(PDF) Supporting the Statistical Analysis of Variability Models

Research Paper - Statistical
Analysis Support

Table of Contents
INTRODUCTION...........................................................................................................................1
Measurement model:...................................................................................................................1
Chi-square test.............................................................................................................................1
Hypotheses testing.......................................................................................................................4
Hypothesized consequences:.......................................................................................................6
Research measure:.......................................................................................................................7
Results:......................................................................................................................................10
CONCLUSION..............................................................................................................................11
REFERENCES..............................................................................................................................12

INTRODUCTION
Statistical analysis is the numerical evaluation of the given data or collected from the
research work. It will be helpful for making reliable decision making in near future time. This
project report consists of analysis of measurement model that consist of four major factors. Apart
from this, various test, analysis, methods are used to calculated the impacts and relationship
among each other.
Measurement model:
All the factors analysis by the help of using T-value measurement model:
One-Sample Statistics
N Mean Std. Deviation Std. Error Mean
Affect Mean 33 6.1818 1.08362 .18863
Loyal Mean 34 5.6667 .95346 .16352
Contribute Mean 34 5.4902 1.29811 .22262
Prof Rasp Mean 34 5.9510 1.43813 .24664
One-Sample Test
Test Value = 0
t df Sig. (2-tailed) Mean Difference 95% Confidence Interval of the Difference
Lower Upper
Affect Mean 32.77
1 32 .000 6.18182 5.7976 6.5661
Loyal Mean 34.65
5 33 .000 5.66667 5.3340 5.9993
Contribute Mean 24.66
1 33 .000 5.49020 5.0373 5.9431
Prof Rasp Mean 24.12
8 33 .000 5.95098 5.4492 6.4528
Chi-square test
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
Affect Mean * GM_LMX 33 97.1% 1 2.9% 34 100.0%
Loyal Mean * GM_LMX 34 100.0% 0 0.0% 34 100.0%
Contribute Mean * GM_LMX 34 100.0% 0 0.0% 34 100.0%
1

Prof Rasp Mean * GM_LMX 34 100.0% 0 0.0% 34 100.0%
2

Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 324.821a 286 .057
Likelihood Ratio 124.990 286 1.000
Linear-by-Linear Association 17.357 1 .000
N of Valid Cases 34
a. 324 cells (100.0%) have expected count less than 5. The minimum expected
count is .03.
The confirmatory factor analysis be done to determine the impacts on the five important
factors under this particular research project. The overall outcomes can be attained by the help of
using specific formulation such as Chi-square test analysis (χ2 =, df =, p <), p< 0.01), it would
determine an improper result. Though, it would provide the chi-square test distribution of the
value is squared standard that is normally deviates. It should be the degrees of freedom of
distribution which is equal to the overall number of standard normal deviates being equal. This
would be absolute techniques that is suitable as complex to wide sample sizes and non-normality
factor distribution of all the important variables. While additional aspects of this method is that
the exponential distribution which is primarily considered as hypothesis testing. The substitute
fits indicate that the measurement method used to provide a sensible result from the given data
Such as AM (32.77), Loyal mean (34.655), CM (24.661) and prof, respect mean (24.128).
Although the results say that the minimum value which is calculated from the given problems is
based on the confidence level of 0.90. As the internal consistency reliabilities for each of the
major four LMX dimension ranged from 0.90 to 0.96. Chi-square test for the given variables are
mentioned accordingly such as Affect mean of the variable is 229.24, loyal mean is 294.1 it is
having predictable close to 5, the least total is .03. Contribution value is 308 which have
expected is also taken into account as 5 and prof. resp. mean value comes out to be 324. This
above table is being presented the items of their loading t-values as well as the inner reliability
which is estimated to be related with five variable used during the research project of LMX and
overall job agreement. The mean as well as standard deviation between these constructed are
shown in below mentioned table.
Descriptive Statistics
Mean Std. Deviation N
3

Affect Mean 6.1818 1.08362 33
Loyal Mean 5.6667 .95346 34
Contribute Mean 5.4902 1.29811 34
Prof Rasp Mean 5.9510 1.43813 34
OrgID_Mean 4.2402 .54463 34
Correlations
GM_LMX Affect Mean Loyal Mean Contribute
Mean
Prof
RespMean
Pearson Correlation
GM_LMX 1.000 .802 .722 .694 .635
Affect Mean .802 1.000 .354 .333 .607
Loyal Mean .722 .354 1.000 .583 .183
Contribute_Mean .694 .333 .583 1.000 .020
ProfResp_Mean .635 .607 .183 .020 1.000
Sig. (1-tailed)
GM_LMX . .000 .000 .000 .000
Affect_Mean .000 . .022 .029 .000
Loyal_Mean .000 .022 . .000 .154
Contribute_Mean .000 .029 .000 . .456
ProfResp_Mean .000 .000 .154 .456 .
N
GM_LMX 33 33 33 33 33
Affect_Mean 33 33 33 33 33
Loyal_Mean 33 33 33 33 33
Contribute_Mean 33 33 33 33 33
ProfResp_Mean 33 33 33 33 33
Hypotheses testing
(H0-H1):
The impact of LMX on confidence level of the people those are working within an organisation.
In order to perform well and attain more reliable outcomes, researcher has used multiple
regression evaluation stood showed to analyse effectively LMX influences of the variable that
are taken into account for this particular research. The both leader and other groups. As, it would
indicate overall level of their non-profit sport organisation. As earlier, 34 % of the participants
used to responded that they used analyse the responsibility and obligations within an
organisation. After making overall consideration of the facts that each independent variable
shares variance with a dependent variable in order to observe essential shares at once, every of
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the eight LMX variables are associated with the leader and follower those are included into the
multiple regression calculation as independent variables.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .929a .863 .854 .30365
a. Predictors: (Constant), Loyal_Mean, Affect_Mean
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 17.388 2 8.694 94.292 .000b
Residual 2.766 30 .092
Total 20.154 32
a. Dependent Variable: GMLMX2
b. Predictors: (Constant), Loyal_Mean, Affect_Mean
Coefficientsa
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. 95.0% Confidence Interval
for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constant) .707 .382 1.852 .074 -.073 1.486
Affect_Me
an .458 .053 .625 8.646 .000 .350 .566
Loyal_Me
an .413 .060 .500 6.920 .000 .291 .535
a. Dependent Variable: GMLMX2
Model Summary
Mode
l
R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .938a .879 .871 .31370 .879 112.717 2 31 .000
5

a. Predictors: (Constant), Prof Resp Mean, Contribute Mean
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 22.184 2 11.092 112.717 .000b
Residual 3.051 31 .098
Total 25.234 33
a. Dependent Variable: GMLMX2
b. Predictors: (Constant), Prof Resp Mean, Contribute Mean
Coefficientsa
Model Unstandardized
Coefficients
Standardize
d
Coefficients
t Sig. 95.0% Confidence Interval
for B
B Std. Error Beta Lower
Bound
Upper
Bound
1
(Constant) 1.364 .301 4.536 .000 .751 1.977
Contribute_Mean .409 .043 .608 9.546 .000 .322 .497
ProfResp_Mean .369 .039 .606 9.522 .000 .290 .447
a. Dependent Variable: GMLMX2
From the above multiple regression table of the various factors such as Affected mean
and loyalty is independent factors, while LMX is considered as dependent in the given date
series. Predictors accounted for approximation of 95 confidence level the regression value of the
variables are mentioned accordingly (R2= 0.879, Adjusted R² = 0.871). Advanced ranks of the
follower’s professional respects (β= 625, P< 0.05) and loyalty means is having (β=0.500, P<
0.001) were associated with higher level of growth percentage. The mean value of the fours
factors is valued by the followers in which graded as surveys. Like, affect (M= 6.181),
professional respects(M=5.9510), Contribution (M=5.4902) and Loyalty (M=5.6667).
Hypothesized consequences:
Under this particular research, respondents were asked regarding their ranking of
financial contribution (1= the school is considered as my priority, 5= do not donated at all).
While certain analysis has been taken into account the actual amount of contribution as
indicative of attachment, this seems to be questionable. First, contribution is increasingly
affected by income which tends to be associated with variable factors.
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Research measure:
Case Processing Summary
Cases
Included Excluded Total
N Percent N Percent N Percent
GMLMX2 * Affect_Mean 33 97.1% 1 2.9% 34 100.0%
GMLMX2 * Loyal_Mean 34 100.0% 0 0.0% 34 100.0%
GMLMX2 *
Contribute_Mean 34 100.0% 0 0.0% 34 100.0%
GMLMX2 * ProfResp_Mean 34 100.0% 0 0.0% 34 100.0%
Report
GMLMX2
Affect_Mean Mean N Std. Deviation
2.33 3.8333 1 .
4.33 4.5833 2 .23570
4.67 4.9167 2 .23570
5.33 5.5000 1 .
5.67 5.4444 3 .04811
6.00 5.7500 2 .35355
6.33 6.1833 5 .46173
6.67 6.0833 4 .34021
7.00 6.3333 13 .66231
Total 5.8712 33 .79361
ANOVA Table
Sum of
Squares
df Mean
Square
F Sig.
GMLMX2 *
Affect_Mean
Between
Groups
(Combined) 13.449 8 1.681 6.018 .000
Linearity 12.972 1 12.972 46.43
6 .000
Deviation from
Linearity .477 7 .068 .244 .969
Within Groups 6.705 24 .279
Total 20.154 32
Measures of Association
7

R R Squared Eta Eta Squared
GMLMX2 * Affect_Mean .802 .644 .817 .667
GMLMX2 * Loyal Mean
Report
GMLMX2
Loyal_Mean Mean N Std. Deviation
2.33 4.5000 1 .
4.00 5.5833 1 .
4.33 4.7500 1 .
4.67 4.9583 2 .76603
5.00 5.4167 3 .62915
5.33 5.2667 5 .92308
5.67 5.6389 3 .31549
6.00 6.3750 8 .37268
6.33 5.7000 5 1.20127
6.67 6.7917 2 .05893
7.00 6.6667 3 .44096
Total 5.8039 34 .87446
ANOVA Table
Sum of
Square
s
df Mean
Square
F Sig.
GMLMX2 *
Loyal_Mean
Between
Groups
(Combined) 13.112 10 1.311 2.48
8 .034
Linearity 8.622 1 8.622 16.3
59 .001
Deviation
from
Linearity
4.489 9 .499 .946 .506
Within Groups 12.123 23 .527
Total 25.234 33
Measures of Association
R R Squared Eta Eta Squared
GMLMX2 * Loyal_Mean .585 .342 .721 .520
8

GMLMX2 * Contribute Mean
Report
GMLMX2
Contribute_Mean Mean N Std. Deviation
1.67 4.5000 1 .
3.33 3.7083 2 .17678
4.00 5.2083 2 .64818
4.33 5.8611 3 .39382
4.67 5.2917 2 .29463
5.00 6.2500 1 .
5.33 5.7333 5 .80017
5.67 5.4722 3 .04811
6.00 6.2917 2 .17678
6.33 5.9167 5 .77504
6.67 6.8333 1 .
7.00 6.6429 7 .31074
Total 5.8039 34 .87446
ANOVA Table
Sum
of
Squar
es
df Mean
Squar
e
F Sig.
GMLMX2 *
Contribute_
Mean
Betwee
n
Groups
(Combined
) 18.807 11 1.710 5.8
52
.00
0
Linearity 13.262 1 13.26
2
45.
394
.00
0
Deviation
from
Linearity
5.545 10 .554 1.8
98
.10
1
Within Groups 6.428 22 .292
Total 25.234 33
Measures of Association
R R Squared Eta Eta Squared
9

GMLMX2 * Contribute Mean .725 .526 .863 .745
GMLMX2 * Prof Resp Mean
ANOVA Table
Sum of
Squares
df Mean
Square
F Sig.
GMLMX2 * Pro
fResp Mean
Between
Groups
(Combined) 17.629 11 1.603 4.63
6 .001
Linearity 13.217 1 13.217 38.2
31 .000
Deviation
from Linearity 4.412 10 .441 1.27
6 .302
Within Groups 7.605 22 .346
Total 25.234 33
Measures of Association
R R Squared Eta Eta Squared
GMLMX2 * Prof Resp Mean .724 .524 .836 .699
Results:
As, it has been shown in the above table about the mean value of four hypothesized
organization factors that are associated with the research work. To assess the unique contribution
of every factors in order to determine the latter was regressed on all the four factors. All the
factors are providing regression value of .724, it means that there is direct relationship among the
unique contribution of examine to each outcome variable was taken into account while
formulating the correlation table for the analysis. The standardized regression coefficients used
to provide reliable strong support for the remaining results variable of the given problem. The
current study is based on current analysis of the few studies in which data were collected from
leader and follower. As per the earlier analysis, leader LMX and followers LMX is having
various relationships with different organizational outcomes.
CONCLUSION
From the above project research, it has been concluded that various statistical tools are taken
into account such as regression analysis, correlation, mean and standard deviation of the four
10

factors such as affect, loyalty, contribution mean, professional mean. These are showing
effective outcomes after making proper analysis of the matters.
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REFERENCES
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education research. Routledge.
Kraemer, H.C. and Blasey, C., 2015. How many subjects? Statistical power analysis in research.
Sage Publications.
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analysis and structural equation modeling. Sas Institute.
Smith, S.L., 2014. Tourism analysis: A handbook. Routledge.
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research. Insect Science, 20(2), pp.254-260.
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