A Research about Measurement Invariance of Attitude
Participating in Field Hockey Sport
Dr. Mao-Chun Chiu, Department of Leisure, Recreation & Health Business Management,
Tajen University, Taiwan
ABSTRACT
The research goal is to test the measurement invariance of attitude participating in field hockey
sport. By utilizing cluster sampling, three hundred and seventy-nine valid samples were selected and
analyzed for descriptive analysis and structural equation model by statistical software SPSS12.0 and
AMOS 16.0. Results revealed: First, there was no difference between the expected covariance equation
and sample covariance. Second, there were no significant differences among measurement model,
structural model, and covariance model after cross-validity analysis. At last, concrete suggestions are
provided to government and other hockey sport organizations for further improvement.
Keywords: Field hockey, attitude toward sport participation
INTRODUCTION
Doing exercise is effective for health improvement. With the economic growth in Taiwan, the
awareness of leisure improves in the public, and the rapid increase of their participation in leisure and
sports. Field hockey is an ancient sport with long history; the ball is small with high speed and is played
in a spacious field with players‟ high skills (Tsai, 1996). Field hockey is suitable for eastern countries
because of its limitlessness of body type and gender. Being included in the competition events in Asian
Games, field hockey has potential for improvement in Taiwan. Ice hockey and field hockey are two kinds
of hockey sport, and the latter is suitable to be promoted in Taiwan for its warm weather. The field,
number of players, and game tactics are similar to those of soccer game; therefore, field hockey has
become a popular leisure sport of the public.
Myers (1993) addressed that one‟s attitude is formed by his consciousness, emotion, and behavioral
tendency. Attitude toward exercise is, regardless of results, one‟s or most people‟s gain to their health
physically, satisfaction, happiness, and spirituality rely on adequate time and space with their independent
willingness. Attitude refers to an individual‟s constancy and coherence tendency towards human affairs
and his/her surroundings. This tendency could be predicted by one‟s external behavior, but the
connotation not only means external behavior, attitude generally includes consciousness, emotion, and
behavior (Chang, 1989). Hence, for the measurement of attitude toward exercise, it could be individually
analyzed one‟s attitude by feelings of doing exercise, belief, and tendency toward exercise; whether their
attitude are positive or negative, liking or disliking, participating it or not. By realizing one‟s attitude
researchers could predict his level of preference to the consciousness, favor and behavior of one matter
(Wang, Chen, 2006).
Ajzen and Fishbein‟s Reasoned Action Theory which addressed in 1980 indicated that attitude plays
an important role in constructing behavioral intention, and intention is a norm in predicting the
occurrence of actual behavior. Ajen (1988) considered attitude as a reflection tendency from some
particular people whether they continuously like or dislike what they learned, Promoting sport
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
61
participating behavior has been a focus worldwide for decades, relevant theory indicated that attitude is a
crucial factor affecting an individual‟s behavior and activity (Noland & Feldman, 1985).
The previous studies mainly focused on the impacts of participation hindrance to leisure behavior
(Huang, Chen, 2005; Alexandris, Tsorbatoudis & Grouios, 2002; Alexandris & Carroll, 1997; Alexandris
& Carroll, 1999). This research attempts to utilize a strict testing process to construct a model of attitude
toward participating field hockey sport. Through a series of testing analysis on factor structure, a most
parsimonious factor model is expected. Reliability, convergent validity, and discriminant validity of this
factor model will be examined; also, the measurement invariance of attitude participating in field
hockey sport will be tested by cross validity.
RESEARCH METHOD
Research Structure
Based on relevant references, this research selected quantification research methods and tested the
results by Structural Equation Modeling. Statistical Model is shown in diagram 1.
1
e8
1
e7
1
e6
1
e5
1
e4
1
e3
1
e2
1
e1
1
e23
1
e22
1
e21
1
e20
I1
B1
1
B2
B3
Benefits
B5
I5
Involvement
I6
B6
I7
1
I8
B8
I9
I10
A1
I11
Ability and
skill
A2
A3
A4
I3
I4
B4
B7
I2
1
1
Achievement and
Satisfaction
S1
S2
S3
S4
S5
1
e9
1
e10
1
e11
1
e12
1
e13
1
e14
1
e15
1
e16
1
e17
1
e18
1
e19
1
e24
1
e25
1
e26
1
e27
1
e28
Diagram 1: Statistical Model
Research Hypothesis
Structural Equation Model (SEM) use co-variance model to estimate the mutual-support and
correlative relationship among several Multi-Regression Equations. (Lee, Ko, Wu, Yu, 2004; Jöreskog &
Sörbom ,1992) Chen (2007) emphasized that structural equation model adopted the covariant equation
testing variable relationship among the variables, the diversity between the covariance equation and
sample covariance equation of the received θ model ought to be smaller, the smaller the better. Chin
(1998) indicated that the goodness of fit of SEM is not allowed to be inferior. It refers to the obvious
disparity between the model and the sample and the wrong model design, and will result in the following
incorrect research outcomes. Hence, this research‟s first step is to advance the hypothesis to the goodness
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The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
of fit of this model, ，S-Σ(θ)=0, S is the sample covariance equation, and Σ(θ) is the model expected
covariance equation.
H1: There is no difference between the model expected covariance equation of the model and the sample
covariance equation.
H2: The model of attitude toward participating in filed hockey sport possesses measurement invariance.
Research Tool
(1) Questionnaire Design
Two sections in this research questionnaire are: the scale about the model of attitude toward
participating in field hockey sport and personal information.
1. Questionnaire Model about attitude participating in field hockey sport
This questionnaire was revised from Yang and Ku‟s research and their scale of university students‟
attitude toward leisure sport in 2004. This questionnaire includes twenty-eight questions in re sport
benefit, sport involvement, ability and skill, achievement and satisfaction respectively.
2. Descriptive Statistics
This questionnaire was revised from Yang and Ku‟s research and their scale of university students‟
attitude toward leisure sport in 2004. A descriptive analysis includes gender, group, and annual income
respectively.
(2) Likert Scaling
Bollen (1989) indicated that seven-point Likert scale in SEM practically reveals the best performance,
and therefore was selected for the questionnaire scoring. Four questionnaire items are benefits from sport,
investment on sport, ability and skills, achievement and psychological satisfaction; and categories of this
scale are rated from “Strongly agree” (seven points) to “Strongly disagree” (one point).
Sampling
The duration for data collection was from August, 5th to 13th, 2014 with random sampling of
subjects as participants in National Presidential In-door Hockey Cup in 2014. Those participants were
questioned an hour before the competitions which began in Changhua Stadium, and 400 questionnaires
were collected.
Sample Estimation and Statistical Power
After settling the SEM model, the amount of samples needs to be determined before sample
collection. Based on the RMSEA estimation method provided by MacCallum, Browne and Sugawara in
1966, and by utilizing the minimum sample size computed by R language and the degree of freedom 344
in this survey for estimation, the minimum sample size is 88.67. The effective sample size in this survey
is 379 and it fits the suggested value mentioned above. Also, the statistical power is 1, which is bigger
than the suggested value 0.80. (Maxwell, Kelley, & Rausch, 2008) Results revealed that the statistical
power is ideal.
RESULTS
Descriptive Statistics of Sample Characters
Among the effective samples in this research, 52.2 percent of subjects are males and 47.8 percent
are females; which refers to 198 and 181 respectively. In the case of “group”, 105 subjects were studying
in elementary school, 126 in the junior high school, 87 in senior high school, and 61 in the adult group.
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
63
As for the item “income”, in 323 people‟s monthly salary is under twenty thousand dollars, 37 people are
in the group between 20,001 to 30,000; only 19 people earn more than thirty thousand dollars.
Table 1: Summary of Descriptive Statistics about Sample Characters
Information Item
Category Standard
Sample size
Male
198
Gender
Female
181
Elementary School
105
Junior High School
126
Group
Senior High School
87
Adult
61
Under 20,000
323
Income
20,001-30,000 dollars
37
Above 30,001
19
Percentage
52.2
47.8
27.7
33.2
23.0
16.1
85.2
9.8
5.0
Measurement and Structural Model Analysis
(1) Confirmation of Convergent Validity
Confirmatory Factor Analysis (CFA) is a crucial step of SEM analysis. This survey, was amended based
on the two-stage model from Kline in 2005, downsized the variables of CFA measurement model. First, test the
measurement model before implementing the structural model evaluation. If the goodness of fit of the
measured model is acceptable, complete SEM model evaluation will be conducted in the second stage.
This research is aimed to conduct CFA analysis on the four aspects in this model as the benefits
from sport, investment on sport, ability and skills, achievement and psychological satisfaction. Factor
loading of all aspects are among 0.74 to 0.94; all reach the significant standard. Composite reliability is
between the number of 0.95 to 0.98; and the average variance extracted is between the number of 0.78 to
0.83 (shown in Table 2), which fits the suggested value addressed by Hair, Anderson, Tatham and Black
in 1998, and by Fornell and Larcker in 1981.
Aspect
Benefits
Involvement
64
Table 2: Reliability Analysis of Potential Aspects
StandarNon-StanardC.R.
Norm
S.E.
dized Loading dized Loading
(t-value)
B1
0.90
1.00
B2
0.88
0.97
0.04 26.40
B3
0.91
1.01
0.03 28.75
B4
0.86
0.96
0.04 25.01
B5
0.94
1.02
0.03 31.18
B6
0.94
1.01
0.03 31.58
B7
0.94
1.01
0.03 31.15
B8
0.90
0.96
0.03 28.15
I1
0.85
1.00
I2
0.89
1.06
0.04 24.30
I3
0.92
1.07
0.04 25.67
I4
0.94
1.07
0.04 27.03
I5
0.89
1.06
0.04 24.11
I6
0.90
1.04
0.04 24.52
I7
0.92
1.05
0.04 25.52
I8
0.85
1.09
0.05 21.98
I9
0.92
1.03
0.04 25.64
P
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
SMC C.R. AVE
0.81 0.97 0.83
0.77
0.82
0.74
0.88
0.88
0.88
0.81
0.73 0.98 0.80
0.80
0.84
0.89
0.79
0.81
0.84
0.72
0.84
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
Ability and skill
Achievement
and Satisfaction
I10
I11
A1
A2
A3
A4
S1
S2
S3
S4
S5
0.85
0.91
0.92
0.92
0.91
0.88
0.74
0.92
0.91
0.91
0.92
0.95
1.13
1.00
0.98
0.99
0.91
1.00
1.08
1.04
1.12
1.02
0.04
0.05
22.04
25.02
0.03
0.03
0.03
31.03
30.06
27.12
0.06
0.06
0.06
0.05
18.99
18.73
18.75
18.88
*** 0.72
*** 0.82
0.84 0.95 0.82
*** 0.84
*** 0.82
*** 0.77
0.54 0.95 0.78
*** 0.84
*** 0.82
*** 0.82
*** 0.84
(2)Confirmation of Discriminant Validity
The aim of discriminant validity analysis is to confirm whether the difference occurs to two
different aspects. This survey chose confidence interval method (Torkzadeh, Koufteros, Pflughoeft, 2003)
to confirm the confidence interval of correlation coefficient between the two aspects. If the interval
excludes 1 (which reaches the complete correlation), discriminant validity exists between aspects. To
establish a confidence interval of correlation coefficient in SEM under the circumstance of confidence
level of 95%, this survey utilized Bootstrap estimation. If the confidence interval excludes 1, null
hypotheses are refused, and the four aspects possess discriminant validity. Hancock and Nevitt (1999)
suggested that the number of Bootstrapping should be more than two hundred and fifty times when
estimating the path coefficient. This survey, with a 95% confident level, repeatedly used sampling over
one thousand times to retrieve the confidence interval of standardized correlation coefficient.
AMOS bootstrap provides estimation for two confidence intervals, which are Bias-corrected
Percentile Method and Percentile Method. Results of these two estimations are shown in Table 3－all
standardized confidence interval of correlation coefficient exclude 1, which means discriminant validity
exists between one aspect and another.
Table 3: Bootstrap Correlation Coefficient 95% Confidence Interval
Bias-corrected Percentile method
Parameter
Estimate
Lower Upper Lower
Upper
Benefits
<-->
Ability and skill
.93
.90
.95
.90
.95
Benefits
<-->
Involvement
.92
.88
.94
.87
.94
Benefits
<--> Achievement and Satisfaction
.93
.90
.95
.89
.95
Involvement <-->
Ability and skill
.95
.91
.97
.91
.97
Ability and skill <--> Achievement and Satisfaction
.97
.95
.99
.95
.99
Involvement <--> Achievement and Satisfaction
.95
.91
.97
.91
.97
(3) Structural Model Analysis
If the sample size of SEM is over 200, it normally causes bigger chi-squared value.
(χ2=(n-1)Fmin). Fmin is the minimum value of difference between sample equation and expectation
equation. The bigger the sample size it is, the higher the chi-squared value it becomes. P value therefore
becomes easily be refused (Chang, 2011). For this reason, Bollen and Stine addressed Bootstrape for
amendment in 1992.
The chi value of Bollen-stine p correction is 527.94, and the original ML chi value is 2399.92. Because
the chi-value became smaller, all goodness of fit requires computation again. Results are listed in Table 4.
Structural model analysis includes model fitness and explanatory power of entire research model. This
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
65
research took Bagozzi and Yi (1988), Bentler (1995), and Hair (1988)‟s opinions, using seven indexes for
evaluation for goodness of fit of over-all model, including chi-value test (χ2), χ2 and ratio of degree of freedom,
goodness of fit index (GFI), adjusted goodness of fit index (AGFI), root mean square error of approximation
(RMSEA), comparative fit index (CFI), and normed fit index (NFI). Results are listed in Table 6.
Bagozzi and Yi (1988) emphasized that, taking χ2 and ratio of degree of freedom to test the
goodness of fit, the degree is ideal to be smaller. The ratio of this research is ＜3(1.53); Hair, etc. (1988)
indicated that GFI and AGFI are ideal to close to 1, no absolute standard to judge the goodness of fit.
Baumgartner and Homburg (1996) recommended that the values of GFI and AGFI in the research need to
be higher than 0.90. GFI and AGFI in this research model are 0.97 and 0.96 respectively. Browne and
Cudeck (1993) explained a good model with reasonable fir if its RMSEA is lower than 0.08. The RMSEA
of this research model is 0.04, the allowable standard is ＞0.90. CFI in this research model is 0.99. NFI
value as least needs to be higher than 0.90, while the NFI is 0.97 in this research model. Over all, the fit
indices are within the standard value, which showed the research results are acceptable. Also, the data of
the samples in this research can be used to explain practical observation data.
Table 4: Fitness Analysis of Research Model
Allowable Range
Research Model
the smaller the better
527.94
378
<3
1.53
>0.9
0.97
>0.9
0.96
<.08
0.04
>0.9
0.99
>0.9
0.97
Fit Indices
χ2(Chi-square)
DF
χ2/DF
GFI
AGFI
RMSEA
CFI
NFI
Judgment of model fit
Fit
Fit
Fit
Fit
Fit
Fit
I1
.80
B1
e8
I2
.77
B2
e7
.90
.88
.91
.86
.83
B3
e6
.73
B4
e5
.88
.85
.89
.92
.94
.90
.94
Benefits
B5
e4
.88
e3
B6
.94
.84
.93
.91
B7
.83
.93
B8
e1
A1
.92
.92
.85
A2
e22
.80
.90
A3
e21
.95
.93
.85
e23
.90
.80
A4
I5
I6
.94
.88
e2
e20
I4
.89
Involvement
.92
I3
.93
I8
.86
.95
I7
I9
.90
I10
I11
Ability and
skill
.97
Achievement and
Satisfaction
S1
.71
.91
S2
.92
.93 .92
S3
S4
S5
.72
e9
.79
e10
.84
e11
.88
e12
.81
e13
.80
e14
.86
e15
.70
e16
.86
e17
.73
e18
.81
e19
.51
e24
.83
e25
.85
e26
.84
e27
.87
e28
Diagram 2: Model of Attitude Participating in Field Hockey Sport
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The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
(4) Cross Validity
This research is operated under the circumstance that the assumption of the research model is
accurate, and two groups are compared after random sampling. (Cliff, 1983; Cudeck & Broene, 1983;
Hairs, etc., 1983) To deeply test the stability of this model, this research progressively restrict three
coefficients, including path coefficient of the measured model, path coefficient of structure and structural
covariance. If no obvious differences are found in this model, it owns a certain level of stability.
1. Set equal path coefficient to two groups, twenty four factor loadings in total are designed equally in
structural model (DF=24) and Chi-square measure (CMIN) increased 12.83. Besides, the test result
p=.97 and failed to reach the significant level, which means setting twenty four factor loading
equally is acceptable.
2. Besides remaining the restriction of the measurement model, plus setting ten structural path
coefficient
(DF=34-24=10),
Chi-square
measure
(CMIN)
increased
18.06
(CMIN=30.89-12.83=18.06). The test result p=.62 and failed to reach the significant level; in other
words, these ten structural path coefficient are acceptable, and are the same.
3. With the same restriction to the structural efficient model, and extra twenty eight set structural
variances and covariances (DF=62-34=28). Chi-square measure increased 109.74
(CMIN=140.63-30.89=109.74).The test result p= 0.00 and has reached the significant level 0.05;
which means making these twenty eight variances and covariances equal is unacceptable. These
twenty eight variances and covariances are unequal.
Chung and Rensvold (2002) brought up the practical significance of test based on CFI norm.
Results showed ΔCFI≦0.01 after the simulation, which means that no differences between the two nested
structural model. Nevertheless, Little (1997) addressed that ΔTLI≦0.05 is the standard of having no
difference among nested structural model. The invariance comparison of ΔCFI and ΔTLI both fit the
suggested value mentioned as well as the requirement of congruent groups. This model is stable and fits
the standard of cross validity.
Table 5: Comparison of group interval invariance
Model
χ2
df
Measurement weights
Structural covariances
Measurement residuals
12.83
30.89
140.63
24
34
62
Δdf
10
28
Δχ2
P
CFI
18.06
109.74
.97
.62
.00
.87
.87
.87
ΔCFI
TLI
.00
.00
-.01
-.01
-.01
CONCLUSION
Most subjects in this survey are males, currently in junior high school, and monthly income lower
than 20,000. This research includes three stages and questionnaires are analyzed by confirmatory factor
analysis. Research results revealed that this questionnaire includes benefit, involvement, achievement and
satisfaction, ability and skill, four factors with twenty eight questions in total. For the application of
structural equation model, Bollen (1989) considered that Likert seven-point scale is ideal to reduce the
over-skewed results, and confirmatory research is suitable because the variance level is greater. Therefore,
the questionnaire design selected was the seven-point scale.
After analyzed by composite reliability, average variance extracted, this questionnaire possesses
good convergent validity. With bootstrap confident interval, two estimated methods－Bias-corrected
Percentile Method and Percentile Method were used to estimate disterminant validity. Results showed
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
67
that every potential variable in this questionnaire is distinguishable. Seven scales in this research were
evaluated for overall model fit, including χ2 test, ratio of χ2 and degree of freedom, goodness of fit (GFI),
adjusted goodness of fit index (AGFI), root mean square error of approximation (RMSEA), and
comparative fit index (CFI), parsimony-adjusted comparative fit index (PCFI), results showed good
model fit. Therefore, the first hypothesis that there is no difference between the model expected
covariance equation of the model and the sample covariance equation is valid.
This research randomly conducted cross-validity confirmation of two groups, both ΔCFI and ΔTLI
as the invariance comparison are smaller than the standard suggested by the scholars, and therefore fit the
requirement of equal group interval. This model has stability and fit the cross-validity. As the result, the
second hypothesis that the model of attitude toward participating in field hockey sport possesses
measurement invariance is valid.
SUGGESTIONS
Academic contribution of this research
In the past thirty years, structural equation model has become a widely used statistical technology.
Researchers applied SEM to construction models and attempted to know the relationship among variables.
By operating the model to test the relationship of hypotheses, and further collect data for evaluation.
According to Schreiber (2008), McDonald and Ho (2002), Boomsma (2000), and Hoyle and Panter (1995)
„s suggestions, well-designed SEM research articles must display sample size, statistical power, the
version of statistical software used (AMOS 16.0), goodness of fit, Chi-square measurement, multi fit
indices (GFI, AGFI, CFI, NFI, RMSEA…), parameter estimation of measurement and structure including
standardized and non-standardized estimated value and the reports of significance, SMC and explainable
variance, final statistical model diagram, and cross validity. Based on the recommendation, this research
provided a more complete statistical analysis report for other researchers in the future.
Statistical power evaluation is very crucial work, because sample size plays the key role while
testing the model. (Bollen, 1989) The degree of freedom in this research shows the minimum number of
the sample size needs to be higher than 344; the valid sample size is 396 and has reached its standard.
Under the request of statistical power, the general statistical power is ideal to be 0.8 (Maxwell, Kelley &
Rausch, 2008), and the statistical power in this research is 1.0 and is ideal for statistical analysis.
Suggestions for Field Hockey Sport Participation
Field hockey sport helps relieve players‟ study pressure and tension, this research recommends
schools and relevant organizations to form field hockey clubs for promotion and pressure relief. Although
the number of hockey players in Taiwan is lower than it in other countries; the researchers expect that by
the active promotion of sports fair council can field hockey sport become popular in Taiwan and further
produce positive impact on players‟ skill and improvement. In the trend of globalization, the demarcation
lines among nations are blurred and bring more frequent skill interactions in countries. Inviting coaches
from other countries for player training in Taiwan to improve competition level is also highly
recommended.
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The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue
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Appendix 1: Covariance Matrix
B1
2.203
1.826
1.758
1.694
1.737
1.862
1.755
1.722
1.392
1.421
1.693
1.713
1.632
1.582
1.668
1.474
1.684
1.675
1.644
1.639
1.595
1.590
1.569
1.171
1.570
1.793
1.648
1.666
70
B2
1.826
2.163
1.770
1.702
1.732
1.720
1.680
1.695
1.377
1.449
1.562
1.543
1.548
1.453
1.585
1.382
1.569
1.481
1.528
1.594
1.553
1.541
1.538
1.311
1.545
1.722
1.606
1.557
B3
1.758
1.770
2.162
1.713
1.861
1.763
1.808
1.714
1.475
1.535
1.562
1.637
1.686
1.540
1.662
1.450
1.608
1.491
1.605
1.715
1.784
1.656
1.659
1.437
1.696
1.662
1.803
1.560
B4
1.694
1.702
1.713
2.213
1.773
1.677
1.688
1.710
1.305
1.407
1.443
1.432
1.529
1.429
1.570
1.371
1.526
1.469
1.469
1.551
1.513
1.549
1.548
1.202
1.491
1.627
1.567
1.532
B5
1.737
1.732
1.861
1.773
2.107
1.824
1.869
1.729
1.498
1.607
1.637
1.643
1.785
1.608
1.728
1.575
1.692
1.549
1.667
1.723
1.719
1.672
1.596
1.482
1.633
1.669
1.764
1.642
B6
1.862
1.720
1.763
1.677
1.824
2.046
1.854
1.704
1.375
1.468
1.596
1.658
1.649
1.580
1.658
1.447
1.604
1.549
1.618
1.661
1.660
1.629
1.567
1.198
1.533
1.734
1.650
1.624
B7
1.755
1.680
1.808
1.688
1.869
1.854
2.050
1.663
1.534
1.620
1.651
1.628
1.774
1.656
1.685
1.545
1.624
1.465
1.761
1.784
1.713
1.716
1.578
1.438
1.660
1.658
1.788
1.623
B8
1.722
1.695
1.714
1.710
1.729
1.704
1.663
1.987
1.444
1.503
1.593
1.578
1.620
1.546
1.700
1.480
1.632
1.598
1.565
1.667
1.665
1.675
1.666
1.276
1.581
1.704
1.673
1.584
I1
1.392
1.377
1.475
1.305
1.498
1.375
1.534
1.444
2.125
1.857
1.740
1.628
1.571
1.667
1.574
1.717
1.446
1.307
1.824
1.595
1.499
1.535
1.514
1.538
1.500
1.464
1.659
1.533
I2
1.421
1.449
1.535
1.407
1.607
1.468
1.620
1.503
1.857
2.187
1.801
1.723
1.716
1.654
1.700
1.874
1.658
1.450
1.927
1.818
1.655
1.589
1.551
1.624
1.652
1.540
1.767
1.654
I3
1.693
1.562
1.562
1.443
1.637
1.596
1.651
1.593
1.740
1.801
2.080
1.827
1.665
1.646
1.723
1.747
1.654
1.577
1.883
1.761
1.668
1.608
1.549
1.470
1.604
1.659
1.776
1.637
I4
1.713
1.543
1.637
1.432
1.643
1.658
1.628
1.578
1.628
1.723
1.827
1.998
1.757
1.775
1.724
1.783
1.682
1.578
1.849
1.702
1.745
1.624
1.540
1.317
1.531
1.596
1.746
1.656
I5
1.632
1.548
1.686
1.529
1.785
1.649
1.774
1.620
1.571
1.716
1.665
1.757
2.194
1.700
1.820
1.790
1.738
1.677
1.795
1.838
1.793
1.757
1.630
1.551
1.696
1.604
1.845
1.652
I6
1.582
1.453
1.540
1.429
1.608
1.580
1.656
1.546
1.667
1.654
1.646
1.775
1.700
2.078
1.684
1.791
1.654
1.490
1.874
1.574
1.652
1.586
1.540
1.353
1.544
1.588
1.711
1.593
I7
1.668
1.585
1.662
1.570
1.728
1.658
1.685
1.700
1.574
1.700
1.723
1.724
1.820
1.684
2.044
1.789
1.749
1.658
1.720
1.754
1.818
1.691
1.736
1.513
1.712
1.733
1.863
1.636
I8
1.474
1.382
1.450
1.371
1.575
1.447
1.545
1.480
1.717
1.874
1.747
1.783
1.790
1.791
1.789
2.564
1.744
1.467
1.984
1.668
1.681
1.599
1.546
1.465
1.596
1.569
1.752
1.545
I9
1.684
1.569
1.608
1.526
1.692
1.604
1.624
1.632
1.446
1.658
1.654
1.682
1.738
1.654
1.749
1.744
1.945
1.676
1.789
1.688
1.688
1.611
1.576
1.456
1.638
1.656
1.717
1.638
I10
1.675
1.481
1.491
1.469
1.549
1.549
1.465
1.598
1.307
1.450
1.577
1.578
1.677
1.490
1.658
1.467
1.676
1.948
1.579
1.516
1.513
1.496
1.483
1.235
1.496
1.597
1.528
1.510
I11
1.644
1.528
1.605
1.469
1.667
1.618
1.761
1.565
1.824
1.927
1.883
1.849
1.795
1.874
1.720
1.984
1.789
1.579
2.376
1.845
1.756
1.705
1.583
1.463
1.689
1.614
1.791
1.678
A1
1.639
1.594
1.715
1.551
1.723
1.661
1.784
1.667
1.595
1.818
1.761
1.702
1.838
1.574
1.754
1.668
1.688
1.516
1.845
2.213
1.888
1.868
1.649
1.583
1.768
1.666
1.834
1.753
A2
1.595
1.553
1.784
1.513
1.719
1.660
1.713
1.665
1.499
1.655
1.668
1.745
1.793
1.652
1.818
1.681
1.688
1.513
1.756
1.888
2.132
1.795
1.677
1.528
1.710
1.633
1.928
1.647
A3
1.590
1.541
1.656
1.549
1.672
1.629
1.716
1.675
1.535
1.589
1.608
1.624
1.757
1.586
1.691
1.599
1.611
1.496
1.705
1.868
1.795
2.231
1.770
1.515
1.713
1.637
1.825
1.647
A4
1.569
1.538
1.659
1.548
1.596
1.567
1.578
1.666
1.514
1.551
1.549
1.540
1.630
1.540
1.736
1.546
1.576
1.483
1.583
1.649
1.677
1.770
2.010
1.447
1.661
1.696
1.840
1.593
S1
1.171
1.311
1.437
1.202
1.482
1.198
1.438
1.276
1.538
1.624
1.470
1.317
1.551
1.353
1.513
1.465
1.456
1.235
1.463
1.583
1.528
1.515
1.447
2.821
1.878
1.390
1.802
1.510
S2
1.570
1.545
1.696
1.491
1.633
1.533
1.660
1.581
1.500
1.652
1.604
1.531
1.696
1.544
1.712
1.596
1.638
1.496
1.689
1.768
1.710
1.713
1.661
1.878
2.104
1.732
1.873
1.662
S3
1.793
1.722
1.662
1.627
1.669
1.734
1.658
1.704
1.464
1.540
1.659
1.596
1.604
1.588
1.733
1.569
1.656
1.597
1.614
1.666
1.633
1.637
1.696
1.390
1.732
2.001
1.788
1.709
S4
1.648
1.606
1.803
1.567
1.764
1.650
1.788
1.673
1.659
1.767
1.776
1.746
1.845
1.711
1.863
1.752
1.717
1.528
1.791
1.834
1.928
1.825
1.840
1.802
1.873
1.788
2.336
1.780
S5
1.666
1.557
1.560
1.532
1.642
1.624
1.623
1.584
1.533
1.654
1.637
1.656
1.652
1.593
1.636
1.545
1.638
1.510
1.678
1.753
1.647
1.647
1.593
1.510
1.662
1.709
1.780
1.912
The Journal of Human Resource and Adult Learning, Vol. 10, Num. 2, December, 2014 issue