(i) Develop an appropriate research question (or integrated set of research questions) that is logically coherent with both the constructs listed in the description and the focal relationship being referred to in each study description.
(ii) Choose the appropriate variables from your SPSS data file to answer the research question (or set of research questions) you have developed for each study.
(iii) Carry out an appropriate set of analyses in SPSS so that you can meaningfully and fully answer the research question(s) you have developed (use the way in which SPSS analyses were done in your lab classes each week as a guide for how to undertake this).
(iv) Undertake calculations in XECI using results from SPSS to appropriately, meaningfully, and fully answer your research question (or set of research questions).
(v) Writeup the results of each study, such that you fully answer your research question (or set of integrated research questions) in a way that is logically coherent with the question(s) you asked, the brief description of each study listed in this document, and the set of analyses you have performed in SPSS and XECI.
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 (FrankfortNachias, 2015). A research problem also acts as the basis under which various analyses are done and conclusions drawn from the research findings (FrankfortNachias, 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 (FrankfortNachias, 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. (2tailed) 
.940 
.147 
.909 
.605 
.095 

N 
275 
275 
275 
275 
275 

Inhibitory Control 
Correlation Coefficient 
1.000 
.369^{**} 
.410^{**} 
.391^{**} 
.033 

Sig. (2tailed) 
. 
.000 
.000 
.000 
.589 

N 
275 
275 
275 
275 
275 

Agreeableness 
Correlation Coefficient 
.369^{**} 
1.000 
.241^{**} 
.209^{**} 
.075 

Sig. (2tailed) 
.000 
. 
.000 
.000 
.213 

N 
275 
275 
275 
275 
275 

Conscientiousness 
Correlation Coefficient 
.410^{**} 
.241^{**} 
1.000 
.426^{**} 
.398^{**} 

Sig. (2tailed) 
.000 
.000 
. 
.000 
.000 

N 
275 
275 
275 
275 
275 

Openness 
Correlation Coefficient 
.391^{**} 
.209^{**} 
.426^{**} 
1.000 
.374^{**} 

Sig. (2tailed) 
.000 
.000 
.000 
. 
.000 

N 
275 
275 
275 
275 
275 

Extroversion 
Correlation Coefficient 
.426^{**} 
.403^{**} 
.254^{**} 
.292^{**} 
.416^{**} 

Sig. (2tailed) 
.000 
.000 
.000 
.000 
.000 

N 
275 
275 
275 
275 
275 

Neuroticism 
Correlation Coefficient 
.033 
.075 
.398^{**} 
.374^{**} 
1.000 

Sig. (2tailed) 
.589 
.213 
.000 
.000 
. 

N 
275 
275 
275 
275 
275 

**. Correlation is significant at the 0.01 level (2tailed). 
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 
DurbinWatson 

R Square Change 
F Change 
df1 
df2 
Sig. F Change 

1 
.949^{a} 
.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
ANOVA^{a} 

Model 
Sum of Squares 
df 
Mean Square 
F 
Sig. 

1 
Regression 
13693.672 
6 
2282.279 
407.781 
.000^{b} 
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.
Coefficients^{a} 

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 selfreported 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 PreTest (in ms) 
275 
979 
406 
1385 
767.96 
13.556 
224.794 
50532.557 
.628 
.147 
.449 
.293 
Reaction Time PostTest (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 pretest time is much higher than the posttest time. Similarly, the variance and the standard deviation of pretest time are higher than the posttest 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 PreTest (in ms) 
275 
979 
406 
1385 
767.96 
13.556 
224.794 
50532.557 
.628 
.147 
.449 
.293 
Reaction Time PostTest (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 (FrankfortNachias, 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 pvalue 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 PostTest (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
FrankfortNachias, 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.: McGrawHill 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, 1520.
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, 210.
Tim, S. (205). Mastering Statistical Process Control: A handbook for Performance Improvement Using Cases. 5056.
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