PSY-380 Introduction to Probability and Statistics Project 3 – SPSS or Excel Analyses
In SPSS or Excel, run the correct statistical test for each study.
- A researcher is interested to learn if there is a linear relationship between the hours in a week spent exercising and a person’s life satisfaction. The researchers collected the following data from a random sample, which included the number of hours spent exercising in a week and a ranking of life satisfaction from 1 to 10 (1 being the lowest and 10 the highest).
| Participant |
Hours of Exercise |
Life Satisfaction |
| 1 |
3 |
1 |
| 2 |
14 |
2 |
| 3 |
14 |
4 |
| 4 |
14 |
4 |
| 5 |
3 |
10 |
| 6 |
5 |
5 |
| 7 |
10 |
3 |
| 8 |
11 |
4 |
| 9 |
8 |
8 |
| 10 |
7 |
4 |
| 11 |
6 |
9 |
| 12 |
11 |
5 |
| 13 |
6 |
4 |
| 14 |
11 |
10 |
| 15 |
8 |
4 |
| 16 |
15 |
7 |
| 17 |
8 |
4 |
| 18 |
8 |
5 |
| 19 |
10 |
4 |
| 20 |
5 |
4 |
- Run descriptive statistics on the data. Report the mean, variance and standard deviation for the data of hours of exercise per week by the participants. Copy and paste the SPSS or Excel output into the word document.
- Run a bivariate correlation to determine if there is a linear relationship between the hours of exercise per week and life satisfaction. In 3-4 sentences interpret the results in words, then write the statistical results in APA style. Report the Pearson’s Correlation and probability statistic.
- Insomnia has become an epidemic in the United States. Much research has been done in the development of new pharmaceuticals to aide those who suffer from insomnia. Alternatives to pharmaceuticals are being sought by sufferers. A new relaxation technique has been tested to see if it is effective in treating the disorder. Sixty insomnia sufferers between the ages of 18 to 40 with no underlying health conditions volunteered to participate in a clinical trial. They were randomly assigned to either receive relaxation treatment or a proven pharmaceutical treatment. Thirty were assigned to each group. The amount of time it took each of them to fall asleep was measured and recorded. The data is shown below.
- Run a two-independent samples t-test to determine if the relaxation treatment is more effective than the pharmaceutical treatment at a level of significance of 0.05. Copy and paste the SPSS or Excel output into the word document.
- In 3-4 sentences interpret the results in words, then write the statistical results in APA style. Report the t statistic and probability statistic.
| Relaxation |
Pharmaceutical |
| 98 |
20 |
| 117 |
35 |
| 51 |
130 |
| 28 |
83 |
| 65 |
157 |
| 107 |
138 |
| 88 |
49 |
| 90 |
142 |
| 105 |
157 |
| 73 |
39 |
| 44 |
46 |
| 53 |
194 |
| 20 |
94 |
| 50 |
95 |
| 92 |
161 |
| 112 |
154 |
| 71 |
75 |
| 96 |
57 |
| 86 |
34 |
| 92 |
118 |
| 75 |
41 |
| 41 |
145 |
| 102 |
148 |
| 24 |
117 |
| 96 |
177 |
| 108 |
119 |
| 102 |
186 |
| 35 |
22 |
| 46 |
61 |
| 74 |
75 |
- A researcher is interested to learn if the level of interaction a woman in her 20s has with her mother influences her life satisfaction ratings. Below is a list of women who fit into each of four levels of interaction. Conduct a One-way Between Subjects ANOVA on the data to determine if there are differences between groups; does the level of interaction influence women’s ratings of life satisfaction?
- Report the results of the One-way Between Subjects ANOVA. If significance is found, run the appropriate post-hoc test and report
between what levels the significant differences were found.
Copy and paste the SPSS or Excel output into the word document.
In 3-4 sentences interpret the results in words, then write the statistical results in APA style. Report the F statistic and probability statistic.
| No Interaction |
Low Interaction |
Moderate Interaction |
High Interaction |
| 2 |
3 |
3 |
9 |
| 4 |
3 |
10 |
10 |
| 4 |
5 |
2 |
8 |
| 4 |
1 |
1 |
5 |
| 7 |
2 |
2 |
8 |
| 8 |
2 |
3 |
4 |
| 1 |
7 |
10 |
9 |
| 1 |
8 |
8 |
4 |
| 8 |
6 |
4 |
1 |
| 4 |
5 |
3 |
8 |
Project 3 Analysis in Excel Instructions
Table of Contents
- Independent Samples t-test
- Repeated Measures t-test
- Single Sample t-test
- Pearson r Correlation
- One-Way Analysis of Variance (ANOVA)
- Independent Samples t-test
Objective: To determine if there is a statistically significant difference between two independent groups in behavioral science research.
Example: Comparing the average anxiety levels of participants who received a new therapy (Group A) versus those who received a placebo (Group B).
Steps:
- Prepare Your Data:
- Organize your data with two separate columns for Group A and Group B.
- Calculate Descriptive Statistics:
- Calculate the means and standard deviations for each group.
- Mean(Group A): =AVERAGE(GroupA)
- Mean(Group B): =AVERAGE(GroupB)
- Std Dev(Group A): =STDEV.P(GroupA)
- Std Dev(Group B): =STDEV.P(GroupB)
- Perform the t-test:
- In an empty cell, enter =T.TEST(GroupA, GroupB, 2, 2) to calculate the t-test result.
- Interpret Results:
- If p-value < 0.05, there is a statistically significant difference between the groups.
- Repeated Measures t-test
Objective: To compare means of the same group under different conditions in behavioral science research.
Example: Evaluating whether there is a significant difference in attention span before and after an intervention.
Steps:
- Prepare Your Data:
- Organize your data with two columns for “Before” and “After” measurements.
- Calculate Descriptive Statistics:
- Calculate means and standard deviations for both “Before” and “After” data.
- Perform the t-test:
- In an empty cell, enter =T.TEST(Before, After, 2, 2) to calculate the t-test result.
- Interpret Results:
- If p-value < .05, there is a statistically significant difference between the “Before” and “After” measurements.
- Single Sample t-test
Objective: To determine if the mean of a single sample significantly differs from a known population mean.
Example: Investigating if the average IQ score of a group is significantly different from the known population mean of 100.
Steps:
- Prepare Your Data:
- Organize your data in a single column.
- Calculate Descriptive Statistics:
- Calculate the sample mean and standard deviation.
- Mean: =AVERAGE(SampleData)
- Std Dev: =STDEV.P(SampleData)
- Perform the t-test:
- In an empty cell, enter =T.TEST(SampleData, 100, 1, 2) to calculate the t-test result.
- Interpret Results:
- If p-value < .05, the sample mean is significantly different from the known population mean.
- Pearson r Correlation
Objective: To assess the strength and direction of a linear relationship between two continuous variables.
Example: Examining the correlation between hours of study (X) and exam scores (Y).
Steps:
- Prepare Your Data:
- Organize your data with one column for variable X and another for variable Y.
- Calculate the Correlation:
- In an empty cell, enter =CORREL(X, Y) to calculate the Pearson correlation coefficient (r).
- Interpret Results:
- r > 0 indicates a positive correlation.
- r < 0 indicates a negative correlation.
- r close to 1 or -1 indicates a strong correlation.
- One-way Analysis of Variance (ANOVA)
Objective: To compare means of more than two independent groups in behavioral science research.
Example: Analyzing the effect of different teaching methods (Groups A, B, C) on student test scores.
Steps:
- Prepare Your Data:
- Organize your data with a column for each group.
- Perform the ANOVA:
- In an empty cell, enter =ANOVA(GroupA, GroupB, GroupC) to calculate the ANOVA result.
- Interpret Results:
- If p-value < .05, there is a statistically significant difference between at least two groups.
Related; PSY-380 Introduction to Probability and Statistics Benchmark – Project 2
Order This Paper
Reviews
There are no reviews yet.