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

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).

 

Hypothesis Testing

 

Hypotheses are scientific falsifiable statements (Chung & Hyland, 2012) that are usually written in pairs, the null and the research hypotheses. The null, designated as H₀, is when there is no effect between the populations. The research hypothesis, designated as H₁, is when there’s an effect between the populations (Dancey & Reidy, 2017).

The research hypothesis can be one-directional or one tailed, as in there is a directional relationship between populations. It can be non-directional or two tailed hypothesis (Bruin, 2006). The major difference between experimental hypotheses and research hypotheses in correlational research is that experiments are always directional. Experimental hypotheses attempt to demonstrate causal and effect between independent and dependent variables. Whereas in correlational research, hypotheses observe relationships among variables, thus can be one or bi-directional, since they can also be descriptive (Dancey & Reidy, 2017). 

Research hypotheses are tested by trying to disprove their respective null hypotheses by providing quantitative evidence, through inferential statistics (SJSU, 2016). Inferential statistics is a statistical method used to make inferences about a population based on data taken from a random sample of a population (Minitab , 2017). A form of statistical inference that is used to determine the probability that the null hypothesis is correct, despite evidence that support the research hypothesis, is the null hypothesis significance test (NHST) . The result of the NHST is determined by the sample size and the binomial parameter, and expressed as a probability (p-value) in percentage or decimal. In psychology, the result of a study is accepted if the level of probability that the null hypothesis is correct is less than 5%, and expressed as P<0.5 , also known as level of statistical significance (Dancey & Reidy, 2017).

The rationale behind setting the level of statistical significance at P<0.5 has to do with what the scientific community perceives as acceptable level of error occurrence (Dancey & Reidy, 2017).  There are two types of error that can occur when taking NHST into account. The fist is called a type 1 error, which is when the research rejects the null hypothesis when it’s true. The second is called type 2 error, which is when the researcher accepts the null hypothesis when it is wrong. When P<0.5 the probability of type 1 error is less than 5%, and when P>0.5, the probability of type 2 error is less than 5%. Therefore, 5% is chosen as a balanced probability that tolerates the occurrence of both errors (Minitab, 2017). However, in the medical field, the tolerance for the occurrence of type 1 error is 1% (p<0.01). The tolerance for type 1 error is low because human life is at stake (Dahiru, 2008).

One major pitfall of NHST is that it’s not comparable and cumulative, whereas scientific research is (SJSU, 2016). Another is that psychological significance is determined by the level of effect, while NHST is mistaken for psychological significance, which is known as the permanent illusion (Cohen, 1994). Type 1 and type 2 errors are also pitfalls. However, all pitfalls can be accounted for with sound research design and research replication (Dancey & Reidy, 2017).

References

Bruin, J. (2006). Institute for digital research and education . Retrieved from University of Califirnia Los Angeles : https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faq-what-are-the-differences-between-one-tailed-and-two-tailed-tests/

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.

Cohen, J. (1994). The earth is round (p <.05). American Psychologist , 997- 1003.

Dahiru, T. (2008). P – value, a true test of statistical significance? A cautionary note. Ann Ib Postgraduate Med, 21-26.

Dancey, C., & Reidy, J. (2017). Hypothesis testing and statistical significance . In Statistics Without Maths for Psychology (7th ed.) (pp. 134-173). Harlow, UK: Pearson.

Minitab . (2017). What are inferential statistics ? Retrieved from Minitab: https://support.minitab.com/en-us/minitab-express/1/help-and-how-to/basic-statistics/inference/supporting-topics/basics/what-are-inferential-statistics/

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/

SJSU. (2016, May 8). Introduction to null hypothesis significance testing. Retrieved from San Jose State University: http://www.sjsu.edu/faculty/gerstman/StatPrimer/hyp-test.pdf

 

Correlational and Causal Relationships

 

Correlational and causal research both follow similar basic scientific research design, where a research question is posed, then followed with a hypothesis and a null-hypothesis, where quantitative data either supports the research or null hypothesis (Gonzalez, 2018). However, they differ greatly when it comes to the purpose and outcome of the research. Correlational research attempts to demonstrate a relationship between two or more variables, usually through surveys, but it doesn’t demonstrate causation among variables (SEP, 2016). On the contrary, causal research aims at demonstrating a relationship causal relationship among variables, as in variable A causes variable B, and does so by accounting for extraneous variables by following the experimental method (Srinagesh, 2006). In order to further demonstrate the difference between the two research methods, the following example has been chosen.

Example:

The relationship between age and driving speed

Does the example demonstrate a causal or correlational relationship?

The above example attempts to demonstrate a correlational relationship due to several reasons. The first is that it is vaguely phrased  (Huff, 1973) in a way that indicates that there might be some kind of relationship between the two variables, age and driving speed. If it were to demonstrate a causal relationship, it should be more specific with a clear direction (Dancey & Reidy, 2017).  A more appropriate phrasing if were to attempt to demonstrate a causal relationship would be, as male driver’s age increase, this causes their testosterone level decreases, which causes their average driving speed to decline.  

What’s needed to demonstrate a causal relationship?

The main requirement for demonstrating a causal relationship is to account for extraneous or confounding variables. Such variables are third party variables that aren’t accounted for and might influence the relationship between the variables being studied (age and driving speed) (Srinagesh, 2006). Such variables are accounted for by following the experimental research design, which also entails having randomized samples (Andrade, 2018).

Evaluate whether a chi-squared test or a correlation coefficient would be more suitable to analyze the data in the example.

In order to properly analyze the data in the example, it is important identify the variable types, which will aid in identifying the most suitable test type. Chi-squared test, an association test, is used to demonstrate that the phenomena co-occur among two or more nominal variables (Dancey & Reidy, 2017). Therefore, if the example was phrased as such: how is gender (nominal) associated with driving speed (nominal), then Chi-squared would be most suitable.

Moreover, how the data is collected can sway the researcher to choose one of two types of correlation coefficients in order to evaluate the data. If an ordinal scale was used to collect the data, a non-parametric, is in does not rely of a normal distribution, test called Spearman’s rank order correlation would be the likely option (Schmid & Schmidt, 2007). However, if the data was collected using interval or ratio scale, an alternative parametric correlation coefficient called Pearson’s product-moment correlation is more suitable for analyzing the data (Bollen & Barb, 1981). Since data regarding age and driving speed is most likely to be collected on an interval scale, it would be best to use Pearson’s correlation coefficient.

References

 

Andrade, C. (2018). Internal, external, and ecological validity in research design, conduct, and evaluation. Indian Journal of Psychological Medicine , 498-499.

Bollen, K. A., & Barb, K. H. (1981). Pearson's r and coarsely categorized measures. American Sociological Review, 232-239.

Dancey, C., & Reidy, J. (2017). Hypothesis testing and statistical significance . In Statistics Without Maths for Psychology (7th ed.) (pp. 134-173). Harlow, UK: Pearson.

Dancey, C., & Reidy, J. (2017). Non-parametric statistics . In Statistics Without Math for Psychology (7th ed) (pp. 516-550). Harlow, UK: Pearson.

Gonzalez, K. (2018). What is a null hypothesis? - definition & examples. Retrieved 7 19, 2018, from Study: https://study.com/academy/lesson/what-is-a-null-hypothesis-definition-examples.html

Huff, D. (1973). How to lie with statistics. London: Penguin.

Schmid, F., & Schmidt, R. (2007). Multivariate extensions of Spearman's rho and related statistics. Statistics & Probability Letters, 407-416.

SEP. (2016). Correlational research. Salem Encyclopedia Press, Retrieved from https://liverpool.idm.oclc.org/login?url=https://search.ebscohost.com/login.aspx?direct=true&db=ers&AN=119214045&site=eds-live&scope=site.

Srinagesh, K. (2006). Planning the experiments in statistical terms. In The Principles of Experimental Research (pp. 333-372). Amsterdam : Butterworth Heinmann.