# Download data and Store in data1
data1 <- read.csv(url("https://www.dropbox.com/s/ypygopkc9dzwhn6/data1.csv?dl=1"), header = TRUE)
# Remove first column and store in efaData
efaData <- data1[,2:35]
# Load Packages
library("psych")An exploratory factor analysis (EFA) was conducted to identify underlying factors among the survey responses. Prior to EFA, initial analyses will be performed to assess if the dataset is suited for an exploratory factor analysis. Kaiser-Meyer-Olkin measure of sampling adequacy was used to measure how suited the data is for factor analysis. This test aims to measure the adequacy of the data for factor analysis with an acceptable value of at least 0.60 (Kaiser and Rice, 1974). Bartlett’s Test of Sphericity was used to test the hypothesis that the correlation matrix is an identity matrix (Snedecor and Cochran, 1980). Once the dataset is established to be suited for the analysis, an exploratory factor analysis will be performed. An exploratory factor analysis is a statistical technique that analyze patterns of linear relationship that aims to identify empirically distinct or unobserved constructs (Sakaluk and Short, 2016).
Initially, the factorability of the survey questionnaire was examined using Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s test of Sphericity. The KMO measure of sampling adequacy is 0.99 which is considered marvelous. Moreover, Bartlett’s test of sphericity was significant \(\chi^2(561) = 900,013.70, p < 0.001)\). Given all these indicators, factor analysis is deemed to be appropriate for the survey items.
KMO(efaData)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = efaData)
## Overall MSA = 0.99
## MSA for each item =
## JST_1 JST_2 JST_3 JST_4 JST_5 JST_6 JST_7 JST_8 JST_9 JST_10 OS_1
## 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.98 0.99
## OS_2 OS_3 OS_4 OS_5 JSA_1 JSA_2 JSA_3 JSA_4 JSA_5 JSA_6 JSA_7
## 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99
## JSA_8 JSA_9 JSA_10 JSA_11 JSA_12 JSA_13 TI_1 TI_2 TI_3 TI_4 TI_5
## 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.98 0.99
## TI_6
## 0.99cortest(efaData)## Tests of correlation matrices
## Call:cortest(R1 = efaData)
## Chi Square value 900013.7 with df = 561 with probability < 0EFA was run iteratively to achieve a simple factor structure where every statements has a primary factor loading of at least 0.50 with no cross-loading among factors. Items that did not meet these criteria were removed. The following results illustrate the entire process undertaken by the researchers to achieve a simple 4-factor structure using the items included in the survey.
print(
fa(efaData[,-c(6)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.30, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.621 -0.491
## JST_2 0.520 -0.471
## JST_3 0.811
## JST_4 0.893
## JST_5 0.822 -0.314
## JST_7 0.927
## JST_8 0.957
## JST_9 -0.391 0.544
## JST_10 -0.874 0.308 0.487
## OS_1 -0.396
## OS_2 1.038 0.304
## OS_3 0.331 0.444
## OS_4 0.362 0.311
## OS_5 0.312 0.565 -0.329
## JSA_1 1.106
## JSA_2 0.483 -0.477
## JSA_3 0.356 -0.453
## JSA_4 0.812
## JSA_5 0.885 -0.382 0.335
## JSA_6 0.657
## JSA_7 0.937
## JSA_8 0.900
## JSA_9 0.661
## JSA_10 0.509 -0.418
## JSA_11 0.651 0.311
## JSA_12 0.936
## JSA_13 1.044
## TI_1 0.638 0.338
## TI_2 0.708
## TI_3 0.358 0.816
## TI_4 0.825
## TI_5 0.822
## TI_6 0.776
##
## ML3 ML4 ML1 ML2
## SS loadings 7.729 6.631 5.090 4.414
## Proportion Var 0.234 0.201 0.154 0.134
## Cumulative Var 0.234 0.435 0.589 0.723Initially, JST_6 is removed as per recommendation.
Cut-off is set to 0.30 to help researchers identify which item should be
removed based on their factor loadings. The result shows no
cross-loading (items with factor loadings of at least 0.50 in two or
more factors) but some items have factor loading that is less than 0.5.
From among these items, OS_4 has the lowest. Hence, it is
removed.
print(
fa(efaData[,-c(6, 14)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.30, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.621 -0.491
## JST_2 0.521 -0.471
## JST_3 0.820
## JST_4 0.887
## JST_5 0.814 -0.320
## JST_7 0.927
## JST_8 0.957
## JST_9 -0.390 0.544
## JST_10 -0.872 0.305 0.476
## OS_1 -0.400
## OS_2 1.033
## OS_3 0.323 0.441
## OS_5 0.316 0.575 -0.322
## JSA_1 1.101
## JSA_2 0.486 -0.470
## JSA_3 0.353 -0.456
## JSA_4 0.813
## JSA_5 0.875 -0.386 0.332
## JSA_6 0.669
## JSA_7 0.941
## JSA_8 0.889
## JSA_9 0.655
## JSA_10 0.509 -0.415
## JSA_11 0.637 0.305
## JSA_12 0.938
## JSA_13 1.021
## TI_1 0.646 0.338
## TI_2 0.699
## TI_3 0.362 0.810
## TI_4 0.816
## TI_5 0.815
## TI_6 0.768
##
## ML3 ML4 ML1 ML2
## SS loadings 7.470 6.607 5.000 4.313
## Proportion Var 0.233 0.206 0.156 0.135
## Cumulative Var 0.233 0.440 0.596 0.731After removing OS_4, the second iteration of EFA was
conducted. Still, cross-loading is not observed but some items have
factor loading that is less than 0.5. From among the items,
OS_1 has the lowest. Thus, it is removed.
print(
fa(efaData[,-c(6, 14, 11)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.3, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.606 -0.494
## JST_2 0.518 -0.469
## JST_3 0.809
## JST_4 0.879
## JST_5 0.819 -0.312
## JST_7 0.913
## JST_8 0.947
## JST_9 -0.391 0.540
## JST_10 -0.866 0.304 0.473
## OS_2 1.026 0.301
## OS_3 0.325 0.438
## OS_5 0.313 0.567 -0.326
## JSA_1 1.095
## JSA_2 0.481 -0.474
## JSA_3 0.356 -0.450
## JSA_4 0.813
## JSA_5 0.873 -0.379 0.330
## JSA_6 0.663
## JSA_7 0.934
## JSA_8 0.886
## JSA_9 0.652
## JSA_10 0.503 -0.418
## JSA_11 0.635 0.306
## JSA_12 0.929
## JSA_13 1.019
## TI_1 0.635 0.341
## TI_2 0.705
## TI_3 0.353 0.816
## TI_4 0.822
## TI_5 0.821
## TI_6 0.774
##
## ML3 ML4 ML1 ML2
## SS loadings 7.330 6.307 4.885 4.370
## Proportion Var 0.236 0.203 0.158 0.141
## Cumulative Var 0.236 0.440 0.597 0.738After removing OS_1, the third iteration of EFA was
conducted. Similar to the previous result, cross-loading is not observed
but some items have factor loading score of less than 0.5. From among
the items, OS_3 has the lowest. Thus, it is removed.
print(
fa(efaData[,-c(6, 14, 11, 13)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.4, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.599 -0.501
## JST_2 0.512 -0.475
## JST_3 0.805
## JST_4 0.868
## JST_5 0.815
## JST_7 0.912
## JST_8 0.951
## JST_9 0.548
## JST_10 -0.871 0.460
## OS_2 1.021
## OS_5 0.566
## JSA_1 1.088
## JSA_2 0.489 -0.463
## JSA_3 -0.445
## JSA_4 0.810
## JSA_5 0.869
## JSA_6 0.677
## JSA_7 0.927
## JSA_8 0.886
## JSA_9 0.655
## JSA_10 0.496 -0.418
## JSA_11 0.631
## JSA_12 0.925
## JSA_13 1.008
## TI_1 0.633
## TI_2 0.696
## TI_3 0.811
## TI_4 0.818
## TI_5 0.817
## TI_6 0.765
##
## ML3 ML4 ML1 ML2
## SS loadings 7.173 6.220 4.699 4.299
## Proportion Var 0.239 0.207 0.157 0.143
## Cumulative Var 0.239 0.446 0.603 0.746After removing OS_3, the fourth iteration of EFA was
conducted setting the cutoff value to 0.40. Now, cross-loading is
observed in JST_1 and some items have factor loading score
of less than 0.5. From among the items, JSA_3 has the
lowest. Hence, it is removed.
print(
fa(efaData[,-c(6, 14, 11, 13, 18)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.4, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.593 -0.500
## JST_2 0.512 -0.470
## JST_3 0.784
## JST_4 0.864
## JST_5 0.804
## JST_7 0.900
## JST_8 0.938
## JST_9 0.538
## JST_10 -0.868 0.461
## OS_2 1.013
## OS_5 0.552
## JSA_1 1.080
## JSA_2 0.484 -0.469
## JSA_4 0.809
## JSA_5 0.874
## JSA_6 0.669
## JSA_7 0.916
## JSA_8 0.881
## JSA_9 0.649
## JSA_10 0.493 -0.426
## JSA_11 0.630
## JSA_12 0.909
## JSA_13 1.002
## TI_1 0.620
## TI_2 0.699
## TI_3 0.819
## TI_4 0.825
## TI_5 0.825
## TI_6 0.783
##
## ML3 ML4 ML1 ML2
## SS loadings 6.980 5.873 4.562 4.393
## Proportion Var 0.241 0.203 0.157 0.151
## Cumulative Var 0.241 0.443 0.601 0.752After removing JSA_3, the fifth iteration of EFA was
done. Still, cross-loading is observed in JST_1 and some
items have factor loading score of less than 0.5. From among the items,
JSA_2 has the lowest. Hence, it is removed.
print(
fa(efaData[,-c(6, 14, 11, 13, 18, 17)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.5, sort = FALSE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_1 0.608
## JST_2 0.519
## JST_3 0.806
## JST_4 0.886
## JST_5 0.821
## JST_7 0.911
## JST_8 0.947
## JST_9 0.541
## JST_10 -0.860
## OS_2 1.000
## OS_5 0.561
## JSA_1 1.083
## JSA_4 0.798
## JSA_5 0.855
## JSA_6 0.675
## JSA_7 0.914
## JSA_8 0.879
## JSA_9 0.644
## JSA_10
## JSA_11 0.630
## JSA_12 0.915
## JSA_13 0.998
## TI_1 0.637
## TI_2 0.684
## TI_3 0.807
## TI_4 0.817
## TI_5 0.813
## TI_6 0.770
##
## ML3 ML4 ML1 ML2
## SS loadings 6.648 6.078 4.580 4.040
## Proportion Var 0.237 0.217 0.164 0.144
## Cumulative Var 0.237 0.454 0.618 0.762After removing JSA_2, the sixth iteration of EFA was
done setting the cutoff value to 0.50. Now, cross-loading is no longer
observed but JSA_10 has a factor loading of less than 0.50.
Thus, it is removed.
print(
fa(efaData[,-c(6, 14, 11, 13, 18, 17, 25)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.5, sort = FALSE)##
## Loadings:
## ML4 ML3 ML1 ML2
## JST_1 0.628
## JST_2 0.506
## JST_3 0.851
## JST_4 0.895
## JST_5 0.824
## JST_7 0.925
## JST_8 0.959
## JST_9 0.607
## JST_10 -0.854
## OS_2 0.980
## OS_5 0.609
## JSA_1 1.069
## JSA_4 0.753
## JSA_5 0.829
## JSA_6 0.699
## JSA_7 0.902
## JSA_8 0.857
## JSA_9 0.632
## JSA_11 0.619
## JSA_12 0.933
## JSA_13 0.961
## TI_1 0.651
## TI_2 0.657
## TI_3 0.775
## TI_4 0.787
## TI_5 0.780
## TI_6 0.743
##
## ML4 ML3 ML1 ML2
## SS loadings 6.331 6.252 4.381 3.577
## Proportion Var 0.234 0.232 0.162 0.132
## Cumulative Var 0.234 0.466 0.628 0.761After removing JSA_10, the seventh iteration of EFA was
done while arranging the items based on their factor loadings.
Similarly, cross-loading is no longer observed and all items has a
factor loading of at least 0.50. Thus, a good factor structure is
achieved and items are examined to create the final factor model.
Upon final examination. TI_1 is removed since
TL_1 - I think about quitting my present job., and
JST_7 - I often think about quitting my present job., are
basically the same. Final iteration of EFA was conducted and the
researchers were able to achieve a good factor structure since
cross-loadings among items are not present and all items has a factor
loading of at least 0.5.
# KMO
KMO(efaData)## Kaiser-Meyer-Olkin factor adequacy
## Call: KMO(r = efaData)
## Overall MSA = 0.99
## MSA for each item =
## JST_1 JST_2 JST_3 JST_4 JST_5 JST_6 JST_7 JST_8 JST_9 JST_10 OS_1
## 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.98 0.99
## OS_2 OS_3 OS_4 OS_5 JSA_1 JSA_2 JSA_3 JSA_4 JSA_5 JSA_6 JSA_7
## 0.99 0.99 0.99 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.99
## JSA_8 JSA_9 JSA_10 JSA_11 JSA_12 JSA_13 TI_1 TI_2 TI_3 TI_4 TI_5
## 0.98 0.99 0.99 0.99 0.99 0.99 0.99 0.98 0.99 0.98 0.99
## TI_6
## 0.99# Bartlett's Test
cortest(efaData)## Tests of correlation matrices
## Call:cortest(R1 = efaData)
## Chi Square value 900013.7 with df = 561 with probability < 0the factorability of the survey questionnaire was examined using Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, and Bartlett’s test of Sphericity. The KMO measure of sampling adequacy is 0.99 which is considered marvelous. Moreover, Bartlett’s test of sphericity was significant \(\chi^2(561) = 900,013.70, p < 0.001)\). Given all these indicators, factor analysis is deemed to be appropriate for the survey items.
print(
fa(efaData[,-c(6, 14, 11, 13, 18, 17, 25, 29)],
nfactors = 4,
rotate = "bentlerQ",
scores = "regression",
fm = "ml"
)$loadings,
digits = 3, cutoff = 0.5, sort = TRUE)##
## Loadings:
## ML3 ML4 ML1 ML2
## JST_10 -0.802
## OS_2 1.033
## JSA_1 1.055
## JSA_4 0.775
## JSA_5 0.847
## JSA_9 0.697
## JSA_11 0.687
## JSA_13 1.021
## JST_1 0.616
## JST_2 0.575
## JST_3 0.723
## JST_4 0.909
## JST_5 0.854
## JST_7 0.885
## JST_8 0.964
## JST_9 0.633
## OS_5 0.512
## JSA_6 0.638
## JSA_7 0.826
## JSA_8 0.840
## JSA_12 0.851
## TI_2 0.696
## TI_3 0.788
## TI_4 0.794
## TI_5 0.788
## TI_6 0.749
##
## ML3 ML4 ML1 ML2
## SS loadings 6.605 5.839 3.785 3.618
## Proportion Var 0.254 0.225 0.146 0.139
## Cumulative Var 0.254 0.479 0.624 0.763Most of the statements belong to the hypothesized component (Job
Stress and Turnover Intention). However, the hypothesized statements Job
Satisfaction items were broken down into two components while
JST_10, OS_2, and OS_5 now
belongs to one of these two components.
Specifically, Component 1 (ML1) includes 5 statements
which are OS_5, JSA_6, JSA_7,
JSA_8, and JSA_12. These statements reflect
respondents’ perceptions of organizational support, pride in their work,
accountability, feedback, and opportunities for career advancement.
Hence, this component will be named Organizational Support as it
highlights the support and growth potential they perceive within the
organization.
Moreover, Component 2 (ML2) includes 5 statements which
are TI_2, TI_3, TI_4,
TI_5, and TI_6. These statements describes the
respondents intent to look for an alternative job/workplace. Thus, this
component is named as Turnover Intention and will be used to describe
the how likely it is for the respondents to render their resignation and
look for an alternative job/workplace.
Additionally, Component 3 (ML3) includes 8 statements
which are JST_10, OS_2, JSA_1,
JSA_4, JSA_5, JSA_9,
JSA_11, and JSA_13. It is important to take
note that JSA_10 (My job makes it difficult to fulfill
family responsibilities) has a negative factor loading suggesting
that the statement is negatively phrased when compared to the other
statements that belong to this component. These statements reflect
respondents’ perception of their work-life balance, compensation,
recognition, and job satisfaction. Thus, this component will be named as
Job Satisfaction as it captures the different aspects of their job
experience which includes how their roles impact their personal lives
and how they feel valued by the company.
Finally, Component 4 (ML4) includes 8 statements which
are JST_1, JST_2, JST_3,
JST_4, JST_5, JST_7,
JST_8, and JST_9. These statements describes
the respondents’ feelings of dissatisfaction, stress, and uncertainty
within the organization and job roles. Thus, this component will be
named as Job stress and will be used to capture their emotions and
attitudes related to their work environment and future career
prospects.
Reliability test using Cronbach \(\alpha\) was conducted after generating the factor structure of the statments using Exploratory Factor Analysis. Cronbach \(\alpha\) tests the reliability of a measurement tool by measuring its internal consistency. For it to be acceptable, all components should have at least 0.70 which indicates strong internal consistency among items within the component.
In summary, all four components has an internal consistency of 0.98
suggesting a very strong internal consistency among items within each
component. It is important to note, however, that JST_10 of
Component 3 is reverse coded (that is, scores of 1 are encoded as 5, 2
as 4, 4 as 2, and 5 as 1) to ensure that the tone of this statement is
consistent with the other items within this component.
Finally, for each component considered, removing items does not merit an improvement to the internal consistency of each. Removing items may, in turn, decrease the overall internal consistency of the component suggesting that all items considered in each component contribute positively to the overall internal consistency of the component.
alpha(efaData[,c(15,21,22,23,27)], check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = efaData[, c(15, 21, 22, 23, 27)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.97 0.9 47 0.0011 2.7 1.6 0.9
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.98 0.98
## Duhachek 0.98 0.98 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## OS_5 0.97 0.97 0.97 0.91 39 0.0013 0.00011 0.91
## JSA_6 0.97 0.97 0.97 0.91 39 0.0013 0.00018 0.91
## JSA_7 0.97 0.97 0.97 0.90 36 0.0014 0.00018 0.90
## JSA_8 0.97 0.97 0.97 0.91 39 0.0013 0.00007 0.90
## JSA_12 0.97 0.97 0.96 0.90 35 0.0014 0.00012 0.90
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## OS_5 210 0.96 0.96 0.94 0.93 2.5 1.7
## JSA_6 210 0.96 0.96 0.94 0.93 2.6 1.6
## JSA_7 210 0.96 0.96 0.95 0.94 2.7 1.7
## JSA_8 210 0.96 0.96 0.94 0.93 2.7 1.7
## JSA_12 210 0.97 0.97 0.96 0.95 2.7 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## OS_5 0.47 0.09 0.12 0.08 0.24 0.79
## JSA_6 0.41 0.12 0.13 0.11 0.22 0.79
## JSA_7 0.41 0.11 0.10 0.10 0.27 0.79
## JSA_8 0.42 0.10 0.10 0.10 0.28 0.79
## JSA_12 0.43 0.11 0.09 0.08 0.29 0.79alpha(efaData[,c(30:34)], check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = efaData[, c(30:34)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.97 0.9 44 0.0011 3.4 1.5 0.9
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.98 0.98
## Duhachek 0.98 0.98 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## TI_2 0.97 0.97 0.96 0.9 35 0.0014 2.1e-05 0.9
## TI_3 0.97 0.97 0.96 0.9 34 0.0015 6.1e-06 0.9
## TI_4 0.97 0.97 0.96 0.9 35 0.0014 2.6e-05 0.9
## TI_5 0.97 0.97 0.96 0.9 36 0.0014 3.9e-05 0.9
## TI_6 0.97 0.97 0.96 0.9 36 0.0014 2.7e-05 0.9
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## TI_2 210 0.96 0.96 0.95 0.94 3.4 1.6
## TI_3 210 0.96 0.96 0.95 0.94 3.4 1.6
## TI_4 210 0.96 0.96 0.95 0.94 3.4 1.6
## TI_5 210 0.96 0.96 0.94 0.93 3.4 1.6
## TI_6 210 0.96 0.96 0.94 0.93 3.4 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## TI_2 0.21 0.09 0.16 0.16 0.38 0.79
## TI_3 0.24 0.10 0.11 0.14 0.41 0.79
## TI_4 0.23 0.06 0.17 0.13 0.41 0.79
## TI_5 0.24 0.08 0.12 0.17 0.39 0.79
## TI_6 0.22 0.09 0.15 0.12 0.42 0.79alpha(efaData[,c(10,12,16,19,20,24,26,28)], check.keys = TRUE)## Warning in alpha(efaData[, c(10, 12, 16, 19, 20, 24, 26, 28)], check.keys = TRUE): Some items were negatively correlated with the first principal component and were automatically reversed.
## This is indicated by a negative sign for the variable name.##
## Reliability analysis
## Call: alpha(x = efaData[, c(10, 12, 16, 19, 20, 24, 26, 28)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.98 0.87 54 0.00088 2.4 1.5 0.87
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.98 0.98
## Duhachek 0.98 0.98 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## JST_10- 0.98 0.98 0.98 0.87 49 0.00098 0.00023 0.87
## OS_2 0.98 0.98 0.98 0.87 47 0.00102 0.00027 0.87
## JSA_1 0.98 0.98 0.98 0.87 47 0.00102 0.00031 0.87
## JSA_4 0.98 0.98 0.98 0.87 48 0.00100 0.00031 0.87
## JSA_5 0.98 0.98 0.98 0.87 47 0.00103 0.00029 0.87
## JSA_9 0.98 0.98 0.98 0.87 47 0.00102 0.00023 0.87
## JSA_11 0.98 0.98 0.98 0.87 46 0.00104 0.00022 0.87
## JSA_13 0.98 0.98 0.98 0.87 49 0.00098 0.00024 0.87
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## JST_10- 210 0.93 0.93 0.92 0.91 2.4 1.6
## OS_2 210 0.94 0.94 0.93 0.93 2.4 1.5
## JSA_1 210 0.95 0.95 0.94 0.93 2.4 1.5
## JSA_4 210 0.94 0.94 0.93 0.92 2.4 1.6
## JSA_5 210 0.95 0.95 0.94 0.93 2.5 1.6
## JSA_9 210 0.95 0.95 0.94 0.93 2.5 1.6
## JSA_11 210 0.95 0.95 0.95 0.94 2.5 1.6
## JSA_13 210 0.93 0.93 0.92 0.91 2.5 1.7
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## JST_10 0.21 0.05 0.09 0.20 0.44 0.79
## OS_2 0.41 0.21 0.11 0.08 0.19 0.79
## JSA_1 0.40 0.20 0.14 0.04 0.21 0.79
## JSA_4 0.45 0.13 0.16 0.05 0.21 0.79
## JSA_5 0.43 0.14 0.15 0.08 0.20 0.79
## JSA_9 0.43 0.13 0.16 0.06 0.21 0.79
## JSA_11 0.44 0.14 0.12 0.09 0.20 0.79
## JSA_13 0.50 0.09 0.09 0.08 0.24 0.79alpha(efaData[,c(1,2,3,4,5,7,8,9)], check.keys = TRUE)##
## Reliability analysis
## Call: alpha(x = efaData[, c(1, 2, 3, 4, 5, 7, 8, 9)], check.keys = TRUE)
##
## raw_alpha std.alpha G6(smc) average_r S/N ase mean sd median_r
## 0.98 0.98 0.98 0.87 52 9e-04 3.4 1.5 0.86
##
## 95% confidence boundaries
## lower alpha upper
## Feldt 0.98 0.98 0.98
## Duhachek 0.98 0.98 0.98
##
## Reliability if an item is dropped:
## raw_alpha std.alpha G6(smc) average_r S/N alpha se var.r med.r
## JST_1 0.98 0.98 0.97 0.86 43 0.00110 0.00039 0.86
## JST_2 0.98 0.98 0.98 0.86 45 0.00106 0.00051 0.86
## JST_3 0.98 0.98 0.98 0.87 46 0.00102 0.00075 0.86
## JST_4 0.98 0.98 0.98 0.86 44 0.00107 0.00051 0.86
## JST_5 0.98 0.98 0.98 0.87 47 0.00101 0.00053 0.86
## JST_7 0.98 0.98 0.98 0.87 47 0.00101 0.00072 0.86
## JST_8 0.98 0.98 0.98 0.87 46 0.00102 0.00076 0.86
## JST_9 0.98 0.98 0.98 0.88 49 0.00097 0.00050 0.87
##
## Item statistics
## n raw.r std.r r.cor r.drop mean sd
## JST_1 210 0.96 0.96 0.96 0.95 3.4 1.7
## JST_2 210 0.95 0.95 0.94 0.93 3.4 1.7
## JST_3 210 0.94 0.94 0.93 0.92 3.4 1.6
## JST_4 210 0.95 0.95 0.95 0.94 3.4 1.6
## JST_5 210 0.94 0.94 0.92 0.92 3.4 1.7
## JST_7 210 0.93 0.93 0.92 0.91 3.5 1.6
## JST_8 210 0.94 0.94 0.92 0.92 3.4 1.6
## JST_9 210 0.92 0.92 0.90 0.89 3.5 1.6
##
## Non missing response frequency for each item
## 1 2 3 4 5 miss
## JST_1 0.29 0.06 0.09 0.11 0.45 0.79
## JST_2 0.25 0.09 0.11 0.14 0.41 0.79
## JST_3 0.22 0.09 0.14 0.15 0.40 0.79
## JST_4 0.25 0.08 0.10 0.17 0.40 0.79
## JST_5 0.27 0.07 0.10 0.16 0.40 0.79
## JST_7 0.21 0.12 0.10 0.13 0.43 0.79
## JST_8 0.21 0.11 0.12 0.16 0.40 0.79
## JST_9 0.20 0.08 0.15 0.16 0.40 0.79sessionInfo()## R version 4.4.3 (2025-02-28 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows 11 x64 (build 26100)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_United States.utf8
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
##
## time zone: Asia/Manila
## tzcode source: internal
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] psych_2.4.12
##
## loaded via a namespace (and not attached):
## [1] digest_0.6.37 R6_2.6.1 fastmap_1.2.0
## [4] xfun_0.51 lattice_0.22-6 GPArotation_2024.3-1
## [7] cachem_1.1.0 parallel_4.4.3 knitr_1.49
## [10] htmltools_0.5.8.1 rmarkdown_2.29 lifecycle_1.0.4
## [13] cli_3.6.4 grid_4.4.3 sass_0.4.9
## [16] jquerylib_0.1.4 mnormt_2.1.1 compiler_4.4.3
## [19] rstudioapi_0.17.1 tools_4.4.3 nlme_3.1-167
## [22] evaluate_1.0.3 bslib_0.9.0 yaml_2.3.10
## [25] rlang_1.1.5 jsonlite_1.9.1