Project 1:Examine the data for errors

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The following small dataset is from a study conducted within a single middle school. Fundamentally, this study is a comparison of the differences between male and female teachers in personal Confidence Scores and was conducted to determine if a relationship exists between the number of Years of Experience and Confidence Scores. As the researcher, you want to examine if years of experience could be used as a predictor of confidence scores and if gender could also impact confidence scores.

Sex

Years of Experience

Confidence Scores

Male

15

110

Male

3

117

Female

12

118

Male

8

120

Female

23

104

Female

9

100

Male

37

107

Male

14

115

Male

10

114

Female

4

115

Female

11

115

Male

1

100

Female

3

117

Female

7

115

Male

2

103

Female

21

125

Male

28

115

Female

9

115

Male

5

110

Female

3

110

Table 1. Small Dataset

Please open the SPSS file confidence scores.Once you have opened the file, follow the instructions below. As a statistician, it is your job to make sure that there are no special circumstances with the data that could lead to confusing or misleading results. Specifically, the data used for analysis are equal to the data collected.

Steps to examine the data for accuracy:

1.Identify the level of measurement of each of the three variables.

2.Identify the dependent and independent variables.

3.Use SPSS to conduct the necessary descriptive statistics on each of the variables based on their level of measurement (e.g., frequency distribution, mean, median, mode, standard deviation, standard error, boxplots, etc. Hint: Not all of the three variables require the same descriptive procedures).

4.After examining the data with descriptive statistics, did you find any issues with the data?

5.If your answer was yes, what procedures will you do as a researcher to make sure that the data in SPSS are the same as the data collected in the original spreadsheet? (Hint: You don't need any calculations to do this.)

6.Once you have addressed the issues with the SPSS dataset, please repeat the necessary descriptive statistics on each of the variables based on their level of measurement (e.g., frequency distribution, mean, median, mode, standard deviation, standard error, boxplots, etc. Hint: Not all of the three variables require the same descriptive procedures.).

Project 2

The following small dataset is from a study conducted within a single middle school. Fundamentally, this study is a comparison of the differences between male and female teachers in personal Confidence Scores and was conducted to determine if a relationship exists between the number of Years of Experience and Confidence Scores. The researcher wants to examine if the variables years of experience and gender would be significant predictors of confidence scores.

Sex

Years of Experience

Confidence Scores

Male

15

110

Male

3

117

Female

12

118

Male

8

120

Female

23

104

Female

9

100

Male

37

107

Male

14

115

Male

10

114

Female

4

115

Female

11

115

Male

1

100

Female

3

117

Female

7

115

Male

2

103

Female

21

125

Male

28

115

Female

9

115

Male

5

110

Female

3

110

Table 2. Small Dataset

1.What is hypothesis testing?

2.Discuss the statement "The sample must be representative of the population." In your discussion, be sure to include the concepts of generalizability and sampling procedures.

3.In hypothesis testing, there are some errors that could be committed if the wrong decision is made regarding the null hypothesis. These errors are Type I and Type II errors. Please describe each one of them, and provide an example when you are committing a Type I and/or a Type II error.

4.Discuss each of the three main assumptions for parametric statistics. Include in your discussion which of the variables will be tested for the assumptions, the independent, or dependent variable.

a.Describe and explain the assumption of normality. How could you test for this assumption?

b.Describe and explain the assumption of independence.

c.Provide an example of a statistical analysis where homogeneity of variance is important.

Open the file used in Week 1 (data containing confidence scores and years of experience), or type the data provided in the table above into SPSS. Once the dataset is open or the data are typed, please proceed to address items 8 and 9.

5.Conduct the appropriate test (Kolmogorov-Smirnov or Shapiro-Wilk's) for normality, and provide the results, and a brief discussion of the results below (test used, test results, p-value, etc.). Be sure to discuss if the assumption of normality was met or violated.

6.Please conduct the test homogeneity of variance, and provide the results, and a brief discussion of the results below (test used, test results, p-value, etc.). Make sure to discuss if the assumption of normality was met or violated

Project 3

Part 1: Correlation and Regression Analysis

Please open the file name personality.sav. In this exercise, you will focus on the relationship of two variables, beckdep (a measure of depression), and emcontot (measure of emotional control using the Courtauld Emotional Control Scale [CECS]). Two instruments used in psychology are the Beck Depression Inventory, which is used to measure characteristics related to depression; and the CECS, which is used to measure subjective control of feelings of depression, anger, and anxiety in uncomfortable situations; this is a self-description instrument.

Scenario:

You are interested in assessing the relationship between these two instruments, with the goal of using the emcontot scores as a predictor of the beckdep scores.

1.In SPSS, calculate the Pearson's correlation between beckdep and emcontot.

a.What are the null and alternative hypotheses?

b.Present a scatterplot with beckdep scores in the Y-axis, and emcontot in the X-axis.

c.What type of relationship is depicted in the scatterplot, positive or negative?

d.Conduct the correlation analysis using the correlate menu in SPSS.

e.Report the results of the correlation analysis.

f.What was the amount of variance shared by the two variables?

g.What is your decision regarding the null hypothesis based on the results of the correlation analysis?

2.In SPSS, develop a regression model between the variables emcontot and beckdep.

1.

a.Which of these variables is the dependent (predicted) variable?

b.Conduct the regression analysis using the regression menu in SPSS.

c.What is the mean and standard deviation for emcontot and beckdep?

d.What is the R-value? Is the value similar to the correlation coefficient conducted above? If so, why do you think it is similar?

e.What is the value of the R square? What does that value tell you about the amount of variance predicted by emcomtot on beckdep?

f.Was the model significant? How could you determine this?

g.Report the results of the regression in the APA format.

h.Develop the regression equation for the predicted variable

i.Using the Regression equation developed above, please estimate the Beck Depression Inventory score for an individual who scores 2.50 in the emcontot (CECS).

Part 2: t-test Analyses

Scenario

You are now interested in examining differences in the Beck Depression Inventory score exist between individuals who are married and not married. In order to determine if differences exist, you will use the variable name marital as the independent variable, and beckdep as the dependent variable. Once you examine the variable marital, you realize that the variable included six different categories. Therefore, the variable needs to be recoded in two categories, single (including separated, divorce, or widowed) and married; because the independent samples t-test is used to compare differences between two groups.

In SPSS, conduct an independent samples t-test:

1.Conduct the independent samples t-test, including the assumptions tests.

2.There are three main assumptions needing to be satisfied before using the independent-samples t-test for testing differences between the genders. Use SPSS to generate the output needed to test the assumptions. Please discuss each one and explain whether each has been met using SPSS output as needed to include the Shapiro-Wilk test for normality (used when the sample size is less than 50), histograms, and the Levene's test. Remember, if the population value is unknown, it is permissible to infer from sample values. Regardless of sample size, test whether these assumptions are met.

3.What are the null and alternative hypotheses?

4.What is the mean of the Beck Depression Inventory for the single and married groups?

5.What is the value of t?

6.What is the associated probability?

7.Report the results in APA format.

8.What might be concluded from this hypothetical study? (Hint: The decision about the null hypothesis.)

Project 4

Scenario

A fashion design professor was interested in developing a regression model to predict the salary of the models. The data file name is Supermodel.sav. There were 231 models included in the data collection. The questions asked to each one of the models were current income per day (Salary), age (Age), their years of experience modeling (Years), an attractiveness rating (Beauty).

1.Assumptions of the multiple linear regression analysis.

a.What are the assumptions of the multiple linear regression analysis? In one or two sentences briefly describe each one of them.

b.What is multicollinearity?

c.How could the researcher examine for multicollinearity?

2.Open the Supermodel.sav file, and conduct a multiple linear regression.

a.Which is your dependent or predicted variable?

b.Which are your independent or predictor variables?

c.Conduct a correlation analysis including all of the predictors (independent variables).

d.What are the correlations between each pair of correlations?

e.Can you determine if all of the variables should be included in the regression analysis? (Hint: Examine for multicollinearity.)

f.Which variables would you include in the regression analysis?

g.Please conduct a multiple regression analysis.

h.What is the R-value of the model?

i.What are the R²-values?

j.What is the meaning of the R²-value in regression analysis?

k.Was the model significant? How could you determine significance in a regression model?

l.Which predictor(s) were significant in the model?

m.Now conduct another regression analysis, changing the variables that were highly correlated with one another.

n.Were the results the same? Which one is a better predictor? (Hint: R² of the model).

o.Develop the regression equation using the values from the coefficients table in SPSS.

p.Based on your equation, what would be the salary of a model