Investigating Inhibitory Control In Adolescence: A Correlation And Regression Analysis Essay.

The aim of the study

The aim of this study is to investigate whether inhibitory control in adolescence might be predicted from whether one is a boy or girl, their level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism. Given a set of data like in our scenario, this aim can be achieved by outlining a definite research problem (s).

A research problem defines the topic of study (Frankfort-Nachias, 2015). A research problem also acts as the basis under which various analyses are done and conclusions drawn from the research findings (Frankfort-Nachias, 2015).

From this case study 1, there are two major research problems that can be drawn following the objective/aim of this study. Firstly, to investigate the relationship between inhibitory control in adolescence and sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism. This is achievable by use of correlation analysis.

Correlation analysis involves investigating the extent or degree of association between variables (Judea, Madelyn, & Nicholas, 2009). Correlation coefficient is a constant indicating the degree of association (Judea, Madelyn, & Nicholas, 2009).

A correlation coefficient ranges between -1 to +1 (Lind, 2008). Correlation coefficient can be classified as either positive or negative (Lind, 2008). Similarly a positive or negative correlation can further be classified as weak or strong (Lind, 2008).

A week positive correlation has a value of between 0 to 0.5 while strong positive correlation is those that have values between 0.5 and 1 (Tim, 205). Likewise, a week negative correlation is that with a value between 0 and -.05 while strong negative correlation  is that with a values between -0.5 to -1 (Tim, 205).

In order to conduct a proper correlation analysis test, hypothesis must be formulated. A hypothesis is a statement about a phenomenon the truth of which is unknown (Frankfort-Nachias, 2015). The truth about a hypothesis is testable (Judea, Madelyn, & Nicholas, 2009).

A hypothesis is properly stated when both the null and alternative hypothesis are outlined. A null hypothesis is stated negatively (Judea, Madelyn, & Nicholas, 2009). A null hypothesis suggests that there is no relationship between the variables (Judea, Madelyn, & Nicholas, 2009). On the other hand, an alternative hypothesis is stated positively (Judea, Madelyn, & Nicholas, 2009). An alternative hypothesis suggests that there is relationship between the variables under study (Judea, Madelyn, & Nicholas, 2009). Null Hypothesis is denoted by H0 while the alternative hypothesis is denoted by H1 (Judea, Madelyn, & Nicholas, 2009)

Two major research problems

The following hypothesis are formulated and used for the correlation analysis;

H0:  There is no relationship between inhibitory control in adolescence and sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism.

H1: There is a relationship between inhibitory control in adolescence and sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism.

The correlation analysis has been done using SPSS. An SPSS is a data analysis tool. The following table outlines the correlation analysis table. From the table, it is clear that there a strong positive correlation or other relationship between inhibitory control and agreement agreeable (0.88). There is a weak positive correlation between inhibitory and sex. There is a weak negative correlation between inhibitory and Conscientiousness.

Correlations
			Inhibitory Control	Agreeableness	Conscientiousness	Openness	Neuroticism
Spearman's rho	Sex of Participant	Correlation Coefficient	.005	.088	-.007	-.031	-.101
		Sig. (2-tailed)	.940	.147	.909	.605	.095
		N	275	275	275	275	275
	Inhibitory Control	Correlation Coefficient	1.000	.369**	.410**	.391**	-.033
		Sig. (2-tailed)	.	.000	.000	.000	.589
		N	275	275	275	275	275
	Agreeableness	Correlation Coefficient	.369**	1.000	.241**	.209**	-.075
		Sig. (2-tailed)	.000	.	.000	.000	.213
		N	275	275	275	275	275
	Conscientiousness	Correlation Coefficient	.410**	.241**	1.000	.426**	-.398**
		Sig. (2-tailed)	.000	.000	.	.000	.000
		N	275	275	275	275	275
	Openness	Correlation Coefficient	.391**	.209**	.426**	1.000	-.374**
		Sig. (2-tailed)	.000	.000	.000	.	.000
		N	275	275	275	275	275
	Extroversion	Correlation Coefficient	-.426**	.403**	.254**	.292**	-.416**
		Sig. (2-tailed)	.000	.000	.000	.000	.000
		N	275	275	275	275	275
	Neuroticism	Correlation Coefficient	-.033	-.075	-.398**	-.374**	1.000
		Sig. (2-tailed)	.589	.213	.000	.000	.
		N	275	275	275	275	275
**. Correlation is significant at the 0.01 level (2-tailed).

The second research problem is investigate whether there is a relationship between inhibitory control in adolescence and sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism. In order to achieve this objective, we need to carry out a regression analysis.

Regression analysis outlines the degree of association between one dependent variable and one or more other independent variables (Lind, 2008). An dependent variable is also known as predictor variable or explanatory variable (Judea, Madelyn, & Nicholas, 2009).

The following hypotheses are formulated in order to complete the analysis;

H0: Inhibitory control in adolescence cannot be predicted using sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism.

H1: Inhibitory control in adolescence can be predicted using sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism.

A regression analysis produces a regression line or the fitted line (Suprun, 2009). This regression line or the fitted line is used evaluate whether Inhibitory control in adolescence can be predicted using sex, level of agreeableness, conscientiousness, extroversion, openness to experience, and neuroticism. Theoretically, the following line can be fitted based on hypothesis.

Inhibitory= B0+B1 (sex) =B2(agre)+B3(cons) B4(extr)+B5(open)+B6(neu)

Where B0, B1, B2, B3, B4 and B5 are constants representing the regression coefficient and;

Agre=agreeable, Cons= conscientiousness,, sex=sex, Open=openness tp experience and neu= neuroticism.

The following three tables outlines the output of the regression analysis done in excel. Table 1 is model summary future cash. The r squared is .901. This implies that our sample explains 90.1% of the population (Robert, 2004). This is an indication that ere our sample may have limited biasedness.

The second table represents the Analysis of Variance table ANOVA. An ANOVA is used to compare means of variables are equal. From the output, the p- value is 0.00 which is less than the alpha value of 0.05. This implies that statistically, there is no evidence to prove that there is no significant difference in the means until them. 

Model Summary
Model	R	R Square	Adjusted R Square	Std. Error of the Estimate	Change Statistics					Durbin-Watson
					R Square Change	F Change	df1	df2	Sig. F Change
1	.949a	.901	.899	2.366	.901	407.781	6	268	.000	2.046
a. Predictors: (Constant), Neuroticism, Agreeableness, Sex of Participant, Openness, Extroversion, Conscientiousness
b. Dependent Variable: Inhibitory Control

Correlation analysis

ANOVAa
Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	13693.672	6	2282.279	407.781	.000b
	Residual	1499.950	268	5.597
	Total	15193.622	274
a. Dependent Variable: Inhibitory Control
b. Predictors: (Constant), Neuroticism, Agreeableness, Sex of Participant, Openness, Extroversion, Conscientiousness

The following table outlines the coefficients table. From this table, we can write our theoretical relationship as follows; 

Inhibitory=23.82+0.071+(sex)+0.459(agre)+.263(cons)-.683(extr)+.-0.43(open)-.053(neu). This is the line that can be used to predict inhibitory given the variables.  

Coefficientsa
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. Error	Beta			Lower Bound	Upper Bound
1	(Constant)	23.815	1.972		12.074	.000	19.932	27.699
	Sex of Participant	.071	.291	.005	.245	.807	-.502	.644
	Agreeableness	.459	.017	.605	27.817	.000	.427	.492
	Conscientiousness	.263	.020	.303	13.042	.000	.223	.302
	Extroversion	-.683	.018	-.877	-38.290	.000	-.718	-.648
	Openness	.345	.021	.372	16.436	.000	.304	.387
	Neuroticism	-.053	.019	-.066	-2.817	.005	-.090	-.016
a. Dependent Variable: Inhibitory Control

The aim of this study is to investigate the possible differences in size of adolescents’ Amygdala brain structure based on self-reported proficiency in videos games.  This objective is achievable by displaying the descriptive statistics (Raynald, 2006). The table below shows the descriptive characteristics of the variables.  

Descriptive Statistics
	N	Range	Minimum	Maximum	Mean		Std. Deviation	Variance	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Reaction Time Pre-Test (in ms)	275	979	406	1385	767.96	13.556	224.794	50532.557	.628	.147	-.449	.293
Reaction Time Post-Test (in ms)	275	886	426	1312	784.65	14.000	232.159	53897.828	.453	.147	-.856	.293
Valid N (listwise)	275

Apart from the quantitative display of variables, a graphical representation can be drawn. In this scenario, a bar plot of proficiency against Amygdala is shown below. From this graph, we can say the amygdala is evenly the body. A bar graph shows the frequencies of the data set that is under study. 

This study involves investigation of possible differences in reaction times between before and after sleep. To handle this effectively, we need to develop a descriptive table (Lind, 2008). Similarly, we need to carry out a hypothesis test to really find out if there is any significant difference in the average times before and after sleep (Robert, 2004).

From the results in the table below, it is clear that there is a difference in the average times. On average, the pre-test time is much higher than the post-test time. Similarly, the variance and the standard deviation of pre-test time are higher than the post-test time. 

Descriptive Statistics
	N	Range	Minimum	Maximum	Mean		Std. Deviation	Variance	Skewness		Kurtosis
	Statistic	Statistic	Statistic	Statistic	Statistic	Std. Error	Statistic	Statistic	Statistic	Std. Error	Statistic	Std. Error
Reaction Time Pre-Test (in ms)	275	979	406	1385	767.96	13.556	224.794	50532.557	.628	.147	-.449	.293
Reaction Time Post-Test (in ms)	275	886	426	1312	784.65	14.000	232.159	53897.828	.453	.147	-.856	.293
Valid N (listwise)	275

In order to establish whether there is any significant difference in the average times (pre and post time), we need to conduct a hypothesis test. Since the test involves mean comparison, the most suitable method is the use of an ANOVA (Frankfort-Nachias, 2015). The following hypothesis is formulated;

H0: There is no difference in average reaction times before and after sleep training

H1: There is difference in average reaction times before and after sleep training

The SPSS output is shown below. From the output, the p-value is 0.000 which is less than the alpha level, 0.05. This implies that we reject the null hypothesis that t. here is no difference in average reaction times before and after sleep training. We conclude that there is no sufficient evidence to prove that there is no difference in average reaction times before and after sleep training.

ANOVA
Reaction Time Post-Test (in ms)
	Sum of Squares	df	Mean Square	F	Sig.
Between Groups	13290369.952	189	70319.418	4.045	.000
Within Groups	1477634.833	85	17383.939
Total	14768004.785	274

References

Frankfort-Nachias, C. &.-G. (2015). Social Statistics for a diverse society. Thousand Oaks, CA: Sage Publications.

Judea, P., Madelyn, G., & Nicholas, P. J. (2009). Causal Inference in Statistics: A primer. Wiley.

Lind, D. A. (2008). Statistical Techniques in Business & . Boston.: McGraw-Hill Irwin.

Raynald, L. (2006). SPSS Programming and Data Management: A Guide for SPSS and SAS Users.

Robert, J. T. (2004). International Phycology and Scientific Psycology: At the Cross of Future Psycology. Psycology, 15-20.

Stuart A., O. K. (1999). Kendall’s Advanced Theory of Statistics: Volume 2A- Classical Inference & the linear Model.

Suprun, A. P. (2009). Relativist Psycology: A new Concept of Psycological Measurement. Psycology, 2-10.

Tim, S. (205). Mastering Statistical Process Control: A handbook for Performance Improvement Using Cases. 50-56.