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Exploratory Data Analysis on All Variables

Exploratory Data Analysis on All Variables

Exploratory Data Analysis on All Variables

QUESTION
Complete one Word document for this assignment. You will create this Word document by cutting and pasting SPSS output into Word. Please answer the questions and include all output below your answers Name the file in the following format: lastnamefirstinitialPSY7107-7.doc (example: smithbPSY7107-7.doc).

Before beginning your assignment, download the following data set:
Chamorro-Premuzic.sav

Part A. SPSS Assignment
Part A has you really getting to know a set of data and allows you the opportunity to perform statistical tests and then interpret the output. You will rely on all you have learned to this point and add correlation and regression strategies to your tool kit.

Using the data set: Chamorro-Premuzic.sav you will focus on the variables related to Extroversion and Agreeableness (student and lecturer).

Do the following:

Exploratory Data Analysis.

Perform Exploratory Data Analysis on all variables in the data set. Because you are going to focus on Extroversion and Agreeableness, be sure to include scatterplots for these combinations of variables (Student Agreeableness/Lecture Agreeableness; Student Extroversion/Lecture Extroversion; Student Agreeableness/Lecture Extroversion; Student Extroversion/Lecture Agreeableness) and include the regression line on the chart.

Give a one to two paragraph write up of the data once you have done this.

Create an APA style table that presents descriptive statistics for the sample.

Make a decision about the missing data. How are you going to handle it and why?

Correlation. Perform a correlational analysis on the following variables: Student Extroversion, Lecture Extroversion, Student Agreeableness, Lecture Agreeableness.

Ensure you handle missing data as you decided above.

State if you are using one or two-tailed test and why.

Write up the results in APA style and interpret them.

Regression. Calculate a regression that examines whether or not you can predict if a student wants a lecturer to be extroverted using the student’s extroversion score.

Ensure you handle missing data as you decided above.

State if you are using one or two-tailed test and why.

Include diagnostics.

Discuss assumptions; are they met?

Write the results in APA style and interpret them.

Do these results differ from the correlation results above?

Multiple Regression. Calculate a multiple regression that examines whether age, gender, and student’s extroversion predict if a student wants the lecturer to be extroverted.

Ensure you handle missing data as you decided above.

State if you are using one or two-tailed test and why.
Exploratory Data Analysis on All Variables
Include diagnostics,

Discuss assumptions; are they met?

Write the results in APA style and interpret it.

Do these results differ from the correlation results above?
Part B. Applying Analytical Strategies to an Area of Research Interest.

Briefly restate your research area of interest.

Pearson Correlation. Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.

Spearman’s Correlation. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.

Partial Correlation vs. Semi-Partial Correlation. Identify three variables for which you may be interested calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses using your variables and research example. Which would you use and why?

Simple Regression. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R2 tell you about the relationship between the two variables?

Multiple Regression. Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would R2 and adjusted R2 tell you about the relationship between the variables?

Logistic Regression. Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?
You should now have the following file:
lastnamefirstinitialPSY7107-7.doc (example: smithbPSY7107-7.doc)

Exploratory Data Analysis on All Variables

ANSWER
Psychology

Student’s Name:
Institution Affiliation:

Exploratory Data Analysis on All Variables
Tests of Normality
Kolmogorov-Smirnova Shapiro-Wilk
Statistic df Sig. Statistic df Sig.
Age .280 265 .000 .621 265 .000
Gender .459 265 .000 .553 265 .000
Student Neuroticosm .051 265 .090 .992 265 .189
Student Extroversion .073 265 .001 .984 265 .005
Student Openness .049 265 .200* .990 265 .055
Student Agreeableness .059 265 .026 .991 265 .119
Student Conscientiousness .058 265 .032 .990 265 .067
Student wants Neuroticism in lecturers .190 265 .000 .796 265 .000
Student wants Extroversion in lecturers .058 265 .031 .992 265 .175
Student wants Openness in lecturers .067 265 .006 .994 265 .376
Student wants Agreeableness in lecturers .052 265 .077 .992 265 .183
Student wants Conscientiousness in lecturers .094 265 .000 .971 265 .000
*. This is a lower bound of the true significance.
a. Lilliefors Significance Correction

With the Shapiro-Wilk test, it is easier to know the outliers and rejected values in the hypothesis analysis. When the p-value is greater than p = 0.05, we reject the null hypothesis in the case analysis. In this case, the significant value for Student Extroversion and Student Neuroticosm is 0.09 and 0.01, which means that we reject the null hypothesis. The null hypothesis is rejected for the variable because of the same reason (Aljandali, 2017). We accept the null hypothesis for gender because the P of 0.000 is less than p = 0.05.

Descriptives
Statistic Std. Error
Age Mean 20.24 .232
95% Confidence Interval for Mean Lower Bound 19.78
Upper Bound 20.70
5% Trimmed Mean 19.72
Median 19.00
Variance 14.318
Std. Deviation 3.784
Minimum 2
Maximum 43
Range 41
Interquartile Range 2
Skewness 2.672 .150
Kurtosis 14.175 .298
Gender Mean .27 .027
95% Confidence Interval for Mean Lower Bound .21
Upper Bound .32
5% Trimmed Mean .24
Median .00
Variance .197
Std. Deviation .444
Minimum 0
Maximum 1
Range 1
Interquartile Range 1
Skewness 1.054 .150
Kurtosis -.896 .298
Student Neuroticosm Mean 23.7132 .53076
95% Confidence Interval for Mean Lower Bound 22.6681
Upper Bound 24.7583
5% Trimmed Mean 23.7474
Median 24.0000
Variance 74.652
Std. Deviation 8.64016
Minimum .00
Maximum 44.00
Range 44.00
Interquartile Range 10.00
Skewness -.018 .150
Kurtosis -.098 .298
Student Extroversion Mean 29.5472 .40789
95% Confidence Interval for Mean Lower Bound 28.7440
Upper Bound 30.3503
5% Trimmed Mean 29.6876
Median 30.0000
Variance 44.090
Std. Deviation 6.64000
Minimum 5.00
Maximum 46.00
Range 41.00
Interquartile Range 8.00
Skewness -.446 .150
Kurtosis .493 .298
Student Openness Mean 28.9698 .37871
95% Confidence Interval for Mean Lower Bound 28.2241
Upper Bound 29.7155
5% Trimmed Mean 28.9403
Median 29.0000
Variance 38.007
Std. Deviation 6.16495
Minimum 14.00
Maximum 44.00
Range 30.00
Interquartile Range 9.00
Skewness .137 .150
Kurtosis -.384 .298
Student Agreeableness Mean 45.7170 .46902
95% Confidence Interval for Mean Lower Bound 44.7935
Upper Bound 46.6405
5% Trimmed Mean 45.7589
Median 46.0000
Variance 58.295
Std. Deviation 7.63509
Minimum 25.00
Maximum 73.00
Range 48.00
Interquartile Range 10.00
Skewness -.078 .150
Kurtosis .394 .298
Student Conscientiousness Mean 29.6189 .42315
95% Confidence Interval for Mean Lower Bound 28.7857
Upper Bound 30.4520
5% Trimmed Mean 29.7621
Median 30.0000
Variance 47.449
Std. Deviation 6.88832
Minimum 7.00
Maximum 45.00
Range 38.00
Interquartile Range 10.00
Skewness -.314 .150
Kurtosis .019 .298
Student wants Neuroticism in lecturers Mean -21.5585 .59049
95% Confidence Interval for Mean Lower Bound -22.7212
Upper Bound -20.3958
5% Trimmed Mean -22.7526
Median -24.0000
Variance 92.399
Std. Deviation 9.61244
Minimum -30.00
Maximum 25.00
Range 55.00
Interquartile Range 11.00
Skewness 2.055 .150
Kurtosis 5.751 .298
Student wants Extroversion in lecturers Mean 12.9057 .41344
95% Confidence Interval for Mean Lower Bound 12.0916
Upper Bound 13.7197
5% Trimmed Mean 12.9235
Median 13.0000
Variance 45.298
Std. Deviation 6.73037
Minimum -5.00
Maximum 28.00
Range 33.00
Interquartile Range 9.50
Skewness -.003 .150
Kurtosis -.286 .298
Student wants Openness in lecturers Mean 8.0189 .49354
95% Confidence Interval for Mean Lower Bound 7.0471
Upper Bound 8.9906
5% Trimmed Mean 7.9843
Median 8.0000
Variance 64.549
Std. Deviation 8.03423
Minimum -15.00
Maximum 30.00
Range 45.00
Interquartile Range 11.00
Skewness .112 .150
Kurtosis -.041 .298
Student wants Agreeableness in lecturers Mean 7.6302 .58502
95% Confidence Interval for Mean Lower Bound 6.4783
Upper Bound 8.7821
5% Trimmed Mean 7.6268
Median 7.0000
Variance 90.696
Std. Deviation 9.52345
Minimum -19.00
Maximum 29.00
Range 48.00
Interquartile Range 13.50
Skewness .017 .150
Kurtosis -.420 .298
Student wants Conscientiousness in lecturers Mean 16.8792 .47158
95% Confidence Interval for Mean Lower Bound 15.9507
Upper Bound 17.8078
5% Trimmed Mean 17.1792
Median 17.0000
Variance 58.932
Std. Deviation 7.67674
Minimum -8.00
Maximum 30.00
Range 38.00
Interquartile Range 11.00
Skewness -.585 .150
Kurtosis .139 .298
The kurtosis and skewness are used in the descriptive analysis to show standard error for the z-score. For example, the skewness and kurtosis of Student Extroversion and Student Neuroticism are represented by -0.018/-0.98 and 1.054/-0.896 (Aljandali, 2017). This test shows that the statistical outcomes of Student Extroversion and Student Neuroticism are within the assumed normality of 1.96.

Exploratory Data Analysis on All Variables
Missing data makes the standard statistical procedures to be incomplete in the analysis process. The sample size of the target population can be reduced to increase statistical power with missing data (Hayes & Montoya, 2017). The amputation methods is also an effective strategy to protect the precision of confidence in the research process. The amputation method helps in reaching conventional approaches in the summary of outcomes.
Correlation Test Analysis
Correlations
Student Extroversion Student wants Extroversion in lecturers
Student Extroversion Pearson Correlation 1 .153*
Sig. (2-tailed) .010
N 418 281
Student wants Extroversion in lecturers Pearson Correlation .153* 1
Sig. (2-tailed) .010
N 281 283
*. Correlation is significant at the 0.05 level (2-tailed).

Correlations
Student Agreeableness Student wants Agreeableness in lecturers
Student Agreeableness Pearson Correlation 1 .164**
Sig. (2-tailed) .001
N 413 405
Student wants Agreeableness in lecturers Pearson Correlation .164** 1
Sig. (2-tailed) .001
N 405 417
**. Correlation is significant at the 0.01 level (2-tailed).

The correlation test involves a 2-tailed analysis of variables. The test on the association between student extraversion and students wants extroversion in lecturers was conducted. The Pearson correlation test shows that the level of significance is 0.01 and the correlation of 0.153. This means that the relationship between the student’s extroversion and students wants lecturer extroversion is significant. The correction tests on student’s agreeableness, and students want agreeableness was 0.164 with a level of significant 0.001. The standard level of significance is 0.01. This means that there is an association between student agreeableness and agreeableness in lectures.

Regression Test Analysis

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .153a .023 .020 6.54986
a. Predictors: (Constant), Student wants Extroversion in lecturers

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 286.891 1 286.891 6.687 .010b
Residual 11969.287 279 42.901
Total 12256.178 280
a. Dependent Variable: Student Extroversion
b. Predictors: (Constant), Student wants Extroversion in lecturers

The simple regression between independent variable and dependent has an R-value of 0.153. This indicates a medium degree of correlation between student extroversion and the student who wants extroversion in the lecturer. The significant difference in the level of correlation has a p-value of 0.01, which is less than 0.05. This means that the regression model statistically significantly predicts the level of association for outcome variables. These outcomes do differ from the correlation above.

Multiple Regression Analysis

Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .168a .028 .018 6.82934
a. Predictors: (Constant), Student Extroversion, Gender, Age

ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 373.952 3 124.651 2.673 .048b
Residual 12872.615 276 46.640
Total 13246.568 279
a. Dependent Variable: Student wants Extroversion in lecturers
b. Predictors: (Constant), Student Extroversion, Gender, Age

The score of students who wants extroversion in lecturers is associated with age, gender, and student extroversion. If p < 0.05, we can say that the coefficient of independent variables is statistically significant (Hayes & Montoya, 2017). The p-value is equaled to 0.048, which is less than the standard value of p. We can conclude that the outcomes of student want extroversion in lecturers scores are predicted by a variable such as age, gender, and student extroversion. These outcomes differ from the correlation above. Part B Pearson Correlation Coefficient – The test can be used to assess the association between weight and height of diabetic patients. These two variables can be measured using a nominal level of measure because they are continuous variables. The R-value provides a correlation between the correlation variable (Šušić, 2018). The simple correction between variables has R = 0.857 for Pearson correlation. Mock r value Model R R Square Adjusted R Square Std. Error of the Estimate 1 0.857a .013 .040 7.4431 a. Predictors: (Constant), Height, and Weight References Aljandali, A. (2017). Multivariate methods and forecasting with IBM® SPSS® Statistics. New York, NY: Springer. Hayes, A. F., & Montoya, A. K. (2017). A tutorial on testing, visualizing, and probing an interaction involving a multicategorical variable in linear regression analysis. Communication Methods and Measures, 11(1), 1-30. Šušić, M. (2018). Research the impact of fdi on some macroeconomic indicators in Bosnia and Herzegovina by using of program IBM SPSS Statistics. Journal of Process Management. New Technologies, 6(3), 13-24. Exploratory Data Analysis on All Variables

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