Spss analysis in psychology

Document Type:Thesis

Subject Area:Statistics

Document 1

The participants rated the personality types as introvert stable, extrovert stable, introvert unstable and extrovert unstable. The degree choices categorized as Psychology, Medicine, History and English. The analysis presents the research question defined by; Is there an association between the personality traits (introvert stable, extrovert stable, extrovert unstable and introvert unstable) and the degree choices (Psychology, Medicine, History and English) present in the college? The hypothesis defined by the statements; Ho: There is no an association between the personality traits (introvert stable, extrovert stable, extrovert unstable and introvert unstable) and the degree choices (Psychology, Medicine, History and English) present in the college H1: There is an association between the personality traits (introvert stable, extrovert stable, extrovert unstable and introvert unstable) and the degree choices (Psychology, Medicine, History and English) present in the college Chi-square tests for association best for the data since they are both categorical variables (nominal vs nominal) with four categories each (Sharpe, 2015). ii) Exploratory data analysis Table 1: Exploratory data analysis (Test for normality) Tests of Normality   Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Subject. Personality. Ho: Subject and personality are normally distributed H1: Subject and personality are not normally distributed According to Kolmogorov-Smirnov test, subject (Kolmogorov-Smirnov=0. df=150, p=0. and personality (Kolmogorov-Smirnov=0. df=150, p=0. do not follow a normal distribution (p<0. at 5% level of significance. This qualifies non-parametric test for the analysis (Chi-square test). iii) Statistical test for the hypothesis Table 2: Chi-square test for hypothesis Chi-Square Tests   Value df Asymptotic Significance (2-sided) Pearson Chi-Square 4. a 9. Likelihood Ratio 4. Linear-by-Linear Association 1.

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N of Valid Cases 150 a. cells (43. This situation leads to the need for one away analysis of variance (One Way Anova) to test for the differences at 5% level of significance. The conditions for the test are met since the dependent variable (neuroticism) is measured on a continous scale (Kim, 2017) normally distributed (Cox, 2017) and the existence of similar standard deviations for the population, in agreement with the law of large numbers. The independent variable five personality traits (introvert stable, extrovert stable, extrovert unstable and introvert unstable) measured on a categorical scale, hence making it possible to come up with the desired analysis for the expected outcomes. ii) Exploratory data analysis Table 3: Exploratory data analysis (Test for normality) Tests of Normality   Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. Statistic df Sig. Error 95% Confidence Interval for Mean Minimum Maximum Between- Component Variance Lower Bound Upper Bound Introvert stable 57 20.

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Extrovert stable 58 19. Extrovert unstable 20 20. Introvert unstable 15 22. Total 150 20. with a standard deviation of 8. on neuroticism levels. The maximum measure of 42. with a minimum of 5. also implicated by the introvert stable according to the neuroticism levels. with a minimum of 6. on the neuroticism levels. Introvert unstable (highest average) had an average of 22. with a standard deviation of 7. on the neuroticism measures. df=(3,146), p=0. Changes in the five personality traits no directly associated with differences in the five personality traits (introvert stable, extrovert stable, extrovert unstable and introvert unstable). This shown by the fact that the p-value of the F-test statistic greater than 0. at 5% level of significance (p>0. Figure 1: Means plot for neuroticism and the personality traits According to the means graph, introvert stable declines from between 21. In terms of the situation, the research questions are depicted by; Does time spent on smartphones affect average exam grades earned by students? Does time spent on smartphones affect the IQ scores of the students? Does time spent on smartphones affect the conscientiousness levels? Does the time spent on smartphones affect the attention scales? The research questions leads to the hypothesis regarding the aims of the study, based on the study variables defined by; H1: Time spent on smartphones affects average exam grades earned by students H2: Time spent on smartphones affects the IQ scores of the students H3: Time spent on smartphones affect the conscientiousness levels of students H4: The time spent on smartphones affect the attention scales of students On the methodology, regression analysis best suited in determining the desired relationships between the dependent and the independent variable within the study.

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Regression analysis of variance used to determine the suitability of the model at 5% level of significance and the model coefficients used to test for the hypothesis. Before the test, exploratory data analysis using the Kolmogorov-Smirnov test used to determine whether the response variables follow a normal distribution. The regression model (Harrell, 2017) works on the assumptions that the predictor variable follows a normal distribution, no multi-collinearity evident on the variables and defined on a continous scale and has no constant variance on white noise. ii) Exploratory data analysis Table 6: Tests for normality for average grade, IQ scores, Conscientiousness and attention scale Tests of Normality   Kolmogorov-Smirnova Shapiro-Wilk Statistic df Sig. df=150, p=0. conscientiousness (Kolmogorov-Smirnov=0. df=150, p=0. and attention scale (Kolmogorov-Smirnov=0. df=150, p=0. Predictors: (Constant), SmartPhoneUse On the analysis of variance, the model analysis of variance, the model on time spent on smartphones on average grades proves adequate in predicting the scores (F=648.

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df=(1,48), p=0. This makes the model adequate for the perceived relationships at 5% level of significance. Model coefficients Table 8: Model coefficients table for effects of time spent on smartphone on average grades Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Regression 27. b Residual 14021. Total 14049.       a. Dependent Variable: IQ b. Dependent Variable: IQ On the model coefficients, the smartphone use reduces the student average scores slightly (β=-0. with an insignificant effect within the model (t=-0. p=0. The analysis indicates that smartphone has minimal effects on the IQ scores on the student score, hence the null hypothesis holds. The model is defined by the equation; Effects of time spent on smartphone on conscientiousness Analysis of variance Table 11: Regression analysis of variance for the effects of time spent on smartphone on conscientiousness ANOVAa Model Sum of Squares df Mean Square F Sig.

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Dependent Variable: Conscientiousness On the model coefficients, the time spent on smartphones increased the level of conscientiousness by 0. anytime the time increased by a unit (β=0. This shows that time used on smartphone significantly affects conscientiousness scores at 5% level of significance (t=2. p=0. since the p-value is less than 0. This due to the p-value greater than 0. at 5% level of significance hence inappropriate for the fit. Model coefficients Table 14: Model coefficients table for effects of time spent on smartphone on on attention scale scores Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) 43. Apress, Berkeley, CA. Harrell Jr, F. E.  Regression modeling strategies: with applications to linear models, logistic and ordinal regression, and survival analysis. Springer.

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