Factor Analysis: Determining Underlying Variables

This article discusses the results and significance of factor analysis, a statistical tool used to determine underlying variables from recorded variables. The article includes a correlation matrix, KMO and Bartlett's test, total variance explained, component matrix, and more. The results show positive significant results for the data set.

Advanced Quantitative
Methods

Table of Contents
Question 1...................................................................................................................................4
Question 2.................................................................................................................................10
Question 3.................................................................................................................................20
Question 4.................................................................................................................................22
Question 5.................................................................................................................................23
REFERENCES .............................................................................................................................26

Question 1
Factor analysis: this is consider to be an effective statistical tool which is used in
determining the underlying variables that are computed with more number of recorded variables
(Arianti, 2018).
RESULTS
Correlation Matrixa
tendenc
y to eat
healthil
y
satisfacti
on with
work
satisfacti
on with
relations
hip
how
often
go on
holid
ay
amou
nt
drinki
ng
water
per
day
(glass)
hours
exercis
e per
week
quali
ty of
sleep
enjoy
hobbi
es
goi
ng
to
chu
rch
etc
Correlati
on
tendency
to eat
healthily
1.000 .060 .005 .085 .721 .217 .151 .138 .12
8
satisfacti
on with
work
.060 1.000 .743 .727 .077 .254 .227 .545 .54
1
satisfacti
on with
relations
hip
.005 .743 1.000 .530 .021 .144 .184 .446 .36
8
how
often go
on
holiday
.085 .727 .530 1.000 .106 .228 .254 .557 .54
7
amount
drinking
water per
day
(glass)
.721 .077 .021 .106 1.000 .183 .090 .150 .17
6
hours
exercise
per week
.217 .254 .144 .228 .183 1.000 .749 .303 .26
0

quality of
sleep .151 .227 .184 .254 .090 .749 1.000 .278 .23
6
enjoy
hobbies .138 .545 .446 .557 .150 .303 .278 1.000 .77
5
going to
church
etc
.128 .541 .368 .547 .176 .260 .236 .775 1.0
00
Sig. (1-
tailed)
tendency
to eat
healthily
.172 .466 .090 .000 .000 .008 .014 .02
2
satisfacti
on with
work
.172 .000 .000 .114 .000 .000 .000 .00
0
satisfacti
on with
relations
hip
.466 .000 .000 .368 .011 .002 .000 .00
0
how
often go
on
holiday
.090 .000 .000 .046 .000 .000 .000 .00
0
amount
drinking
water per
day
(glass)
.000 .114 .368 .046 .002 .078 .009 .00
3
hours
exercise
per week
.000 .000 .011 .000 .002 .000 .000 .00
0
quality of
sleep .008 .000 .002 .000 .078 .000 .000 .00
0
enjoy
hobbies .014 .000 .000 .000 .009 .000 .000 .00
0
going to
church
etc
.022 .000 .000 .000 .003 .000 .000 .000
a. Determinant = .008
KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling
Adequacy. .699
Bartlett's Test of
Sphericity
Approx. Chi-Square 1174.214
df 36
Sig. .000
Communalities
Initial Extraction
tendency to eat
healthily 1.000 .847
satisfaction with work 1.000 .787
satisfaction with
relationship 1.000 .610
how often go on
holiday 1.000 .691
amount drinking water
per day (glass) 1.000 .858
hours exercise per week 1.000 .872
quality of sleep 1.000 .875
enjoy hobbies 1.000 .664
going to church etc 1.000 .628
Extraction Method: Principal Component
Analysis.
Total Variance Explained
Compone
nt
Initial Eigenvalues Extraction Sums of Squared
Loadings
Rotation Sums of
Squared Loadingsa
Tota
l
% of
Variance
Cumulativ
e %
Tota
l
% of
Variance
Cumulativ
e %
Total
1 3.70
4 41.155 41.155 3.70
4 41.155 41.155 3.496
2 1.78
6 19.845 61.000 1.78
6 19.845 61.000 1.870
3 1.34
3 14.923 75.923 1.34
3 14.923 75.923 2.143
4 .803 8.919 84.842
5 .433 4.813 89.655

6 .286 3.179 92.834
7 .257 2.855 95.689
8 .226 2.510 98.199
9 .162 1.801 100.000
Extraction Method: Principal Component Analysis.
a. When components are correlated, sums of squared loadings cannot be added to obtain a total
variance.
Component Matrixa
Component
1 2 3
tendency to eat
healthily .267 .795 .378
satisfaction with work .824 -.305 .128
satisfaction with
relationship .685 -.354 .125
how often go on
holiday .790 -.229 .124

amount drinking water
per day (glass) .279 .758 .453
hours exercise per week .527 .421 -.646
quality of sleep .510 .337 -.708
enjoy hobbies .804 -.095 .094
going to church etc .774 -.088 .142
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
Pattern Matrixa
Component
1 2 3
tendency to eat
healthily -.025 .916 -.032
satisfaction with work .897 -.054 .018
satisfaction with
relationship .802 -.117 .073
how often go on
holiday .829 .001 -.007
amount drinking water
per day (glass) .031 .931 .050
hours exercise per week .003 .043 -.924
quality of sleep .008 -.066 -.944
enjoy hobbies .765 .096 -.093
going to church etc .753 .125 -.042
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
a. Rotation converged in 4 iterations.
Structure Matrix
Component
1 2 3
tendency to eat
healthily .089 .920 -.209
satisfaction with work .885 .044 -.236

satisfaction with
relationship .768 -.040 -.140
how often go on
holiday .831 .097 -.252
amount drinking water
per day (glass) .122 .925 -.145
hours exercise per week .280 .228 -.933
quality of sleep .279 .124 -.933
enjoy hobbies .803 .201 -.337
going to church etc .780 .219 -.290
Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization.
Component Correlation Matrix
Component 1 2 3
1 1.000 .114 -.295
2 .114 1.000 -.200
3 -.295 -.200 1.000
Extraction Method: Principal Component
Analysis.
Rotation Method: Oblimin with Kaiser
Normalization.
a)
Form the above tables of outcome it has been determined that the data in well-being.sav
is appropriate to factor analysis (Fritz, Verhoeven and Avenia, 2017). This is because the results
shows the positive significant results to the data set.
b)
In the above calculation it is identified that Oblimin with Kaiser Normalization is used
for rotation as it gives 4 iterations of results to the specific data set . The data underneath define
the total outcome for relevant rotation method:
Component Matrixa
Component
1 2 3
tendency to eat
healthily .267 .795 .378

satisfaction with work .824 -.305 .128
satisfaction with
relationship .685 -.354 .125
how often go on
holiday .790 -.229 .124
amount drinking water
per day (glass) .279 .758 .453
hours exercise per week .527 .421 -.646
quality of sleep .510 .337 -.708
enjoy hobbies .804 -.095 .094
going to church etc .774 -.088 .142
Extraction Method: Principal Component Analysis.
a. 3 components extracted.
c)
In the above table it is determined that factor ratings as an imply over variables
calculating comparable factors is a better idea (Goel, Chadha and Sharma, 2015). Such means
correlate with "real" factor scores almost perfectly, but they do not suffer from the above
problems.
d)
The results shows that all figures are related with factor analysis as all values are
positively coded which means that there are higher value that shows better advantages
sentiments.
Question 2
The SPSS linear mixed-effect designs (MIXED) method allows you to adjust linear
blended-effect designs to information from linear regressions.
Within-Subjects Factors
Measure: MEASURE_1
effect Dependent
Variable
1 Gender
2 school
Between-Subjects Factors
N

mark 14 1
19 1
23 1
24 2
25 3
26 2
27 2
28 1
29 2
30 6
31 4
32 5
33 3
34 5
35 13
36 8
37 6
38 8
39 6
40 10
41 13
42 14
43 11
44 18
45 7
46 12
47 17
48 14
49 14
50 13
51 16
52 15
53 9
54 15
55 22
56 19
57 21
58 17

59 25
60 17
61 23
62 17
63 28
64 20
65 14
66 21
67 20
68 8
69 13
70 12
71 17
72 21
73 14
74 23
75 12
76 17
77 8
78 13
79 8
80 13
81 7
82 11
83 5
84 13
85 2
86 4
87 4
88 4
89 3
90 4
91 3
92 6
93 2
94 2
95 2

Descriptive Statistics
mark Mean Std. Deviation N
Gender 14 2.00 . 1
19 2.00 . 1
23 2.00 . 1
24 1.00 .000 2
25 1.67 .577 3
26 1.50 .707 2
27 2.00 .000 2
28 1.00 . 1
29 2.00 .000 2
30 1.83 .408 6
31 1.75 .500 4
32 1.40 .548 5
33 1.67 .577 3
34 1.40 .548 5
35 1.85 .376 13
36 1.50 .535 8
37 1.83 .408 6
38 1.75 .463 8
39 1.33 .516 6
40 1.50 .527 10
41 1.54 .519 13
42 1.57 .514 14
43 1.45 .522 11
44 1.39 .502 18
45 1.43 .535 7
46 1.33 .492 12
47 1.24 .437 17
48 1.50 .519 14
49 1.29 .469 14
50 1.31 .480 13
51 1.50 .516 16
52 1.27 .458 15
53 1.11 .333 9

54 1.27 .458 15
55 1.45 .510 22
56 1.42 .507 19
57 1.29 .463 21
58 1.35 .493 17
59 1.32 .476 25
60 1.35 .493 17
61 1.35 .487 23
62 1.47 .514 17
63 1.50 .509 28
64 1.65 .489 20
65 1.14 .363 14
66 1.38 .498 21
67 1.65 .489 20
68 1.25 .463 8
69 1.54 .519 13
70 1.75 .452 12
71 1.41 .507 17
72 1.67 .483 21
73 1.57 .514 14
74 1.48 .511 23
75 1.42 .515 12
76 1.47 .514 17
77 1.88 .354 8
78 1.62 .506 13
79 1.63 .518 8
80 1.62 .506 13
81 1.71 .488 7
82 1.64 .505 11
83 1.60 .548 5
84 1.46 .519 13
85 2.00 .000 2
86 2.00 .000 4
87 2.00 .000 4
88 2.00 .000 4
89 2.00 .000 3
90 1.75 .500 4
91 2.00 .000 3

92 1.83 .408 6
93 2.00 .000 2
94 2.00 .000 2
95 2.00 .000 2
Total 1.49 .500 792
school 14 3.00 . 1
19 3.00 . 1
23 2.00 . 1
24 3.00 .000 2
25 3.00 .000 3
26 3.00 .000 2
27 3.00 .000 2
28 3.00 . 1
29 2.50 .707 2
30 2.83 .408 6
31 2.75 .500 4
32 3.00 .000 5
33 2.67 .577 3
34 2.80 .447 5
35 2.85 .376 13
36 3.00 .000 8
37 3.00 .000 6
38 2.75 .463 8
39 3.00 .000 6
40 2.90 .316 10
41 2.46 .877 13
42 2.36 .745 14
43 2.27 1.009 11
44 2.61 .778 18
45 2.43 .787 7
46 2.58 .669 12
47 2.53 .800 17
48 2.71 .611 14
49 2.00 1.038 14
50 2.00 1.000 13
51 2.44 .892 16
52 2.27 .961 15
53 2.11 .928 9

54 2.07 .961 15
55 1.77 .973 22
56 1.84 .958 19
57 1.62 .921 21
58 1.41 .795 17
59 1.60 .913 25
60 1.24 .664 17
61 1.48 .790 23
62 1.29 .686 17
63 1.50 .882 28
64 1.30 .657 20
65 1.36 .745 14
66 1.10 .436 21
67 1.00 .000 20
68 1.63 .916 8
69 1.31 .751 13
70 1.17 .577 12
71 1.24 .664 17
72 1.05 .218 21
73 1.00 .000 14
74 1.22 .600 23
75 1.17 .577 12
76 1.12 .485 17
77 1.00 .000 8
78 1.00 .000 13
79 1.00 .000 8
80 1.00 .000 13
81 1.00 .000 7
82 1.00 .000 11
83 1.00 .000 5
84 1.00 .000 13
85 1.00 .000 2
86 1.00 .000 4
87 1.00 .000 4
88 1.00 .000 4
89 1.00 .000 3
90 1.00 .000 4
91 1.00 .000 3

92 1.00 .000 6
93 1.00 .000 2
94 1.00 .000 2
95 1.00 .000 2
Total 1.71 .924 792
Box's Test of Equality of
Covariance Matricesa
Box's M 146.184
F 1.120
df1 120
df2 19182.182
Sig. .175
Tests the null hypothesis
that the observed
covariance matrices of the
dependent variables are
equal across groups.
a. Design: Intercept + mark
Within Subjects Design:
effect
Multivariate Testsa
Effect Value F Hypothesis
df
Error df Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powerc
effect
Pillai's Trace .042 31.160b 1.000 717.000 .000 .042 31.160 1.000
Wilks' Lambda .958 31.160b 1.000 717.000 .000 .042 31.160 1.000
Hotelling's
Trace .043 31.160b 1.000 717.000 .000 .042 31.160 1.000
Roy's Largest
Root .043 31.160b 1.000 717.000 .000 .042 31.160 1.000
effect *
mark
Pillai's Trace .419 7.001b 74.000 717.000 .000 .419 518.065 1.000
Wilks' Lambda .581 7.001b 74.000 717.000 .000 .419 518.065 1.000
Hotelling's
Trace
.723 7.001b 74.000 717.000 .000 .419 518.065 1.000

Roy's Largest
Root .723 7.001b 74.000 717.000 .000 .419 518.065 1.000
a. Design: Intercept + mark
Within Subjects Design: effect
b. Exact statistic
c. Computed using alpha = .05
Mauchly's Test of Sphericitya
Measure: MEASURE_1
Within Subjects
Effect
Mauchly's W Approx. Chi-
Square
df Sig. Epsilonb
Greenhouse-Geisser Huynh-Feldt Lower-bound
effect 1.000 .000 0 . 1.000 1.000 1.000
Tests the null hypothesis that the error covariance matrix of the orthonormalized transformed dependent variables is
proportional to an identity matrix.
a. Design: Intercept + mark
Within Subjects Design: effect
b. May be used to adjust the degrees of freedom for the averaged tests of significance. Corrected tests are displayed in the Tests
of Within-Subjects Effects table.
Tests of Within-Subjects Effects
Measure: MEASURE_1
Source Type III
Sum of
Squares
df Mean
Square
F Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powera
effect
Sphericity
Assumed 12.338 1 12.338 31.160 .000 .042 31.160 1.000
Greenhouse-
Geisser 12.338 1.000 12.338 31.160 .000 .042 31.160 1.000
Huynh-Feldt 12.338 1.000 12.338 31.160 .000 .042 31.160 1.000
Lower-bound 12.338 1.000 12.338 31.160 .000 .042 31.160 1.000
effect * mark Sphericity
Assumed 205.135 74 2.772 7.001 .000 .419 518.065 1.000
Greenhouse-
Geisser 205.135 74.000 2.772 7.001 .000 .419 518.065 1.000
Huynh-Feldt 205.135 74.000 2.772 7.001 .000 .419 518.065 1.000

Lower-bound 205.135 74.000 2.772 7.001 .000 .419 518.065 1.000
Error(effect)
Sphericity
Assumed 283.905 717 .396
Greenhouse-
Geisser 283.905 717.000 .396
Huynh-Feldt 283.905 717.000 .396
Lower-bound 283.905 717.000 .396
a. Computed using alpha = .05
Tests of Within-Subjects Contrasts
Measure: MEASURE_1
Source effect Type III Sum of
Squares
df Mean
Square
F Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powera
effect Linear 12.338 1 12.338 31.160 .000 .042 31.160 1.000
effect * mark Linear 205.135 74 2.772 7.001 .000 .419 518.065 1.000
Error(effect) Linear 283.905 717 .396
a. Computed using alpha = .05
Levene's Test of Equality of Error Variancesa
F df1 df2 Sig.
Gender 6.617 74 717 .000
school 9.805 74 717 .000
Tests the null hypothesis that the error variance of the dependent
variable is equal across groups.
a. Design: Intercept + mark
Within Subjects Design: effect
Tests of Between-Subjects Effects
Measure: MEASURE_1
Transformed Variable: Average
Source Type III Sum of
Squares
df Mean Square F Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powera
Intercept 2144.336 1 2144.336 7178.819 .000 .909 7178.819 1.000
mark 169.966 74 2.297 7.689 .000 .442 569.011 1.000

Error 214.170 717 .299
a. Computed using alpha = .05
Estimates
Measure: MEASURE_1
effect Mean Std. Error 95% Confidence Interval
Lower Bound Upper Bound
1 1.584 .025 1.534 1.633
2 1.844 .036 1.774 1.913
Pairwise Comparisons
Measure: MEASURE_1
(I) effect (J) effect Mean Difference
(I-J)
Std. Error Sig.b 95% Confidence Interval for
Differenceb
Lower Bound Upper Bound
1 2 -.260* .047 .000 -.351 -.169
2 1 .260* .047 .000 .169 .351
Based on estimated marginal means
*. The mean difference is significant at the .05 level.
b. Adjustment for multiple comparisons: Bonferroni.
Multivariate Tests
Value F Hypothesis
df
Error df Sig. Partial Eta
Squared
Noncent.
Parameter
Observed
Powerb
Pillai's trace .042 31.160a 1.000 717.000 .000 .042 31.160 1.000
Wilks' lambda .958 31.160a 1.000 717.000 .000 .042 31.160 1.000
Hotelling's trace .043 31.160a 1.000 717.000 .000 .042 31.160 1.000
Roy's largest
root .043 31.160a 1.000 717.000 .000 .042 31.160 1.000
Each F tests the multivariate effect of effect. These tests are based on the linearly independent pairwise comparisons
among the estimated marginal means.
a. Exact statistic
b. Computed using alpha = .05

From the above outcome it is determined that yes, different types of school have a
influencing factors on gender because the significance of correlation is higher than 0.5 for both
the lower and upper bands.
Question 3
Mediation Analysis: A mediation model is intended to define and clarify in statistics a
framework or procedure that characterises an interaction found between independent factor and
the dependents factors by adding a hypothesis third (or intermediate) variable identified as the
mediator (or intermediate variable) (Guess and Ma, 2015).
Descriptive Statistics
Mean Std. Deviation N
EMVuln 46.7500 15.45824 100
Avoidance of threat 5.0640 2.28185 100
Correlations
EMVuln Avoidance of
threat
Pearson Correlation EMVuln 1.000 -.430
Avoidance of threat -.430 1.000
Sig. (1-tailed) EMVuln . .000
Avoidance of threat .000 .
N EMVuln 100 100
Avoidance of threat 100 100
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 Avoidance of
threatb . Enter
a. Dependent Variable: EMVuln
b. All requested variables entered.

Model Summaryb
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
Durbin-Watson
1 .430a .185 .176 14.02926 1.040
a. Predictors: (Constant), Avoidance of threat
b. Dependent Variable: EMVuln
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 4368.387 1 4368.387 22.195 .000b
Residual 19288.363 98 196.820
Total 23656.750 99
a. Dependent Variable: EMVuln
b. Predictors: (Constant), Avoidance of threat
Coefficientsa
Model Unstandardized
Coefficients
Standardized
Coefficients
t Sig. Correlations Collinearity
Statistics
B Std. Error Beta Zero-
order
Partial Part Tolerance VIF
1
(Constant) 61.492 3.429 17.932 .000
Avoidance of
threat -2.911 .618 -.430 -4.711 .000 -.430 -.430 -.430 1.000 1.000
a. Dependent Variable: EMVuln
Collinearity Diagnosticsa
Model Dimension Eigenvalue Condition Index Variance Proportions
(Constant) Avoidance of
threat
1 1 1.912 1.000 .04 .04
2 .088 4.675 .96 .96
a. Dependent Variable: EMVuln

Residuals Statisticsa
Minimum Maximum Mean Std. Deviation N
Predicted Value 33.9528 60.1527 46.7500 6.64267 100
Residual -29.88517 31.75842 .00000 13.95822 100
Std. Predicted Value -1.927 2.018 .000 1.000 100
Std. Residual -2.130 2.264 .000 .995 100
a. Dependent Variable: EMVuln
The above results, define that there is a positive connection between the avoidance of
threat and motivational vulnerability. It is clear that Std. Dev between the variable is 13.95822
that define the positive results.
Question 4
Simple Slop Analysis: When the relationship is found substantially, the essence of the
connection should be explored (Hepworth, 2015). The shape an interaction assumes is really not
feasible without at least showing the outcomes. Of example, in the estimation of elementary
school violence, if a major relationship among family income (X) and self-esteem (Z) occurs,
higher-income children may have a better chance of violent behaviours.
Variables Entered/Removeda
Model Variables Entered Variables
Removed
Method
1 severityb . Enter
a. Dependent Variable: Events
b. All requested variables entered.
Model Summary
Model R R Square Adjusted R
Square
Std. Error of the
Estimate
1 .309a .096 .080 1.23019
a. Predictors: (Constant), severity

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 9.286 1 9.286 6.136 .016b
Residual 87.775 58 1.513
Total 97.061 59
a. Dependent Variable: Events
b. Predictors: (Constant), severity
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1 (Constant) 2.298 .361 6.364 .000
severity .075 .030 .309 2.477 .016
a. Dependent Variable: Events
The above tables clearly states that there is a direct impact of depressed mother on their
children at the time of birth. It also shows that there is a no moderating effect on predicting
Severity because of Risk factors. This is due to positive relation between the different variables
of the data set.
Question 5
Meta Analysis: This is a quantitative, businesslike and biological plausibility layout for
the systematic evaluation of previous studies in order to draw findings on the above study bodies
(Morden, 2016). The findings of a meta-analysis could include more previsions than any
individual study leading to the aggregate analysis, when assessing the impact of medication, or
risk factor for diseases and other outcomes. The study outcomes are also objectively tested with
the uncertainty or heterogeneity.
a)
Case Processing Summary
Cases
Valid Missing Total
N Percent N Percent N Percent
training * reduction 7 100.0% 0 0.0% 7 100.0%

training * reduction Crosstabulation
Count
reduction Total
-2.93 13.44 13.78 14.74 15.62 15.96 18.32
training
1.90 1 0 0 0 0 0 0 1
2.10 0 0 1 0 0 0 0 1
3.10 0 0 0 1 0 0 0 1
3.30 0 1 0 0 0 0 0 1
4.30 0 0 0 0 1 0 0 1
5.50 0 0 0 0 0 1 0 1
7.80 0 0 0 0 0 0 1 1
Total 1 1 1 1 1 1 1 7
Chi-Square Tests
Value df Asymp. Sig. (2-
sided)
Pearson Chi-Square 42.000a 36 .227
Likelihood Ratio 27.243 36 .853
Linear-by-Linear Association 2.357 1 .125
N of Valid Cases 7
a. 49 cells (100.0%) have expected count less than 5. The minimum expected
count is .14.
b)
Group Statistics
reduction N Mean Std. Deviation Std. Error Mean
training 1.00 0a . . .
2.00 0a . . .
a. t cannot be computed because at least one of the groups is empty.
c)

REFERENCES
Books and Journals:
Alsemgeest, L., 2015. Arguments for and against financial literacy education: where to go from
here?. International Journal of Consumer Studies. 39(2). pp.155-161.
Arianti, B. F., 2018. THE INFLUENCE OF FINANCIAL LITERACY, FINANCIAL
BEHAVIOR AND INCOME ON INVESTMENT DECISION. EAJ (ECONOMICS
AND ACCOUNTING JOURNAL). 1(1). pp.1-10.
Fritz, V., Verhoeven, M. and Avenia, A., 2017. Political Economy of Public Financial
Management Reforms.
Goel, U., Chadha, S. and Sharma, A. K., 2015. Operating liquidity and financial leverage:
Evidences from Indian machinery industry. Procedia-Social and Behavioral Sciences.
189. pp.344-350.
Guess, G. M. and Ma, J., 2015. The risks of Chinese subnational debt for public financial
management. Public Administration and Development. 35(2). pp.128-139.
Hepworth, N., 2015. Debate: Implementing advanced public financial management reform in
developing countries. Public Money & Management. 35(4). pp.251-253.
Morden, T., 2016. Principles of strategic management. Routledge.
Osadchy, E. A. and Akhmetshin, E. M., 2015. Development of the financial control system in the
company in crisis. Mediterranean Journal of Social Sciences. 6(5). p.390.
Prawitz, A. D. and Cohart, J., 2016. Financial management competency, financial resources,
locus of control, and financial wellness. Journal of Financial Counseling and Planning.
27(2). pp.142-157.