2 Background The research focuses on investigating leaders from highly rated managed care organizations based on their leadership practices in comparison to leaders from low rated managed care organizations. High rated organizations are managed care organizations who have attained either 4.5 or 5 Medicare Stars ratings whiles low ratings organizations are organizations who have attained 3 Stars or less. The research design: Survey was sent to leaders from both high Medicare rated and low rated organizations. I believe I have enough sample size so the result will be significant. I have received 35 response from leaders from high rated organizations and 35 from low rated organizations (35 participants each responded, making 70 participants in total). The goal is to find out if there is a significant difference in leadership practice between leaders from highly rated organizations and low rated organizations. The survey tool used is Leadership Practice Inventory (LPI), which has a total of 30 behavioral statements that reflect on the practices leaders regularly use in managing their organizations. The leaders were invited to complete the survey online. The 30 survey questions are grouped in 5 Models: 1. Model the Way 1. Inspire a Shared Vision 1. Challenge the Process 1. Enable Others to Act 1. Encourage the Heart The participants completed the LPI self-test, where they must rate themselves depending on the frequency, which they believe in engaging in each of the five models. They rate themselves on a 10 point likert scale, below. 1-Almost Never 3-Seldom 5-Occasionally 7-Fairly Often 9-Very Frequently 2-Rarely 4-Once in a While 6-Sometimes 8-Usually 10-Almost always 1. Dependent Variable: Attaining high Overall Medicare Star Rating 1. Independent Variables: 1. Leadership practice Practices (Model the Way, Inspire a Shared Vision, Challenge the Process, Enable Others to Act, and Encourage the Heart) 1. Years of Experience 1. Leadership Style Abbreviations meaning: LP- Leadership Practice MSR – Medicare Stars Ratings MSROs – Medicare Stars Ratings Organizations YoE – Years of Experience The following hypotheses has been tested, analyzed (page 4-23). SPSS software was used for data analysis. Hypothesis 1 - There is a significant difference in LP between leaders from high (4.5 or 5) MSROs and low (3 Stars or less) MSROs. Hypothesis 2 – There is a strong relationship between MSRs and the LP of both high and low MSROs Hypothesis 3 - In comparison to other 4 models (thus Model the Way, Challenge the Process, Enable Others to Act, Encourage the Hearts), practicing the “Inspire A Shared Vision” model is very significant in helping leaders influence the attainment of high MSR in MCOs. Hypothesis 4 – The leaders’ leadership style contributes to a leader’s ability to influence the achievement of high Medicare ratings for MCO. Hypothesis 5 – The Leaders’ of Years of Experience (YoE) is effective in enabling leaders influence the attainment o.

1. 2
Background
The research focuses on investigating leaders from highly rated
managed care organizations based on their leadership practices
in comparison to leaders from low rated managed care
organizations. High rated organizations are managed care
organizations who have attained either 4.5 or 5 Medicare Stars
ratings whiles low ratings organizations are organizations who
have attained 3 Stars or less.
The research design: Survey was sent to leaders from both high
Medicare rated and low rated organizations. I believe I have
enough sample size so the result will be significant. I have
received 35 response from leaders from high rated organizations
and 35 from low rated organizations (35 participants each
responded, making 70 participants in total). The goal is to find
out if there is a significant difference in leadership practice
between leaders from highly rated organizations and low rated
organizations.
The survey tool used is Leadership Practice Inventory (LPI),
which has a total of 30 behavioral statements that reflect on the
practices leaders regularly use in managing their organizations.
The leaders were invited to complete the survey online. The 30
survey questions are grouped in 5 Models:
1. Model the Way
1. Inspire a Shared Vision
1. Challenge the Process
1. Enable Others to Act
1. Encourage the Heart
The participants completed the LPI self-test, where they must
rate themselves depending on the frequency, which they believe
in engaging in each of the five models. They rate themselves on
a 10 point likert scale, below.
1-Almost Never

2. 3-Seldom
5-Occasionally
7-Fairly Often
9-Very Frequently
2-Rarely
4-Once in a While
6-Sometimes
8-Usually
10-Almost always
1. Dependent Variable: Attaining high Overall Medicare Star
Rating
1. Independent Variables:
1. Leadership practice Practices (Model the Way, Inspire a
Shared Vision, Challenge the Process, Enable Others to Act, and
Encourage the Heart)
1. Years of Experience
1. Leadership Style
Abbreviations meaning:
LP- Leadership Practice
MSR – Medicare Stars Ratings
MSROs – Medicare Stars Ratings Organizations
YoE – Years of Experience
The following hypotheses has been tested, analyzed (page 4-23).
SPSS software was used for data analysis.
Hypothesis 1 - There is a significant difference in LP between

3. leaders from high (4.5 or 5) MSROs and low (3 Stars or less)
MSROs.
Hypothesis 2 – There is a strong relationship between MSRs and
the LP of both high and low MSROs
Hypothesis 3 - In comparison to other 4 models (thus Model the
Way, Challenge the Process, Enable Others to Act, Encourage
the Hearts), practicing the “Inspire A Shared Vision” model is
very significant in helping leaders influence the attainment of
high MSR in MCOs.
Hypothesis 4 – The leaders’ leadership style contributes to a
leader’s ability to influence the achievement of high Medicare
ratings for MCO.
Hypothesis 5 – The Leaders’ of Years of Experience (YoE) is
effective in enabling leaders influence the attainment of either a
high MSRs in MCOs
Hypothesis 6- Leadership practice is highly effective in helping
a leader influence the attainment of high MSR in MCOs in
regard to leader’s years of experience and leadership styles,
Hypothesis 1 - There is a significant difference in LP between
leaders from high (4.5 or 5) MSROs and low (3 Stars or less)
MSROs.

4. To check if there is a difference in the LPs between leaders
from high MSROs and low MSROs, a non-parametric
independent sample test is conducted using SPSS software, see
Table 1. According to the results, mean for high MSRO leaders
is 8.56 and low MSRO leaders is 7.00 whiles the p-value =
0.0001 which is less than .05 significance level. Therefore, the
null hypothesis Hₒ is rejected and H1 is accepted. There is a
significant difference in LPs between the two (2) leader groups.
Table 1: Independent Sample Test for Hypothesis 1
Descriptive Statistics
N
Mean
Std. Deviation
Minimum
Maximum
Leadership_Practice_Low_Med_Stars
35
7.0048
.99529
4.67
9.43
Leadership_Practice_High_Med_Stars
35
8.5600
.70876
7.60
10.07
Medicare Stars Ratings
35
3.3857
.47489
2.50
4.00

5. Hypothesis 2 – There is a strong relationship between MSRs and
the LP of both high and low MSROs
Correlation is used to check how strong the relationship the two
variables is. To interpret the correlation matrix, we need to look
at the significance value (p) for reach value of r. A strong
positive relationship can be overserved between the dependent
variable (Medicare Star Ratings) and the independent variable
(Leaders from high MSROs), where the value of Pearson r is
.834 and the p > .05 (p = .001), therefore the H1 should be
accepted and the H0 rejected. Since the p-value is significant, a
significant correlation exists between the two variables. See
Table 2
The results of the test also indicate that there is also a strong
positive relationship between MSR and leaders from low
MSROs. The value of the Pearson r is.817 and the p > .05 (p =
.000), therefore the H1 should be accepted and the H2 rejected.,
a statistically significant correlation exists between the two
variables since p-value is significant, see Table below 2
Correlations
Medicare Stars Ratings
Leadership_Practice_Low_Med_Stars
Leadership_Practice_High_Med_Stars
Pearson Correlation
Medicare Stars Ratings

6. 1.000
.817
.834
Leadership Practice Low Medicare Stars
.817
1.000
.946
Leadership Practice High Medicare Stars
.834
.946
1.000
Sig. (1-tailed)
Medicare Stars Ratings
.
.000
.000
Leadership Practice Low Medicare Stars
.000
.
.000
Leadership Practice High Medicare Stars
.000
.000
.
N
Medicare Stars Ratings
35
35
35
Leadership Practice Low Medicare Stars
35

7. 35
35
Leadership Practice High Medicare Stars
35
35
35
Table 2: Correlation between MSR and LP of leaders of high
MSROs and low MSROs.
The Model Summary table in Table 3 provides R and R2 values.
The R value represents the simple correlation which is .839 (the
"R" Column), and it indicates a high degree of correlation.
The R2 value (the "R Square" column) indicates how much of
the total variation in the dependent variable MSR can be
explained by the independent variable (Leadership practices of
both high & low MSROs). In this case, 70.4% can be explained,
which is very high. See Table below 3
The Coefficientsa table in Table 3 shows the beta weight and
significance value for the individual independent variables. The
regression model is perfect at 70.4% only, then the beta
coefficient is .126 for leaders from low MSROs with p-value of
.379, which is statistically insignificant. For leaders from high
MSROs, the beta coefficient is .392 and p-value is .057, which
also statistically insignificant. This implies that each variable is
not predictive enough on its own to be statistically significant.
However, the sample provides enough evidence to conclude that
the model is significant but not enough to conclude that the
individual variable is significant. The p-value is 0.000 for the
overall model in the ANOVA in Table 3, hence H0 is rejected as
a result.

8. Table 3: Linear regression of MSR and LP of leaders of high
MSROs and low MSROs.
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
1
.839a
.704
.685
.26653
a. Predictors: (Constant),
Leadership_Practice_High_Med_Stars,
Leadership_Practice_Low_Med_Stars
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
5.395
2
2.697
37.968
.000b
Residual
2.273
32

9. .071
Total
7.668
34
a. Dependent Variable: MedicareStarsRatingsM
b. Predictors: (Constant),
Leadership_Practice_High_Med_Stars,
Leadership_Practice_Low_Med_Stars
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
-.851
.828
-1.028
.312
Leadership_Practice_Low_Med_Stars

10. .126
.141
.264
.892
.379
Leadership_Practice_High_Med_Stars
.392
.198
.585
1.978
.057
a. Dependent Variable: Medicare Stars Ratings
Hypothesis 3 - In comparison to other 4 models (thus Model the
Way, Challenge the Process, Enable Others to Act, Encourage
the Hearts), practicing the “Inspire A Shared Vision” model is
very significant in helping leaders influence the attainment of
high MSR in MCOs.
The below correlations matrix (Table 4) provides details of the
dependent variable (MSR) and the five (5) principles of the
independent (Leadership Practices) to determine the association
between each principle and the dependent variable. The first
correlation between Medicare Stars ratings and Challenge the
Process (R3) model, where Pearson r is .850 and p-value is
.000, this shows there is a strong positive relationship as p
value is less than .01. For the relationship between MSR and
Enable Other to Act (R4) model where the Pearson r is .566 and
p-value is .000 this shows there is also a strong positive

11. relationship as p value is less than .01. The relationship
between MSR and Encourage the Hearts (R5) model as the
Pearson r is .557 and p-value is .000, this shows there is a
strong positive relationship as p-value is less than .01. The
relationship between MSR and Inspire the Heart (R2) model as
the Pearson r is .874 and p-value is .000, this shows there is a
strong positive relationship as p-value is less than .01. The
relationship between MSR and Model the Way (R1) model as
the Pearson r is .622 and p-value is .001, this shows there is a
strong positive relationship as p value is less than .01.
Table 4: Correlation between Medicare Stars ratings and the
Five (5) leadership models.
Correlations
Variable
MedStar#
ChallengeM
EnableM
EncourageM
InspireM
ModelM
MedicareStar#
Pearson Correlation
1
.850**
.566**
.557**
.874**

12. .622**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
70
70
70
70
70
70
Challenge(R3)
Pearson Correlation
.850**
1
.534**
.630**
.818**
.658**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
70

13. 70
70
70
70
70
Enable(R4)
Pearson Correlation
.566**
.534**
1
.375**
.535**
.441**
Sig. (2-tailed)
.000
.000
.001
.000
.000
N
70
70
70
70
70
70
Encourage(R5)
Pearson Correlation
.557**
.630**
.375**
1
.599**

14. .709**
Sig. (2-tailed)
.000
.000
.001
.000
.000
N
70
70
70
70
70
70
Inspire(R2)
Pearson Correlation
.874**
.818**
.535**
.599**
1
.704**
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
70

15. 70
70
70
70
70
Model(R1)
Pearson Correlation
.622**
.658**
.441**
.709**
.704**
1
Sig. (2-tailed)
.000
.000
.000
.000
.000
N
70
70
70
70
70
70
**. Correlation is significant at the 0.01 level (2-tailed).
Hierarchical Regression for Medicare Stars rating and five (5)
principles
The Model Summary in Table 5 below provides the R and
R2 values for the two (2) models of hierarchical regression in
the first (1st) model. The R-value represents the simple

16. correlation which is .863 (the “R” column), which indicates
there is a high degree of correlation between the variables. The
R2 value (the “R Square” column) indicates how much of the
total variation in the dependent variable (Medicare Stars rating)
can be explained by the independent variable Model the
Way(R1), Challenge the Process(R3), Enable Others to Act(R4),
and Encourage the Hearts(R5). In this case, 74.50% can be
explained, which is very high. There model 1 is statistically
significant. The p-value in ANOVA table (Table 5) is .000.
In the second (2nd) model, the R value represents the simple
correlation and it is .909 (the “R” column), which also indicate
a high degree of correlation. The R2 value (the “R Square”
column) indicates how much of the total variation in the
dependent variable (Medicare Stars rating) can be explained by
the independent variable (Model the Way, Challenge the
Process, Enable Others to Act, and Encourage the Hearts). Here,
82.60% can be explained, which is very high. It implies that
when the other four (4) variables were added to Inspired the
Vision (R2) principle, the r-square increases. This model
increases the model’s predictive capacity in predicting the
attainment of high Medicare Stars ratings in a statistically
significant way by increasing the percentage accounted for by
8.1%. See Table 5
The F-ratio in the ANOVA table tests whether the overall
regression model is a good fit for the data. Table 5 shows that
the independent variables statistically and significantly predict
the dependent variable, F(5, 64) = 60.673, p < .0005, thus the
regression model is a good fit of the data. All five (5)
predicators accounted for a significant proportion of unique
criterion in the final regression model. As seen in the chart
(Figure 1), there is a strong positive correlation between the
two (2) variables.
The below table (Table 5) shows the results for the hierarchical
regression coefficient between the two (2) models. In the first
(1st) model, the dependent variable is Medicare Stars ratings
and the independent variables are Model the Way[R1],

17. Challenge the Process[R3], Enable Others to Act[R4], and
Encourage the Hearts[R5]. In this model, as the regression
model is perfect which 74.50% only, then the beta coefficient
for Challenge the Process (R3) is .693 and the significance
value is .000. This result shows the relationship between the
two (2) variables is significant. The beta coeffect for Enable
Other to Act (R4) is 102 where p-value is .054. The beta
coefficient for Model the Way (R1) is .114 where p-value is
.310.
In the second (2nd) model, the dependent variable is MSR and
the independent variables are: Inspire a Vision (R2), Model the
Way(R1), Challenge the Process(R3), Enable Others to Act(R4),
and Encourage the Hearts(R5). Also, the p-value in ANOVA
table (Table 5) is .000 as well, therefore the H0 is rejected for
the 2nd model. In this model, the regression is perfect which is
82.6% only, then the beta coefficient for Challenge the Process
(R3) is .388 and significance value is .000. This result indicates
the relationship between the two (2) variables is significant.
The beta coeffect for Enables Other to Act (R4) is .063 and p-
value is 0.156, which shows the relationship is statistically
insignificant. The beta coeffect for Model the Way (R1) is -.58
and p-value is .555, which is also statistically insignificant. The
beta coefficient for Encourage the Heart (R5) is -.21 and p-
value is .790, which is also a statistically insignificant
relationship. But when Inspire a Vision (R2) is added to the
other four (4) variables, then the beta coefficient for Inspire a
vision (R2) is .575 and p-value is .000. It implies that, in
comparison to the other four(4) variables: Model the Way[R1],
Challenge the Process[R3], Enable Others to Act[R4], and
Encourage the Hearts[R5], practicing Inspire a Vision [R2]
principle is very effective in enabling leaders influence the
attainment of high Medicare Stars rate in MCOs.

18. Table 5: Hierarchical Regression for Medicare Stars rating and
five (5) principles
Model Summaryc
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1
df2
Sig. F Change
1
.863a
.745
.729
.701
.745
47.422
4
65

19. .000
2
.909b
.826
.812
.584
.081
29.756
1
64
.000
a. Predictors: (Constant), Model, Enable, Challenge, Encourage
b. Predictors: (Constant), Model, Enable, Challenge, Encourage,
Inspire
c. Dependent Variable: MedStar#
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
Correlations
B
Std. Error
Beta
Zero-order
Partial
Part
1
(Constant)
-3.460
.585

20. -5.914
.000
Challenge(R3)
.693
.090
.721
7.723
.000
.850
.692
.484
Enable(R4)
.102
.052
.147
1.959
.054
.566
.236
.123
Encourage(R5)
-.023
.094
-.023
-.248
.805
.557
-.031
-.016

21. Model(R1)
.114
.111
.099
1.024
.310
.622
.126
.064
2
(Constant)
-4.158
.504
-8.255
.000
Challenge(R3)
.388
.093
.404
4.159
.000
.850
.461
.217
Enable(R4)
.063
.044
.091
1.437

22. .156
.566
.177
.075
Encourage(R5)
-.021
.078
-.021
-.268
.790
.557
-.033
-.014
Model(R1)
-.058
.098
-.051
-.593
.555
.622
-.074
-.031
Inspire(R2)
.575
.105
.543
5.455
.000
.874
.563
.285
a. Dependent Variable: MedStar#

23. ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
93.162
4
23.291
47.422
.000b
Residual
31.924
65
.491
Total
125.086
69
2
Regression
103.294
5
20.659
60.673
.000c

24. Residual
21.792
64
.340
Total
125.086
69
a. Dependent Variable: MedStar#
b. Predictors: (Constant), Model, Enable, Challenge, Encourage
c. Predictors: (Constant), Model, Enable, Challenge, Encourage,
Inspire
Figure 1: Histogram and Scatterplot for Medicare Stars rating
and five (5) principle

25. Hypothesis 4 – The leaders’ leadership style contributes to a
leader’s ability to influence the achievement of high Medicare
ratings for MCO.
Correlation is used to check how strong the relationship the two
variables is. To interpret the correlation matrix, we need to look
at the significance value (p) for reach value of r. In Table 5, a
moderate positive linear relationship exists between the
dependent variables (Medicare Stars ratings) and the
independent variables (the leadership styles of leaders from low
Medicare Star ratings organizations), where Pearson r is .392
and the p-value is .20 at 0.5 level of significance. Hence, the p-
value is statistically significant. From this result, a correlation
exists between Medicare Stars ratings and leadership style of
leaders from low MSROs.
In comparison to the correlation that exists between MSR
(dependent variable) and the leadership style of leaders from
high MSROs (independent variable), a moderate positive linear
relationship exists between the two (2) variables, where Pearson
r is .329, see Table 5. However, the p-value is .54 at .5 level of
significance. The p-value is not significant ([p > 0.5], p= .54)
unlike the p-value of the correlation of Medicare Stars ratings
and the leadership styles of leaders from MSROs ([p > 0.5], p=
.20), though there is a positive correlation.
Table 5: Correlation between MSR and leadership style of high
and low MSROs.
Correlations

26. Leadership Style High Star
Leadership Style Low Star
Medicare Stars Ratings
Leadership Style High Star
Pearson Correlation
1
-.060
.329
Sig. (2-tailed)
.733
.054
N
35
35
35
Leadership Style Low Star
Pearson Correlation
-.060
1
.392*
Sig. (2-tailed)
.733
.020
N
35
35
35
Medicare Stars Ratings
Pearson Correlation
.329

27. .392*
1
Sig. (2-tailed)
.054
.020
N
35
35
35
*. Correlation is significant at the 0.05 level (2-tailed).
Table 6 below provides the R and R2 values. The R value
represents the simple correlation which is 0.685 (the "R"
Column), which indicates a high degree of correlation.
The R2 value (the "R Square" column) measures the strength of
the relationship between the model and dependent variable
MSR. In this case, 47.00% can be explained, which is low.
Table 6 below also shows the results for the simple linear
regression between the dependent variable (Medicare Stars
ratings) and the independent variables (leadership style). This
regression model is not perfect as it is only 47%, then the beta
coefficient is .200 and a significant value is .001 for leadership
style of leaders from low MSROs and it is statistically
significant. The beta for leadership style of leaders from high
MSROs is .180 and p-value is .000, which is also statistically
significant.
Table 6: Hierarchical Regression for Medicare Stars rating and
leadership styles of leadership from both low and high MSROs.
Model Summaryb
Model
R

28. R Square
Adjusted R Square
Std. Error of the Estimate
1
.685a
.470
.437
.219
a. Predictors: (Constant), Leadership Style High Star,
Leadership Style Low Star
b. Dependent Variable: Medicare Star Rating
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
1.362
2
.681
14.172
.000b
Residual
1.538
32
.048
Total
2.900

29. 34
a. Dependent Variable: Medicare Star Rating
b. Predictors: (Constant), Leadership Style High Star,
Leadership Style Low Star
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
B
Std. Error
Beta
1
(Constant)
3.795
.157
24.218
.000
Leadership Style Low Star
.200
.052
.492
3.816
.001
Leadership Style High Star

30. .180
.046
.507
3.933
.000
a. Dependent Variable: Medicare Star Rating
Residuals Statisticsa
Minimum
Maximum
Mean
Std. Deviation
N
Predicted Value
4.17
4.93
4.60
.200
35
Residual
-.574
.445
.000
.213
35
Std. Predicted Value
-2.124
1.673
.000
1.000
35
Std. Residual
-2.620
2.030
.000

31. .970
35
a. Dependent Variable: Medicare Star Rating
Hypothesis 5 – The Leaders’ of Years of Experience (YoE) is
effective in enabling leaders influence the attainment of either a
high MSRs in MCOs
The correlation matrix below (Table 7) measures the strength
and direction of the relationship between the two (2) variable.
There is a weak positive linear relationship between the
dependent variable (MSR) and independent variable (years of
experience of leaders from high MSROs), where Pearson r .109,
and p-value is .267 at 0.5 level of significance. Hence, it is
statistically insignificant. This means a correlation does not
exist between MSR and YoE of leaders from high MSROs.
The correlation that exists between MSR (dependent variable)
and YoE of leaders from high MSROs is a moderate positive
linear relationship, where Pearson r is .583 and p-value is .000
at .05 level of significance. The p-value is significant ([p >
0.5], p= .000) unlike the p-value of the correlation of MSR and
YoE of leaders from high MSROs ([p > 0.5], p= .267).
Table 7: Correlation between MSR and Years of Experience of
leaders from high and low MSROs.
Correlations
Variables
Medicare Stars Ratings
YoE Low Medicare Stars
YoE High Medicare Stars
Pearson Correlation
Medicare Stars Ratings

32. 1.000
.583
.109
YoE_Low_Med_Stars
.583
1.000
.160
YoE_High_Med_Stars
.109
.160
1.000
Sig. (1-tailed)
Medicare Stars Ratings
.
.000
.267
YoE Low Medicare Stars
.000
.
.179
YoE High Medicare Stars
.267
.179
.
N
Medicare Stars Ratings
35
35
35
YoE Low Medicare Stars
35

33. 35
35
YoE High Medicare Stars
35
35
35
Table 8 below shows the R and R2 values. The R value
represents the simple correlation which is 0.583 (the "R"
Column), which indicates a moderate degree of correlation.
The R2 value (the "R Square" column) indicates how much of
the total variation in the dependent variable Medicare star rating
can be explained by the independent variable leaders YoE. In
this case, 29.90% can be explained, which is low very.
The results displayed in Table 8 shows the simple linear
regression between the dependent variable (MSR) and the
independent variable (leaders’ YoE). This regression model is
not perfect as it is only 34%, then the beta coefficient for YoE
of leaders from low MSROs is .346 and p-value is .000, which
is statistically significant. However, the beta coefficient for
YoE of leaders from high Medicare Stars is .012 and p-value is
.915, which is not statistically significant. The sample provides
enough evidence to conclude that the model is significant but
not enough to conclude that the individual variable is
significant. The p-value is 0.001 for the overall model in the
ANOVA in Table 8, hence H0 is rejected as a result.

34. Table 8: Hierarchical Regression for Medicare Stars rating and
leaders YoE
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1
df2
Sig. F Change
1
.583a
.340
.299
.39765
.340
8.247
2
32
.001
a. Predictors: (Constant), YoE_Low_Med_Stars YoE High
Medicare Stars
ANOVAa
Model
Sum of Squares

35. df
Mean Square
F
Sig.
1
Regression
2.608
2
1.304
8.247
.001b
Residual
5.060
32
.158
Total
7.668
34
a. Dependent Variable: Medicare Stars Ratings
b. Predictors: (Constant), YoE_Low_Med_Stars YoE High
Medicare Stars
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.

36. B
Std. Error
Beta
1
(Constant)
2.775
.280
9.922
.000
YoE_Low_Med_Stars
.346
.087
.580
3.990
.000
YoE_High_Med_Stars
.012
.108
.016
.108
.915
a. Dependent Variable: Medicare Stars Ratings
Hypothesis 6- Leadership practice is highly effective in helping

37. a leader influence the attainment of high MSR in MCOs in
regard to leader’s years of experience and leadership styles,
The correlation matrix gives details of the dependent variable
(Medicare Stars ratings) and three (3) independent variables:
Leadership Practice (LP), Years of Leadership Experience, and
Leadership Style to check the association between the variables.
See Table 9.
The first correlation relationship between MSR and leadership
practice where Pearson r is .839 and p-value is .000, which
shows there is a strong and significant relationship as p-value is
less than .01. The relationship between MSR and leaders’ YoE
is Pearson r is .533 and p-value is .000, this indicates there is a
moderate and significant relationship as p-value is less than .01.
The relationship between MSR and leader’s leadership style is
Pearson r is .197 and p-value is .102, this shows there is a weak
and insignificant relationship as p-value is greater than .01, see
Table 9.
Table 9: Correlation between MSR and leadership style,
leadership practice and leaders’ years of experience
Correlations
Medicare Star Ratings
Leadership Practice
Years of Leadership Experience
Leadership Style
Medicare Star Ratings
Pearson Correlation
1
.839**
.533**
.197
Sig. (2-tailed)
.000
.000

38. .102
N
70
70
70
70
Leadership Practice
Pearson Correlation
.839**
1
.604**
.342**
Sig. (2-tailed)
.000
.000
.004
N
70
70
70
70
Years of Leadership Experience
Pearson Correlation
.533**
.604**
1
.348**
Sig. (2-tailed)
.000
.000

39. .003
N
70
70
70
70
Leadership Style
Pearson Correlation
.197
.342**
.348**
1
Sig. (2-tailed)
.102
.004
.003
N
70
70
70
70
**. Correlation is significant at the 0.01 level (2-tailed).
The Model Summary in Table 10 provides R and R2 values for
the two (2) models of hierarchical regression in the first (1st)
model. The R value represents the simple correlation which is
.534 (the “R” column), which indicates there is a moderate
positive relationship between the variables. The R2 value (the
“R” Square” column) indicates how much of the total variation
in the dependent variable (MSRs) can be explained by the
independent variable: Leadership Practice, Leaders’ Years of
Experience, and Leadership Style. In this case, 28.50% can be

40. explained, which is very low, though the p-value is .000. There
model 1 is statistically significant.
In the second (2nd) model, the R value represents the simple
correlation which .846 (the “R” column), which also indicate a
high degree of correlation. The R2 value (the “R” Square”
column) indicates how much of the total variation in the
dependent variable (Medicare Stars rating) can be explained by
the independent variable: Leadership Practice, Leaders’ Years
of Experience, and Leadership Style. In this model, 71.50% can
be explained which is very high. This indicates that the r-square
increases when leadership practice is employed in addition to
leadership style and years of experience. This model increases
predictive capacity in predicting the leaders’ influence in
attaining high MSR in a statistically significant way by
increasing the percentage accounted by 43.10% in the 2nd
model. See Table 10
The F-ratio in the ANOVA table tests whether the overall
regression model is a good fit for the data (Table 10). The
independent variables statistically and significantly predict the
dependent variable, F(3, 66) = 55,251, p-value is .000 as p <
.05, thus the regression model is good git of the data.
Furthermore, the result of the hierarchical regression coefficient
between the two (2) models is shown in the Coefficientsa
section, Table 10. In the first (1st) model, the dependent
variable is MSRs and the independent variables are: Leaders’
Years of Experience, and Leadership Style. In this model, the
regression model is moderate 28.50% only, then the beta
coefficient for Years of Leadership is .902 and p-value is .000.
This result shows the relationship between the two (2) variables
is significant. The beta coefficient for Leadership Style is .024
where p-value is .906. which is statistically insignificant.
In the second (2nd) model, the dependent variable (MSR) and
the independent variables: Leadership Practice, Leaders’ Years
of Experience, and Leadership Style. With this model, the
regression is perfect which is 71.50%. then the beta coefficient
for Leaders’ Years of Experience is .115 and p-value is .426,

41. which is statistically insignificant. The beta coefficient for
Leadership Style is -.207 and p-value is .118, which is also
statistically insignificant. However, when Leadership Practice is
added to the other two (2) variables, then the beta coefficient
for Leadership Practice is .970 and p-value is .000. This shows
that, in comparison to the independent variable in the 1st model,
practicing Leadership Practice is very effective in helping
leaders influence the attainment of high MSR in MCOs; hence
H0 is rejected as a result.
Table 10: Hierarchical Regression for Medicare Stars rating and
leadership practice, leaders’ YoE and leadership style.
Model Summaryc
Model
R
R Square
Adjusted R Square
Std. Error of the Estimate
Change Statistics
R Square Change
F Change
df1

42. df2
Sig. F Change
1
.534a
.285
.263
1.156
.285
13.333
2
67
.000
2
.846b
.715
.702
.735
.431
99.777
1
66
.000
a. Predictors: (Constant), Leadership Style, Years of Leadership
Experience
b. Predictors: (Constant), Leadership Style, Years of Leadership
Experience, LeadPracM
c. Dependent Variable: MedStar#
ANOVAa
Model
Sum of Squares
df
Mean Square
F
Sig.
1

43. Regression
35.610
2
17.805
13.333
.000b
Residual
89.476
67
1.335
Total
125.086
69
2
Regression
89.463
3
29.821
55.251
.000c
Residual
35.622
66
.540
Total

44. 125.086
69
a. Dependent Variable: MedStar#
b. Predictors: (Constant), Leadership Style, Years of Leadership
Experience
c. Predictors: (Constant), Leadership Style, Years of Leadership
Experience, LeadPracM
Coefficientsa
Model
Unstandardized Coefficients
Standardized Coefficients
t
Sig.
Correlations
B
Std. Error
Beta
Zero-order
Partial
Part
1
(Constant)
1.518
.483
3.145
.002

45. Years of Leadership Experience
.902
.188
.529
4.799
.000
.533
.506
.496
Leadership Style
.024
.202
.013
.118
.906
.197
.014
.012
2
(Constant)
-3.955
.628
-6.298
.000
Years of Leadership Experience
.115
.143
.067

46. .800
.426
.533
.098
.053
Leadership Style
-.207
.131
-.113
-1.583
.118
.197
-.191
-.104
Leadership Practice
.970
.097
.837
9.989
.000
.839
.776
.656
a. Dependent Variable: MedStar#