Literature Review
The biopsychosocial perspective was proposed by the
physician George Engle in 1988 as an alternative to the traditional biomedical
model for the analysis of health behavior. Engle argued that the traditional
biomedical model was too reductionist, and was not capable of accounting for
all the variables that influence health
Developed by the social psychologist Albert Bandura,
selfefficacy is a psychological concept defined as a personal judgement about
the capability of the self to innately achieve set goals (Bandura, 1977). Selfefficacy was first
approached in 1977 in Bandura’s journal article “selfefficacy” from a
psychosocial perspective. Bandura argued that selfefficacy is attained mainly
through three things: (1) what we attempt to perform, (2) what we observe
others doing, (3) how we are persuaded
Knowledge Gap
Since selfefficacy is being analyzed through the
biopsychosocial model, it is a good fit to measure the relationship of
different biological, psychological, and social variables with selfefficacy.
In order to approach the knowledge gap, the predictor variables of confidence,
usefulness, male dominance, and teacher attitude and perception will be
compared statistically with selfefficacy in order to explore correlational
outcomes.
Statistical Requirements
Factorial analysis of variance (ANOVA)
which compares means of two or more independent variables is usually the
utilized statistical tool in experimental design. However, it can also be used
in correlational research
Regression
analysis is another statistical tool that enables the researcher to explore the
relationships among multiple variables, where they examine the influence of the
predictor variables on the outcome variable. There are several types of
regression analyses, the most basic form is simple regression, it’s when
there’s one predictive variable and it’s called a simple regression, and
represented by a correlation coefficient denoted by r^{2}
Hypotheses
Gender and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between gender and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between gender and selfefficacy
Math Grades and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between math grades and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between math grades and selfefficacy
Science Grades and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between science grades and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between science grades and selfefficacy
Confidence and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between confidence and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between confidence and selfefficacy
Usefulness and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between usefulness and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between usefulness and selfefficacy
Male Dominated Field and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between male dominated field and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between male dominated field and selfefficacy
Tutor Attitude and Perception and SelfEfficacy
H0 μ1 = μ2: There is no
significant relationship between tutor attitude and perception and selfefficacy
H1 μ1 ≠ μ2: There is a
significant relationship between tutor attitude and perception and
selfefficacy
Simple Regression Analyses
Age Predictor Variable
Variables
Entered/Removed^{a} 

Model 
Variables
Entered 
Variables
Removed 
Method 
1 
Age^{b} 
. 
Enter 
a. Dependent Variable: Self Efficacy 

b. All requested variables entered. 
Model Summary^{b} 

Model 
R 
R Square 
Adjusted
R Square 
Std.
Error of the Estimate 
1 
.115^{a} 
.013 
.003 
32.00833 
a. Predictors: (Constant), Age 

b. Dependent Variable: Self Efficacy 
A simple linear
regression was conducted to measure the effect of age on selfefficacy.
R^{ }=
0.115 indicates weak predictive quality of age on selfefficacy
R^{2} =
0.013 age explains 1.3% of the variability of selfefficacy
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
1341.447 
1 
1341.447 
1.309 
.255^{b} 
Residual 
100404.263 
98 
1024.533 



Total 
101745.710 
99 




a. Dependent Variable: Self Efficacy 

b. Predictors: (Constant), Age 
F(98) = 1.309 ; P = 0.255 > P =
0.05
The results are not statistically
significant.
Coefficients^{a} 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
63.716 
46.877 

1.359 
.177 


Age 
2.652 
2.317 
.115 
1.144 
.255 
1.000 
1.000 

a. Dependent Variable: Self Efficacy 
B = 2.652
indicates that for every increase of one year in age, selfefficacy increases
by 2.65 points
β = .115
t (98) = 1.114 ; P
= .225 > P=0.05
There
is some difference in mean scores, however the results are not statistically
significant, and thus the null hypothesis cannot be rejected.
Collinearity Diagnostics^{a} 

Model 
Dimension 
Eigenvalue 
Condition
Index 
Variance
Proportions 

(Constant) 
Age 

1 
1 
1.998 
1.000 
.00 
.00 
2 
.002 
29.256 
1.00 
1.00 

a. Dependent Variable: Self Efficacy 
Collinearity
is measure to account for the variance in the predictor variable that might
affect the significance of the findings
Residuals Statistics^{a} 


Minimum 
Maximum 
Mean 
Std.
Deviation 
N 
Predicted Value 
108.7972 
127.3599 
117.2300 
3.68103 
100 
Residual 
73.40449 
61.55095 
.00000 
31.84626 
100 
Std. Predicted Value 
2.291 
2.752 
.000 
1.000 
100 
Std. Residual 
2.293 
1.923 
.000 
.995 
100 
a. Dependent Variable: Self Efficacy 
The
residual plots are randomly dispersed along the X axis, and thus indicating
that regression analysis is a good model fit for analyzing the data
Math Grades Predictor Variable
Variables
Entered/Removed^{a} 

Model 
Variables
Entered 
Variables
Removed 
Method 
1 
Math Grade^{b} 
. 
Enter 
a. Dependent Variable: Self Efficacy 

b. All requested variables entered. 
Model Summary^{b} 

Model 
R 
R Square 
Adjusted
R Square 
Std.
Error of the Estimate 
1 
.056^{a} 
.003 
.007 
32.17069 
a. Predictors: (Constant), Math Grade 

b. Dependent Variable: Self Efficacy 
A simple linear
regression was conducted to measure the effect of math grades on selfefficacy.
R^{ }=
0.056 indicates weak predictive quality of math grades on selfefficacy
R^{2} =
0.003 math grades explains 0.3% of the variability of selfefficacy
ANOVA^{a} 

Model 
Sum of
Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
320.311 
1 
320.311 
.309 
.579^{b} 
Residual 
101425.399 
98 
1034.953 



Total 
101745.710 
99 




a. Dependent Variable: Self Efficacy 

b. Predictors: (Constant), Math Grade 
F (98) = 0.309 ; P = 0.579 > P =
0.05
The results are not statistically
significant.
Coefficients^{a} 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
113.347 
7.686 

14.747 
.000 


Math Grade 
1.639 
2.945 
.056 
.556 
.579 
1.000 
1.000 

a. Dependent Variable: Self Efficacy 
B = 1.63 this
indicates that for every increase of one math grade, selfefficacy increases by
1.639 points
β = .056
t (98) = .056 ; P
= .556 > P=0.05
There
is negligible difference in mean scores, however the results are not
statistically significant, and thus the null hypothesis cannot be rejected.
Collinearity Diagnostics^{a} 

Model 
Dimension 
Eigenvalue 
Condition
Index 
Variance
Proportions 

(Constant) 
Math
Grade 

1 
1 
1.908 
1.000 
.05 
.05 
2 
.092 
4.559 
.95 
.95 

a. Dependent Variable: Self Efficacy 
Residuals Statistics^{a} 


Minimum 
Maximum 
Mean 
Std.
Deviation 
N 
Predicted Value 
113.3467 
119.9008 
117.2300 
1.79874 
100 
Residual 
70.62376 
60.37625 
.00000 
32.00780 
100 
Std. Predicted Value 
2.159 
1.485 
.000 
1.000 
100 
Std. Residual 
2.195 
1.877 
.000 
.995 
100 
a. Dependent Variable: Self Efficacy 
The
residual plots are randomly dispersed along the X axis, and thus indicating
that regression analysis is a good model fit for analyzing the data.
Science Grades Predictor Variable
Variables
Entered/Removed^{a} 

Model 
Variables
Entered 
Variables
Removed 
Method 
1 
Science Grade^{b} 
. 
Enter 
a. Dependent Variable: Self Efficacy 

b. All requested variables entered. 
Model Summary^{b} 

Model 
R 
R Square 
Adjusted
R Square 
Std.
Error of the Estimate 
1 
.035^{a} 
.001 
.009 
32.20187 
a. Predictors: (Constant), Science Grade 

b. Dependent Variable: Self Efficacy 
A simple linear
regression was conducted to measure the effect of science grades on
selfefficacy.
R^{ }=
0.035 indicates weak predictive quality of science grades on selfefficacy
R^{2} =
0.001 science grades explains 0.1% of the variability of selfefficacy
ANOVA^{a} 

Model 
Sum of
Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
123.608 
1 
123.608 
.119 
.731^{b} 
Residual 
101622.102 
98 
1036.960 



Total 
101745.710 
99 




a. Dependent Variable: Self Efficacy 

b. Predictors: (Constant), Science Grade 
F (98) = 0.119 ; P = 0.731 > P =
0.05
The results are not statistically
significant.
Coefficients^{a} 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
119.839 
8.214 

14.590 
.000 


Science Grade 
1.078 
3.122 
.035 
.345 
.731 
1.000 
1.000 

a. Dependent Variable: Self Efficacy 
B = 1.078 this
indicates that for every increase of one science grade, selfefficacy decreases
by 1.078 points
β = .035
t (98) = .345 ; P
= .731 > P=0.05
There
is negligible difference in mean scores, however the results are not
statistically significant, and thus the null hypothesis cannot be rejected.
Collinearity Diagnostics^{a} 

Model 
Dimension 
Eigenvalue 
Condition
Index 
Variance
Proportions 

(Constant) 
Science
Grade 

1 
1 
1.920 
1.000 
.04 
.04 
2 
.080 
4.897 
.96 
.96 

a. Dependent Variable: Self Efficacy 
Residuals Statistics^{a} 


Minimum 
Maximum 
Mean 
Std.
Deviation 
N 
Predicted Value 
115.5267 
119.8389 
117.2300 
1.11739 
100 
Residual 
71.68278 
60.39526 
.00000 
32.03882 
100 
Std. Predicted Value 
1.524 
2.335 
.000 
1.000 
100 
Std. Residual 
2.226 
1.876 
.000 
.995 
100 
a. Dependent Variable: Self Efficacy 
The
residual plots are randomly dispersed along the X axis, and thus indicating
that regression analysis is a good model fit for analyzing the data.
Multiple Regression Analyses
Regression
[DataSet1] C:\Users\Roy
Riachi\Desktop\Research\University of Liverpool\03  Data Analysis For
Psychology\Week 8\Trial 1 .sav
Variables
Entered/Removed^{a} 

Model 
Variables
Entered 
Variables
Removed 
Method 
1 
Tutor Attitudes, Male Dominated Field, Usefulness, Confidence^{b} 
. 
Enter 
a. Dependent Variable: Self Efficacy 

b. All requested variables entered. 
Model Summary^{b} 

Model 
R 
R Square 
Adjusted
R Square 
Std.
Error of the Estimate 
1 
.567^{a} 
.322 
.293 
26.94722 
a. Predictors: (Constant), Tutor Attitudes, Male Dominated
Field, Usefulness, Confidence 

b. Dependent Variable: Self Efficacy 
A
multiple linear regression was conducted to measure the effect of confidence,
usefulness, male dominance, teacher attitude and perception on selfefficacy.
R^{ }=
0.567 indicates strong predictive quality of math grades on selfefficacy
R^{2} =
0.322 confidence, usefulness, male dominance, teacher attitude and perception
explains 32.2% of the variability of selfefficacy. The low value of R^{2}
does not indicate a goodness of fit for the model.
ANOVA^{a} 

Model 
Sum of
Squares 
df 
Mean
Square 
F 
Sig. 

1 
Regression 
32761.211 
4 
8190.303 
11.279 
.000^{b} 
Residual 
68984.499 
95 
726.153 



Total 
101745.710 
99 




a. Dependent Variable: Self Efficacy 

b. Predictors: (Constant), Tutor Attitudes, Male Dominated
Field, Usefulness, Confidence 
F (95) = 11.279; P = 0.000 < P =
0.05
The results are statistically
significant.
Coefficients^{a} 

Model 
Unstandardized
Coefficients 
Standardized
Coefficients 
t 
Sig. 
Collinearity
Statistics 

B 
Std.
Error 
Beta 
Tolerance 
VIF 

1 
(Constant) 
219.800 
40.157 

5.474 
.000 


Confidence 
1.132 
.469 
.346 
2.411 
.018 
.347 
2.882 

Usefulness 
.448 
.368 
.142 
1.217 
.227 
.525 
1.906 

Male Dominated Field 
.300 
.482 
.056 
.622 
.536 
.869 
1.150 

Tutor Attitudes 
.631 
.610 
.124 
1.035 
.304 
.494 
2.022 

a. Dependent Variable: Self Efficacy 
Confidence
B = 1.132 this
indicates that for every increase of one point in confidence score,
selfefficacy decreases by 1.132 points
β = .346
t (98) = 2.411 ;
P = .018 < P=0.05
There
is a difference in mean scores, and the results are statistically significant,
and thus the alternative hypothesis can be accepted.
Usefulness
B = .448 this
indicates that for every increase of one point in usefulness score,
selfefficacy decreases by .448 points
β = .142
t (98) = 1.217 ;
P = .227 > P=0.05
There
is some difference in mean scores, however the results are not statistically
significant, and thus the null hypothesis cannot be rejected.
Male
Dominated Field
B = .300 this
indicates that for every increase of one point in male dominated field,
selfefficacy decreases by 0.3 points
β = .056
t (98) = .662 ; P
= .536 > P=0.05
There
is minor difference in mean scores, however the results are not statistically
significant, and thus the null hypothesis cannot be rejected.
Tutor
Attitudes
B = .631 this
indicates that for every increase of one point in tutor attitude, selfefficacy
decreases by .631 points
β = .124
t (98) = 1.035 ;
P = .304 > P=0.05
There is some difference in mean scores, however the results are not statistically significant, and thus the null hypothesis cannot be rejected.
Collinearity Diagnostics^{a} 

Model 
Dimension 
Eigenvalue 
Condition
Index 
Variance
Proportions 

(Constant) 
Confidence 
Usefulness 
Male
Dominated Field 
Tutor
Attitudes 

1 
1 
4.835 
1.000 
.00 
.00 
.00 
.00 
.00 
2 
.086 
7.518 
.01 
.05 
.34 
.10 
.00 

3 
.048 
10.088 
.01 
.03 
.05 
.80 
.01 

4 
.030 
12.637 
.01 
.58 
.61 
.06 
.00 

5 
.002 
50.133 
.97 
.33 
.00 
.04 
.99 

a. Dependent Variable: Self Efficacy 
Collinearity
is measure to account for the variance in the predictor variable that might
affect the significance of the findings
Residuals Statistics^{a} 


Minimum 
Maximum 
Mean 
Std.
Deviation 
N 
Predicted Value 
66.6626 
147.4701 
117.2300 
18.19124 
100 
Residual 
75.09506 
58.58260 
.00000 
26.39722 
100 
Std. Predicted Value 
2.780 
1.662 
.000 
1.000 
100 
Std. Residual 
2.787 
2.174 
.000 
.980 
100 
a. Dependent Variable: Self Efficacy 
Charts
The regression
standardized residual is normally distributed in terms of frequency.
The residual plots
are randomly dispersed along the X axis, and thus indicating that regression
analysis is a good model fit for analyzing the data.
Discussion
After performing simple
regression analysis for the categorical predictor variables, which are age,
math scores, and science scores; we also performed a multiple regression
analysis for continuous predictor variables, which are confidence, usefulness,
male dominated field, and teacher attitude and perception. We were able to
establish a statistically significant negative correlation between confidence
and selfefficacy. However, the rest of the predictor variables were not able
to establish a correlational relationship with selfefficacy, since all of
their P values were greater than 0.05, hence lacking in statistical
significance
This negative correlation
between confidence and selfefficacy is in accordance with the literature on
selfesteem. Selfesteem and confidence are positively correlated, since both
are defined by feelings of selfworth
References
Bandura, A. (1977). Selfefficacy:
toward a unifying theory of behavioral change. Psychological Review, 191215.
Bandura, A. (1977). Social learning theory.
New Jersey: Englewood Cliffs .
Born, K., Wolvaardt, L., & Mcintosh, E.
(2015). Risky sexual behaviour of university students: Perceptions and the
effect of a sex education tool. African Journal For Physical, Health
Education, Recreation & Dance, 502518.
Brummelman, E., Thomaes, S., Nelemans, S., &
Castro, B. O. (2017). When parents praise inflates, childrens' selfesteem
deflates. Child Development, 17991809.
Dahiru, T. (2008). P – value, a true test of
statistical significance? A cautionary note. Ann Ib Postgraduate Med,
2126.
Engle, G. L. (1981). The clinical application of
the biopsychosocial model. Journal of Medicine and Philosophy, 101124.
Gallo, A. (2015, November 4). A refresher on
regression analysis . Retrieved from Harvard Business Review :
https://hbr.org/2015/11/arefresheronregressionanalysis
Laureate online education. (2016, Nevember 10).
Week 7: the biopsychosocial perspective. systems, holism and reductionism. Mind,
Brain and Behavior. Netherlands: Laureate Online Education B.V.
Lund. (2018). Multiple regression analysis
using spss statistics. Retrieved from Lund Research Ltd:
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Minitab. (2017). What are type I and type II
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Saslow, E. (2018, July 11). Collinearity  what
it means, why its bad, and how does it affect other models? Retrieved from
Medium :
https://medium.com/@Saslow/collinearitywhatitmeanswhyitsbadandhowdoesitaffectothermodels94e1db984168
SPSS T. (2019). ANOVA  simple introduction.
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https://www.spsstutorials.com/anovawhatisit/
ST. (2019). Risidual analysis in regression .
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Twenge, J., & Campbell, K. (2017). Motivation
. In Personality Psychology (pp. 179209). New York : Pearson .
Yi, T., Ji, J., & Yu, F. (2018). The effect of
metacognitive knowledge on mathematics performance in selfregulated learning
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https://doiorg.liverpool.idm.oclc.org/10.3389/fpsyg.2018.02518.
Appendix
Age Regression Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT SE
/METHOD=ENTER AGE
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).
Math Grades Regression Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT SE
/METHOD=ENTER MATH
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).
Science Grades Syntax
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT SE
/METHOD=ENTER SCIENCE
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).
Multiple Regression Syntax
GET
FILE='C:\Users\Roy Riachi\Desktop\Research\University of Liverpool\03 
Data Analysis For Psychology\Week 8\UKL1_LPSY_303_Week08_statsconfidence.sav'.
Warning # 5281. Command name: GET FILE
SPSS Statistics is running in
Unicode encoding mode. This file is
encoded in
a localespecific (code page)
encoding. The defined width of any
string
variables are automatically
tripled in order to avoid possible data loss.
You
can use ALTER TYPE to set the
width of string variables to the width of the
longest observed value for each string
variable.
DATASET NAME DataSet1
WINDOW=FRONT.
SAVE OUTFILE='C:\Users\Roy
Riachi\Desktop\Research\University of Liverpool\03  Data Analysis '+
'For Psychology\Week 8\Trial 1 .sav'
/COMPRESSED.
REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA COLLIN TOL
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT SE
/METHOD=ENTER Confidence Usefulness Male.domain
Teacher.attitude.perceptions
/PARTIALPLOT ALL
/SCATTERPLOT=(*ZRESID ,*ZPRED)
/RESIDUALS HISTOGRAM(ZRESID) NORMPROB(ZRESID).