Freud versus Eysenck Theories of Personality

 

 

            Theories of personality that were developed throughout the 20^th century were varied in approach and substance. There were four major schools the psychodynamic, humanistic, trait theorists, and the social cognitive perspective (Gedney-Rubel, 2014). This paper will compare and contrast two perspectives, the psychodynamic and the work of Sigmund Freud (1856 – 1939) with trait theory and the work of Hans Eysenck (1916 – 1997).

As its name suggests, psychodynamic theory is centered on the idea that there’s a continuous dynamic conflict between the conscious and the unconscious mind (Deal, 2007). This perspective was pioneered by Freud, who relied heavily on his clinical observations with his patients in order to develop his theories (Ciabattari, 2014). Freud also proposed two personality models, the topographic and the structural models. The first is composed of the mental layers, the conscious, pre-conscious, and the unconscious. The structural model includes the id, ego, and superego. Both models overlap, where the id and the superego are unconscious drives, and the ego is the conscious mind that mitigates between the unconscious desires of the id, and the unconscious moral compass of the super-ego. When the dynamic conflicts of the unconscious can’t be resolved, a state of neurosis presents itself.

Freud’s theory relied heavily on the influence of the unconscious, but because he relied heavily of self-report, introspection, and other subjective techniques to form his theories, they were deemed unscientific due to the difficulty of testing them empirically.  (Twenge & Campbell, 2017).

Trait theory on the other hand was propelled forward by Gordon Allport, who defined personality through conscious motivations and behavior patterns, also by using traits as descriptive measures of personality rather than attempting to explain it (Rosenzweig & Fisher, 1997). Hans Eysenck was a trait theorist whose work revolved around the notion that people’s traits are inborn and is rooted in biology, he designated those traits as temperaments (Eysenck, 1967) Eysenck was also the first trait theorist to use the statistical tool of factor analysis to determine personality traits, he developed what is known as the Eysenck 3 factor model . In it, people’s personalities are measured on a sliding scale between two opposite traits. The three main factors are neuroticism vs calm, introversion vs extroversion, and psychotic vs impulse control (Costa & McRae, 1995). Eysenck theorized that high scores on the neuroticism scale meant that people will be more likely to suffer from neurotic problems, and not neurotic themselves. As for introversion and extraversion, he hypothesized that extraverted brains required more external stimulation than introverted brains, which are more sensitive to external stimuli. As for high scores on the psychoticism scale, they indicate that in certain environments, the individual will have very low impulse control and a tendency to be out of touch with reality (Cooper, 2010).

Eysenck’s 3 factor model was later developed by Paul Costa and Robert McRae into what is now known as the 5 factor model, which is the revised and updated scientific approach to measuring personality (Costa & McRae, 1995). However, Freud, even though was unscientific in his approach, he was the first to emphasize unconscious drives, which shape personality (Deal, 2007).

 

References

Ciabattari, J. (2014, April 22). Does Sigmund Freud still matter ? Retrieved July 31, 2018, from BBC: http://www.bbc.com/culture/story/20140421-does-freud-still-matter

Cooper, C. (2010). Biological, Cognitive and Social Bases of Personality. In Individual differences and personality (pp. 96–117). London: Hodder.

Costa, P., & McRae, R. (1995). Primary traits of eysenck's p-e-n system: three- and five-factor solutions. Journal of Personality and Social Psychology, 308.

Deal, K. H. (2007). Psychodynamic theory . Advances in Social Work.

Eysenck, H. J. (1967). The biological basis of personality. Springfield: Thomas Publishing.

Gedney-Rubel, S. (2014). Exploring personality theory: past, present, and future considerations. New Hampshire: Southern New Hampshire University.

Rosenzweig, S., & Fisher, S. L. (1997). "Idiographic" vis-a-vis "idiodynamic" in the historical perspective of personality theory: Remembering Gordon Allport, 1897-1997. Journal of the History of the Behavioral Sciences , 405-419.

Twenge, J. M., & Campbell, W. K. (2017). Psychodynamic approaches. In Personality Psychology: Understanding Yourself and Others (pp. 142-176). New York: Pearson.

 

Introduction to Personality Theory

 

The goals of psychology as a discipline are concerned with describing, explaining and predicting people’s behavior, with the aim of supporting and helping people with their lives (Poznań , 2014). One way of approaching those goals is by studying individual differences, which are split into two categories, traits and states. Traits are concerned with personality and ability whereas states are concerned with mood and motivation (Cooper, 2010).

Personality is comprised on an individual’s usual pattern of feelings, thoughts and behavior (Cooper, 2010). Prior to the emergence of personality theory however, psychologists in the 20^th century had what was called the person-situation debate, where they debated whether the person’s distinct characteristics determined behavior or the specific environment the person is in, or situation is a better determinant of behavior (Epstein, 1985). Modern psychology places somewhat equal weight on both the person and the situation (Judge & Zappata, 2015).

Also throughout the 20^th century, there were several approaches that dealt with personality theory, from both the quantitative and the clinical or therapeutic schools of psychology. Since psychology as a science was not well developed in the beginnings of the 20^th century (Chung & Hyland, 2012), personality was first approached by schools that belonged to the therapeutic tradition. Some of the most notable schools were the psychodynamic and the humanistic schools of psychology (Laureate online education, 2016).

The psychodynamic school of thought was founded by the famous Austrian psychologist, Sigmund Freud. Freud’s theory focuses on the dichotomy between the conscious and unconscious mind and their respective dynamic conflicts (Deal, 2007). Freud also proposed two personality models, the topographic and the structural models. The first is composed of the mental layers, the conscious, pre-conscious, and the unconscious. The structural model includes the id, ego, and superego. Both models overlap, where the id and the superego are unconscious drives, and the ego is the conscious mind that mitigates between the unconscious desires of the id, and the unconscious moral compass of the super-ego. When the dynamic conflicts of the unconscious can’t be resolved, a state of neurosis presents itself. Freud also developed the concept of defense mechanisms, and the role of defense mechanisms is to prohibit undesirable unconscious thoughts from entering the conscious mind. Most Freudian theories do not meet the scientific standards of modern psychology because they have been very difficult if not near impossible to test. However, some defense mechanisms, such as denial and reaction formation have substantial scientific literature to back them up (Twenge & Campbell, 2017).

As for the humanistic schools of thought, it was founded by Abraham Maslow and expanded by Carl Rogers, it was formed to some extent as a rebuttal to psychodynamic theory and the early 20^th century behaviorism. Humanism focused on the idea that humans are conscious beings whereas the others focused more on unconscious drives (Twenge & Campbell, Self-actualization and humanistic psychology, 2017). Carl Rogers developed the Self-Theory, which is split into two modes, the congruence and the incongruence modes. In the first the ideal self is equal to the true self. In the later there are the ideal self, self-image, and true-self. Most people are incongruent and aim to be congruent (JP, 2018).

Even though both humanistic and psychodynamic schools are unscientific by modern standards, they have certainly contributed greatly to the advancement of personality psychology.

 

 

References

Chung, M. C., & Hyland, M. E. (2012). Evaluation of the idea that psychology is a science: what is science ? In M. C. Chung, & M. E. Hyland, History and Philosophy of Psychology (pp. 76 - 79). West Sussex: John Wiley & Sons Incorporated.

Cooper, C. (2010). Introduction to individual differences. In Individual Differences and Personality (pp. 1-6). London: Hodder.

Deal, K. H. (2007). Psychodynamic theory . Advances in Social Work.

Epstein, S. (1985). The person-situation debate in historical and current perspective.  Psychological Bulletin , 98(3):513-37.

JP. (2018). Revisiting carl rogers theory of personality. Retrieved from Journal Psyche: http://journalpsyche.org/revisiting-carl-rogers-theory-of-personality/

Judge, & Zappata. (2015). The person-situation debate revisited: effect of situation strength and trait activation on the validity of the big five personality traits in predicting job performance. Academy of Management Journal, 1149–1179.

Laureate online education. (2016, November 10). Week 5: conceptual and historical paradigms in psychology: a critical analysis. part 1. gestalt psychology. Mind, Brain and Behavior. Netherlands: Laureate Online Education B.V.

Poznań . (2014). On the method of psychology. an introduction to the comparative methodology of scientific research. Poznań studies in the philosophy of the sciences and the humanities, p. 61.

Twenge, J. M., & Campbell, W. K. (2017). Psychodynamic approaches. In Personality Psychology: Understanding Yourself and Others (pp. 142-176). New York: Pearson.

Twenge, J. M., & Campbell, W. K. (2017). Self-actualization and humanistic psychology. In Personality Psychology (pp. 189-191). New York: Pearson.

 

 

Biopsychosocial Analysis of the Effects of Self-Efficacy

 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 (Engle, 1981). Engle’s biopsychosocial model is based on the general systems model where humans are at one level of a multidimensional system, where analysis can be conducted vertically by widening the scope to include larger ecosystems in the analysis, or narrowing the analysis into the cellular or atomic level. He also argued that a horizontal dimension of analysis of health behavior can be conducted by factoring in time and other influences in the environment of the same vertical level (Laureate online education, 2016).

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 (Bandura, 1977). Self-efficacy is also related to motivational psychology, which deals with what drives human behavior (Yi, Ji, & Yu, 2018). Moreover, what drives human behavior can be biological, for example a state of sexual arousal greatly influences behavior and impulsivity (Born, Wolvaardt, & Mcintosh, 2015). Since there are biological, psychological, and social influences on self-efficacy and repercussions, the biopsychosocial model is an ideal fit.

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 (SPSS T., 2019). However since we have more than 3 predictive variables, a factorial ANOVA cannot be used, since it would be increasing the chance of conducting a Type 1 error (Minitab, 2017).

            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 (Lund, 2018). When analyzing multiple predictive variables, it is called multiple regression analysis, however in order to conduct it, the predictor variables should be continuous or dichotomous (Gallo, 2015). Since Age, math scores, and science scores are categorical predictor variables, they will not be included in the multiple regression analysis. Instead, three independent simple regression analyses will be conducted in order to measure their respective relationships with the outcome variable (self-efficacy).

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 (Saslow, 2018). The Eigenvalue = 0.002 which is close to 0 usually indicates that the predictor variables are intercorrelated. However, since age is the only predictor variable in this table, the Eigenvalue is non-indicative.

 

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 (ST, 2019).

 

 

 

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 (Saslow, 2018). The Eigenvalues of .086 for confidence, .048 for usefulness, .030 for male dominated field, and .002 for tutor attitude and perception, which are close to 0 usually indicates that the predictor variables are intercorrelated.

 

 

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 (Dahiru, 2008).

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 (Brummelman, Thomaes, Nelemans, & Castro, 2017). However, motivational research indicates that self-efficacy is a better predictor of academic performance than self-esteem and confidence, and that the latter can negatively impact performance (Twenge & Campbell, 2017).

 

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

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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).