SPSS Data Analysis
Does the number of average study hours per week during the semester accurately predict final exam grades?

Independent variable: average number of study hours per week.

Hours is continuous data because it can take on any value below 168 hours, which is the number of hours in a week. Even though the data is reported in integer form the 'hours' data is continuous.

Hours data is quantitative, since it can be summed, divided, and multiplied.

Hours data is ratio data because it has a natural zero point. For example, the range of hours that a student can theoretically study per week is between zero and 168 hours.

Hours is a predictor variable, because it is believed/assumed to influence the outcome of the study.

Dependent variable: Final exam score.

Final exam scores is a discrete variable as reported, although it could be a continuous variable with a value anywhere between zero and 100.

Exam scores are quantitative,….

SPSS
Study Description:

List the research question for the study. The researcher is interested in looking at whether or not an appraisal from a person's manager regarding their job performance affects the person's self-esteem as measures on a self-esteem appraisal survey (SEM). According to the vignette while the researcher has managers give either a positive rating or a negative rating; he/she has no pre-experimental hypotheses regarding of how these specific appraisals will affect self-esteem (other than appraisals may affect self-esteem) and therefore the directionality of positive or negative ratings does not appear to be important in terms of the hypothesis.

Ma = Mb (Here the researcher is interested in the change on a measure of self-esteem from pre-to post intervention). Note to Customer: I'm using M. To represent mean here; it would also be correct to use the variance of the groups).

H1: Ma ? Mb

Variables. The independent variable is the feedback from the….

Over 250 respondents reported working 40 hours, with the next highest frequency being under 100.
Number of Siblings

The histogram for the number of siblings shows a negatively skewed data set, with more participants reporting fewer siblings. However, the range in this variable was quite high, ranging from 0 to 22 siblings. The mean response was 3.71 siblings, the median response was 3 siblings and the mode of the sample was 2, indicating that the most frequent response was 2 siblings. In this case the mode would likely be a good representation of this sample, as it represents the majority. The mean could also be used as a way of representing the number of participants who did have more than 2 participants, but outliers, such as the individuals reporting more than 15 siblings, heavily influence the mean. After the scale reaches approximately 10 siblings, very few individuals endorse responses higher, and….

Overall, it appears that the relationships between these variables are somewhat similar between men and women, although there are slight differences, most keenly pointed out in the ANOVA results.
Correlations

espondent's Sex

Age of espondent

Highest Year of School Completed

Total Family Income

Job Satisfaction

Male

Age of espondent

Pearson Correlation

1

-.240**

-.065

-.125**

Sig. (2-tailed)

.000

.103

.005

N

Highest Year of School Completed

Pearson Correlation

-.240**

1

.419**

-.042

Sig. (2-tailed)

.000

.000

.350

N

Total Family Income

Pearson Correlation

-.065

.419**

1

-.114*

Sig. (2-tailed)

.103

.000

.012

N

Job Satisfaction

Pearson Correlation

-.125**

-.042

-.114*

1

Sig. (2-tailed)

.005

.350

.012

N

Female

Age of espondent

Pearson Correlation

1

-.275**

-.115**

-.123**

Sig. (2-tailed)

.000

.001

.002

N

Highest Year of School Completed

Pearson Correlation

-.275**

1

.459**

-.093*

Sig. (2-tailed)

.000

.000

.018

N

Total Family Income

Pearson Correlation

-.115**

.459**

1

-.196**

Sig. (2-tailed)

.001

.000

.000

N

Job Satisfaction

Pearson Correlation

-.123**

-.093*

-.196**

1

Sig. (2-tailed)

.002

.018

.000

N

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Correlations

Age of espondent

Highest Year of School Completed

Total Family Income

Job Satisfaction

Age of espondent

Pearson Correlation

1

-.259**

-.099**

-.124**

Sig. (2-tailed)

.000

.000

.000

N

Highest Year of School Completed

Pearson Correlation

-.259**

1

.437**

-.068*

Sig. (2-tailed)

.000

.000

.022

N

Total Family Income

Pearson Correlation

-.099**

.437**

1

-.160**

Sig. (2-tailed)

.000

.000

.000

N

Job Satisfaction

Pearson Correlation

-.124**

-.068*

-.160**

1

Sig. (2-tailed)

.000

.022

.000

N

**. Correlation is significant at the 0.01 level (2-tailed).

*. Correlation is significant at the 0.05 level (2-tailed).

Case Processing Summary

Cases

Included

Excluded

Total

N

Percent

N

Percent

N

Percent

Highest Year of School Completed * Job Satisfaction

76.1%

23.9%

Age of espondent * Job Satisfaction

76.0%

24.0%

Total Family Income….

Look at the variables that show the change in the percentage of schools meeting or exceeding state standards (mathch94, readch94, and scich94). Test the hypothesis that the true change in the percentage meeting state standards is 0. Write a short report to the mayor detailing your findings.
eading improved but not math and science.

Assignment #7c:

Use Assignment 7c -- Tutorial

Problem 8 ?" Chapter 13:

Look at the changes between 1993 and 1994 in graduation rates (variables grad93 and grad94), ACT scores (variables act93 and pctact94). Does it look like the Chicago school system is improving? Which schools appear to be "outliers"?

Assignment #7d:

Use Assignment 7d?"Tutorial

Problem 1 ?" Chapter 14:

Perform the appropriate analyses to test whether the average number of hours of daily television viewing (variable tvhours) is the same for men and women. Write a short summary of your results, including appropriate charts to illustrate your findings. Be sure to look at the….

SPSS Exercise
Each problem below describes a different research question. For each problem, you will state the null and alternative hypotheses, determine which statistical test is appropriate to answer each question, run the analysis using SPSS and the accompanying data set from Blackboard, and then draw a conclusion based on the results of the analysis. Keep all of your SPSS output from running each analysis.

A researcher wishes to assess whether vitamin C is effective in the treatment of colds. To evaluate her hypothesis, she decides to conduct a 2-year experimental study. She obtains 30 volunteers from undergraduate classes to participate. She randomly assigns an equal number of students to three groups: placebo (group 1), low dose of vitamin C (group 2) and high dose of vitamin C (group 3). In the first and second years of the study, students in all three groups are monitored to assess the number of days….

401
Question 11D

1. What are the null and alternative hypotheses?

Null Hypothesis: Volume has no relation to defect rate (the slope is equal to 0).

Alternative Hypothesis: As volume increase, defect rate increases. (the slope is not equal to 0).

2. What is the population of interest? What is the sample?

All shifts at the plant in question make up the population of interest.

160 randomly selected shifts make up the sample.

3. On the basis of the output, what can you conclude about the null hypothesis?

The null hypothesis can be rejected. There is a significant linear regression between volume and defect rate and the slope is not equal to 0.

4. Can you reject the null hypothesis that the slope is 0?

Yes. The scatter plot shows a linear relationship and the regression coefficient is .740. The value of t is 13.846, indicating that the slope is 13.846 standard error units above a slope of 0, which has….

2%, female 5.1%
CKEVE - male 13.9%, female 8.6%

HEEVE -- male .9%, female, .5%

5

H1: Females are more likely than males to have ever smoked a cigarette.

This hypothesis is not supported by the data. In this sample, 34.7% of those who had ever tried a cigarette were male, while 32.3% of those who had ever tried a cigarette were female.

H2: Males are more likely than females to have ever used cocaine.

This hypothesis is supported by the data. Of those who had ever tried cocaine, 13.9% were male and 8.6% were female.

6

For all of the drugs included in this survey, males made up a larger percentage than females in the group that reported having used each drug. Given that the sample has slightly more females than males overall, this finding emphasizes that men are more likely than women to have tried a wide range or drugs. The percentages were fairly close between the….

Crosstabs would be used to test this association. There is a significant correlation between the education of a mother and a daughter (r = .474, p < .01).
e. Does Belief in Life After Death impact one's happiness in their marriage?

Belief in life after death would be the Independent Variable and Happiness in one's marriage would be the Dependent Variable. Crosstabs would be used to test this association. 83.8% of people who report being very happy in their marriage believe in life after death. 66.7% of people who report being not very happy with their marriage believe in life after death.

Part 3

a. How many people without a high school diploma find life exciting?

66

b. What percentage of people without a high school diploma find life exciting?

35.5%

c. Of the people who find life exciting, what percentage do not have a high school diploma?

14%

d. If degree is the column variable and life is….

86
34.00

additive 2

9

11.33

Total

16

Test Statisticsb

gas mileage

Mann-Whitney U

6.000

Wilcoxon W

34.000

Z

-2.701

Asymp. Sig. (2-tailed)

.007

Exact Sig. [2*(1-tailed Sig.)]

.005a

a. Not corrected for ties.

b. Grouping Variable: fuel additives (per m)

P (1)

0.004

P (2)

0.008

With the two P. values being so far apart, as well as the variance of the two groups being of significant value, around 2 whole values, it is clear that there is a significant difference to be noted between the two sample groups. Through the analysis of both the variance and the computations worked out through the Mann-Whitney test, it is clear that Sample B. has a higher rate of miles per gallon than the vehicles tested in Sample a. Here, the significant difference can then be interpreted that the fuel additive used within the context of Sample B. is more effective in terms of increased mileage within its test vehicles.

B. Exercise and Calories Burnt

Data Table

Swimming

Tennis

Cycling

Data Rank

Rank a

Rank B

Rank C

8

9

5

4

14

1

11

13

3

6

10

7

12

15

2

Data Set

Sum

2040

Mean

Variance

Rank Sum

41

61

18

Rank Mean

8.2

12.2

3.6

Combined Sum

Combined Median of Ranks

8

Three separate….

244, p = .000. Men had an average rank of 852.94 hours, while women had an average rang of 632.24 hours, indicating that on average, women worked fewer hours than men, in this sample.
4. Using a nonparametric test to see whether current salaries (variable salnow) for clerical employees differ for the four gender/race groups (variable sex/race). Compare your results from those from a parametric analysis. Summarize the conclusion.

A Kruskal-Wallis test was conducted to evaluate whether current salaries for clerical employees are equal between four groups: white males, minority males, white females and minority females. The results of the test indicate that the groups are significantly different from one another, X2 (3, 474) = 175.068, p = .000. The rank output indicates that white males had the highest average salary, followed by minority males, white females and finally minority females.

A one-way anova was conducted to examine the same question for the….

SPSS Output Interpretation
SPSS Output Intrpretation

SPSS Output Summary-Use the study information and SPSS output file provided to answer the questions listed.

Study Description: • Participants were assigned to a control group or a training group. The training group received 1 hour of training every day for one week. During the training, participants learned various techniques for decreasing their stress levels. All participants were given a life stress test to determine their current level of life stress. Scores on the test were labeled as either high or low. After a week, all participants were given a puzzle to put together in a stressful environment (e.g., loud noises, bright lights, etc.). The amount of the puzzle completed in the time frame of 5 minutes was measured according to the number of pieces completed (there were 10 pieces to the puzzle). It was expected that the training group would have different scores on the test….

SPSS
How did you treat missing or oddly coded data, and outliers?

The SPSS function for Missing Data was used to identify any outliers or oddly coded data. The percent of missing data is high at 47.2% or 1063 potential responses to the question. The number of extremes or outliers in the high range is 220. Because the numbers are high, it would be useful to look at the raw data to determine how these answers were coded. This question is an interesting one and the pattern of responses suggests that additional analysis with this variable could reveal relationships with other factors.

What did you visually observe about your variables?

esponses to some of the questions are clustered for a number of the survey respondents. That is to say that there appear to be some respondents in the sample who are very active users of their cell phones and other respondents who barely….

statistical test (SPSS Output) is effective as far as it goes in this particular case, but the results are not as clear as what they could be. A number of variables were used to generate results that on the surface were easily defined but a more analytical approach shows that the results could be much more comprehensive. The variables included three groups; low, medium and high scores depending on the respondent's answers to 10 questions. Additional variables included the gender of the respondent as well as the number of respondents in each category. Other variables could include the questions themselves and how the questions were written. Since there were only ten total questions, the validity and reliability of the questionnaire are two unspoken variables that should have been addressed.
SPSS serves a primary purpose of measuring the output from the respondents question, but since they questions were seeking qualitative responses,….

P oftware
The study chooses the Age Category as the categorical variable and Lived Poverty Index as the metric variable. The study uses P software Version 21 for the analysis and presents the frequency distributions for the two variables. The visual P output of both the Age Category and Lived Poverty Index are as follows:

FREQUENCIE VARIABLE=AGE_COND

/ORDER=ANALYI.

Frequencies

[Dataet1] C:UsersDELL 3521DownloadsAfrobarometer__student_8210_.sav

Age Category

Valid

Missing

Age Category

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

51 and above

Total

Missing

Total

The paper uses the frequency distribution to summary the age category in a manageable form. The frequency distribution reveals that the age groups are between 18 and over 51 years. The number of participants between 15 and 35 years of age are 27,888, which are 54.1% of the participants. However, participants between 36 and 50 years of age are 13,868(26.9%). The sample population who are 51 years and above are 9,819 (19%). The total participants are 51,573 (100%). The frequency distribution table also presents the cumulative frequency….