Perform a correlational analysis on the following variables.

1)For this assignment, you will submit a total of three files: two SPSS data files and one MS Word document. Complete the following: Create a data file in SPSS and enter the data presented in the table located in section 3.5 of chapter 3 in your textbook. Save this SPSS data file. Create a mock research project. Respond to the following three questions. Considering your area of research interest, briefly state your area and a possible research project related to the area (150-500 words) Pose one or more null and alternative hypotheses that follow from the possible research project.

List at least 10 variables that would be collected in your mock research project that would be used to answer the hypotheses. After each variable list the variable name you will use in SPSS (Section C), the level of measurement (binary, nominal, ordinal, interval, or ratio), and the possible range of scores. Feel free to be creative. Create a mock SPSS data set. Open a data file in SPSS and enter in a set of mock data for the research project you describe in Part B. (Note: It is important that you do not collect real data for this activity; you cannot collect data without IRB approval). You must enter 10 rows of data for each of the 10 variables (that is, create data for 10 mock participants). Each row represents the scores of each mock participant on the ten variables. Participant #1 must have missing data for Variable #3. Ensure this is coded correctly.

2)  For this assignment, you will submit one MS Word document. Create this document by cutting and pasting SPSS output into your document. Complete the following: Part A In Part A, you will be creating visual displays of data. You will copy and paste output you created while working in Chapter 4 into an MS Word document. Please read the instructions below to ensure you are pasting the correct material into your document (In this chapter, you create many charts but not all are required for this assignment). Using the data set: DownloadFestival.sav, create a boxplot for males and females for the variable Day1. It is important that you change the outlier identified to 2.02 prior to creating the boxplot.

Be sure to save the data set with a new name, indicating it is the corrected data set (outlier identified and corrected). Save this boxplot, with an appropriate title in your document. Using the data set: ChickFlick.sav, create a simple bar chart for independent means. The variables you will use are: Arousal, Film, and Gender (grouping variable). Be sure to display error bars and save your chart with an appropriate title in your document. Using the data set: Hiccups.sav, create a clustered bar chart for related means. The variables you will use are: Baseline, Tongue Pulling, Carotid Artery Massage, Digital Rectal Massage. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your document. Using the data set: Text Messages.sav (note: you may see an additional data set with the same name: TextMessages.sav – either will create the correct output), create a clustered bar chart for mixed designs. The variables you will use are: Time1, Time2, and Group. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your document. Using the data set: Exam Anxiety.sav, create a scatterplot that includes a regression line. The variables you will use are: Exam Performance and Exam Anxiety. Be sure to include the regression line and save your chart with an appropriate title in your document. Part B Why Exploratory Data Analysis? Write a short paragraph that highlights your understanding of why exploratory data analysis is a critical part of any analytical strategy (500 Word limit). To receive full credit for this assignment, you must show a high level of understanding the importance of exploring data visually.

3)    Create an MS Word document by cutting and pasting SPSS output into your document. Complete the following: Why do we care whether the assumptions required for statistical tests are met? (You might also want to write your answer on a note card you paste to your computer.) Open the data set that you corrected previously for DownloadFestival.sav. You will use the following variables: Day1, Day2, and Day3 (hygiene variable for all three days). Create a simple histogram for each variable. Choose to display the normal curve (under Element Properties) and title your charts. Copy these plots into your document. Now create probability-probability (p-p) plots for each variable. This output will give you additional information. Read over the Case Processing Summary. Notice that there is missing data for Days 2 and Day 3? Copy only the Normal p-p Plots into your document (you do not need to copy the beginning output nor the Detrended Normal p-p Plots).

Examining the histograms and p-p plots describe the dataset, with particular attention toward the assumption of normality. For each day, do you think the responses are reasonably normally distributed? (Just give your impression of the data.) Why or why not? Using the same dataset, and the Frequency command, calculate the standard descriptive measures (mean, median, mode, standard deviation, variance and range) as well as kurtosis and skew for all three hygiene variables. Paste your output into your document (you do not need to paste the Frequency Table). What does the output tell you? You will need to comment on: sample size, measures of central tendency and dispersion and well as kurtosis and skewness. You will need to either calculate z scores for skewness and kurtosis or use those given in the book to provide a complete answer. Bottom line: is the assumption of normality met for these three variables? Does this match your visual observations from question #1? Using the dataset SPSSExam.sav, and the Frequency command, calculate: the standard descriptive statistics (mean, median, mode, standard deviation, variance and range) plus skew and kurtosis, and histograms with the normal curve on the following variables: Computer, Exam, Lecture, and Numeracy for the entire dataset. Complete the same analysis using University as a grouping variable. Paste your output into your document (you do not need to paste the Frequency Table). What do the results tell you with regard to whether the data is normally distributed? Using the dataset SPSSExam.sav, determine whether the scores on computer literacy and percentage of lectures attended (with University as a grouping variable) meet the assumption of homogeneity of variance (use Levene’s test). You must remember to unclick the “split file” option used above before doing this test. What does the output tell you? (Be as specific as possible.)

Describe the assumptions of normality and homogeneity of variance. When these assumptions are violated, what are your options? Are there cases in which the assumptions may technically be violated, yet have no impact on your intended analyses? Explain.

4)   Create an MS Word document by cutting and pasting SPSS output into your document. Please answer the questions first and include all output at the end of the assignment in an Appendix. Name the file in the following format: lastnamefirstinitialEDU8006-3.doc (example: smithbEDU8006-3.doc). Complete the following: Part A In Part A, 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).

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. 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 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?

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