Analyze data through ( IBM SPSS statistics program)

please do this work by using ( IBM SPSS statistics program)

Data file download attached

Now, here are the things I want you to provide:

1. Correlation Table and Means and Standard Deviations. This is super easy—I’ve already done the work of cleaning and coding the data for you. All you’ve got to do is go to Analyze➡ Correlate➡ Bivariate. Make sure under options, you check the box for means and standard deviations. Run it.

2. Answer a few questions:

a. What is the correlation between optimism and extraversion? Is it statistically significant?

b. What is the correlation between neuroticism and self-esteem ? Is it statistically significant?

c. What is the correlation between creativity and proactive personality? Is it statistically significant?

d. What is the correlation between creativity and creativity? (not a typo—you’ll need to think conceptually for this one.)

Is it statistically significant?

e. What variables have a negative correlation? (name some; all are not necessary)

f. What variables is proactive personality significantly correlated with?

3. Next, I want you to compare my sections of MGMT 305—which are labeled 1 and 2. Go to Analyze ➡ Compare Means➡ Means, and put all the variables EXCEPT for Section in the Dependent List. Put Section in the Layer Box below. Run it, and then answer the following questions.

a. On average, which section of my class is most extraverted? How do you know?

b. Which section has the lower level, on average, of proactive personality? How do you know?

c. Which section has the higher level, on average—of achievement striving? How do you know?

4. Next, I’d like you to think about a theory and hypothesis—specifically, I’d like you to think about what variables might be related to other variables. (We will mostly ignore questions of time and causality for this exercise.) To test your idea, you’ll use very simple linear regression. I want you to have one dependent variable and three independent variables in your model. Independent variables are what predict, dependent variables are what is predicted. For example, in the hypothesis more money is positively associated with more problems, I could say that money is the independent variable, and problems are the dependent variable, suggesting that more money is leading to more problems, and not the other way around.

a. Write your theory/hypothesis—what do you think relationships will be, and why?

b. To test your idea, go to Analyze➡ Regression➡ Linear.

Put in the dependent variable box one variable that you think can be predicted.

And put into the independent box at least three other variables. You can put more if you choose.

Once everything is in, click OK. Now that you have output, look under the table called “Coefficients.”

c. Looking at the table, answer two questions:

i. First, was your hypothesis supported? (A helpful way to figure this out is if under “Sig.

” which means “significance,” the number is less than .05. Less than .05 is significant,

a number greater than .05 means nonsignificant.)

ii. Second, which one of your independent variables had the biggest effect on your dependent variable?

You can figure this out by looking in the Coefficients table under “Standardized Coefficients Beta.”

Whichever one has the largest absolute value has the biggest effect. What is the effect size?

iii. Which of your independent variables has the smallest effect? What is the effect size?

5. Last, a general question: after looking at all of the data you’ve analyzed, write two or three general conclusions at which you can arrive—and explain how.

answers to the questions above

Skills Required

Data Processing

Statistics

R Programming Language

Statistical Analysis

SPSS Statistics