1
)
## Compare the two matrices
chiSq_1 <- cortest.jennrich(
  R1 = bfi_cor, R2 = itemCors_1,
  n1 = sampleSize, n2 = sampleSize
)

Other test cases as for the first study

The remaining test cases are as for the first study and this last test case. Changes are made to the number of decimal places considered, the presence of uniquenesses, presence of factor correlation matrix, and level of item-factor loading that is included.

Study #2 Summary results

Treatment	chi2	p
Full information	178.4	1.000
Full information - No uniquenesses	183.7	1.000
Rounded loadings	178.8	1.000
Rounded loadings - No uniquenesses	183.7	1.000
Censored loadings <0.1	223.8	1.000
Censored loadings <0.2	454.5	0.000
Censored loadings <0.3	490.1	0.000
Censored loadings <0.1 - no uniqueness	232.6	0.998
Censored loadings <0.2 - no uniqueness	447.9	0.000
Censored loadings <0.3 - no uniqueness	478.1	0.000
Censored loadings <0.1 - no uniqueness, no factor cors	241.2	0.995
Censored loadings <0.2 - no uniqueness, no factor cors	440.1	0.000
Censored loadings <0.3 - no uniqueness, no factor cors	484.4	0.000

Overall Results

Both studies show consistent results.

	Party panel		Big 5 (bfi)
treatment	chi2	p	chi2	p
Full information	21.1	0.977	178.4	1.000
Full information - No uniquenesses	22.2	0.965	183.7	1.000
Rounded loadings	21.1	0.978	178.8	1.000
Rounded loadings - No uniquenesses	22.2	0.965	183.7	1.000
Censored loadings <0.1	24.5	0.926	223.8	1.000
Censored loadings <0.2	44.1	0.167	454.5	0.000
Censored loadings <0.3	74.2	0.000	490.1	0.000
Censored loadings <0.1 - no uniqueness	26.2	0.886	232.6	0.998
Censored loadings <0.2 - no uniqueness	46.0	0.123	447.9	0.000
Censored loadings <0.3 - no uniqueness	78.9	0.000	478.1	0.000
Censored loadings <0.1 - no uniqueness, no factor cors	30.5	0.727	241.2	0.995
Censored loadings <0.2 - no uniqueness, no factor cors	49.6	0.065	440.1	0.000
Censored loadings <0.3 - no uniqueness, no factor cors	65.8	0.002	484.4	0.000

Conclusions

The makeCorrLoadings() function is designed to produce an item correlation matrix based on item-factor loadings and factor correlations as one might see in the results of Exploratory Factor Analysis (EFA) or Structural Equation Modelling (SEM).

Results of this study suggest that the makeCorrLoadings() function does a surprisingly good job of reproducing the target correlation matrix when all item-factor loadings are present.

A correlation matrix created with makeCorrLoadings() seems to be robust even with the absence of specified uniquenesses, or even without factor correlations. But a valid reproduction of a correlation matrix should have complete item-factor loadings.

makeCorrLoadings validation

Hume Winzar

April 2025

LikertMakeR package

The makeCorrLoadings() function

Study design

Study #1: Party Panel 2015

Original data

Extract and clean the original data file

Target correlation matrix

Test cases

Evaluation

Exploratory Factor Analysis

Factor Loadings

Factor Correlations

Test Case #1: Full information

Test case #2: Full information - No uniquenesses

Test case #3: Rounded loadings

Test case #4: Rounded loadings - No uniquenesses

Censored loadings

Test case #5: Censored loadings

Test case #6: Censored loadings - no uniquenesses

Test case #7 Censored loadings, no uniqueness, no factor correlations

Summary results

Conclusion

Study #2: Big Five (bfi personality items)

Original data: bfi 25 Personality items representing 5 factors

Second target correlation matrix

Test cases and Evaluation

Exploratory Factor Analysis

item-factor loadings & uniquenesses

factor correlations

Test Case #1: Full information

Other test cases as for the first study

Study #2 Summary results

Overall Results

Conclusions

References