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,
self-efficacy is a psychological concept defined as a personal judgement about
the capability of the self to innately achieve set goals (Bandura, 1977). Self-efficacy was first
approached in 1977 in Bandura’s journal article “self-efficacy” from a
psycho-social perspective. Bandura argued that self-efficacy 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 self-efficacy is being analyzed through the
biopsychosocial model, it is a good fit to measure the relationship of
different biological, psychological, and social variables with self-efficacy.
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 self-efficacy 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 r2
Hypotheses
Gender and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between gender and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between gender and self-efficacy
Math Grades and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between math grades and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between math grades and self-efficacy
Science Grades and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between science grades and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between science grades and self-efficacy
Confidence and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between confidence and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between confidence and self-efficacy
Usefulness and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between usefulness and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between usefulness and self-efficacy
Male Dominated Field and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between male dominated field and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between male dominated field and self-efficacy
Tutor Attitude and Perception and Self-Efficacy
H0 μ1 = μ2: There is no
significant relationship between tutor attitude and perception and self-efficacy
H1 μ1 ≠ μ2: There is a
significant relationship between tutor attitude and perception and
self-efficacy
Simple Regression Analyses
Age Predictor Variable
Variables
Entered/Removeda |
|||
Model |
Variables
Entered |
Variables
Removed |
Method |
1 |
Ageb |
. |
Enter |
a. Dependent Variable: Self Efficacy |
|||
b. All requested variables entered. |
Model Summaryb |
||||
Model |
R |
R Square |
Adjusted
R Square |
Std.
Error of the Estimate |
1 |
.115a |
.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 self-efficacy.
R =
0.115 indicates weak predictive quality of age on self-efficacy
R2 =
0.013 age explains 1.3% of the variability of self-efficacy
ANOVAa |
||||||
Model |
Sum of Squares |
df |
Mean
Square |
F |
Sig. |
|
1 |
Regression |
1341.447 |
1 |
1341.447 |
1.309 |
.255b |
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.
Coefficientsa |
||||||||
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, self-efficacy 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 Diagnosticsa |
|||||
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 Statisticsa |
|||||
|
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/Removeda |
|||
Model |
Variables
Entered |
Variables
Removed |
Method |
1 |
Math Gradeb |
. |
Enter |
a. Dependent Variable: Self Efficacy |
|||
b. All requested variables entered. |
Model Summaryb |
||||
Model |
R |
R Square |
Adjusted
R Square |
Std.
Error of the Estimate |
1 |
.056a |
.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 self-efficacy.
R =
0.056 indicates weak predictive quality of math grades on self-efficacy
R2 =
0.003 math grades explains 0.3% of the variability of self-efficacy
ANOVAa |
||||||
Model |
Sum of
Squares |
df |
Mean
Square |
F |
Sig. |
|
1 |
Regression |
320.311 |
1 |
320.311 |
.309 |
.579b |
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.
Coefficientsa |
||||||||
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, self-efficacy 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 Diagnosticsa |
|||||
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 Statisticsa |
|||||
|
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/Removeda |
|||
Model |
Variables
Entered |
Variables
Removed |
Method |
1 |
Science Gradeb |
. |
Enter |
a. Dependent Variable: Self Efficacy |
|||
b. All requested variables entered. |
Model Summaryb |
||||
Model |
R |
R Square |
Adjusted
R Square |
Std.
Error of the Estimate |
1 |
.035a |
.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
self-efficacy.
R =
0.035 indicates weak predictive quality of science grades on self-efficacy
R2 =
0.001 science grades explains 0.1% of the variability of self-efficacy
ANOVAa |
||||||
Model |
Sum of
Squares |
df |
Mean
Square |
F |
Sig. |
|
1 |
Regression |
123.608 |
1 |
123.608 |
.119 |
.731b |
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.
Coefficientsa |
||||||||
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, self-efficacy 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 Diagnosticsa |
|||||
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 Statisticsa |
|||||
|
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/Removeda |
|||
Model |
Variables
Entered |
Variables
Removed |
Method |
1 |
Tutor Attitudes, Male Dominated Field, Usefulness, Confidenceb |
. |
Enter |
a. Dependent Variable: Self Efficacy |
|||
b. All requested variables entered. |
Model Summaryb |
||||
Model |
R |
R Square |
Adjusted
R Square |
Std.
Error of the Estimate |
1 |
.567a |
.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 self-efficacy.
R =
0.567 indicates strong predictive quality of math grades on self-efficacy
R2 =
0.322 confidence, usefulness, male dominance, teacher attitude and perception
explains 32.2% of the variability of self-efficacy. The low value of R2
does not indicate a goodness of fit for the model.
ANOVAa |
||||||
Model |
Sum of
Squares |
df |
Mean
Square |
F |
Sig. |
|
1 |
Regression |
32761.211 |
4 |
8190.303 |
11.279 |
.000b |
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.
Coefficientsa |
||||||||
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,
self-efficacy 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,
self-efficacy 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,
self-efficacy 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, self-efficacy
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 Diagnosticsa |
||||||||
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 Statisticsa |
|||||
|
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 self-efficacy. However, the rest of the predictor variables were not able
to establish a correlational relationship with self-efficacy, since all of
their P values were greater than 0.05, hence lacking in statistical
significance
This negative correlation
between confidence and self-efficacy is in accordance with the literature on
self-esteem. Self-esteem and confidence are positively correlated, since both
are defined by feelings of self-worth
References
Bandura, A. (1977). Self-efficacy:
toward a unifying theory of behavioral change. Psychological Review, 191-215.
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, 502-518.
Brummelman, E., Thomaes, S., Nelemans, S., &
Castro, B. O. (2017). When parents praise inflates, childrens' self-esteem
deflates. Child Development, 1799-1809.
Dahiru, T. (2008). P – value, a true test of
statistical significance? A cautionary note. Ann Ib Postgraduate Med,
21-26.
Engle, G. L. (1981). The clinical application of
the biopsychosocial model. Journal of Medicine and Philosophy, 101-124.
Gallo, A. (2015, November 4). A refresher on
regression analysis . Retrieved from Harvard Business Review :
https://hbr.org/2015/11/a-refresher-on-regression-analysis
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:
https://statistics.laerd.com/spss-tutorials/multiple-regression-using-spss-statistics.php
Minitab. (2017). What are type I and type II
errors? . Retrieved from Minitab:
https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/basic-statistics/inference/supporting-topics/basics/type-i-and-type-ii-error/
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/collinearity-what-it-means-why-its-bad-and-how-does-it-affect-other-models-94e1db984168
SPSS T. (2019). ANOVA - simple introduction.
Retrieved from SPSS Tutorials :
https://www.spss-tutorials.com/anova-what-is-it/
ST. (2019). Risidual analysis in regression .
Retrieved from Stat Trek:
https://stattrek.com/regression/residual-analysis.aspx
Twenge, J., & Campbell, K. (2017). Motivation
. In Personality Psychology (pp. 179-209). New York : Pearson .
Yi, T., Ji, J., & Yu, F. (2018). The effect of
metacognitive knowledge on mathematics performance in self-regulated learning
framework—multiple mediation of self-efficacy and motivation. Frontiers in
Psychology,
https://doi-org.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 locale-specific (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).