In this Signature Assignment, you will use what you have learned in this course to conduct three statistical tests: a Pearson correlation, an independent t-test, and an ANOVA. You will use
Instructions
In this Signature Assignment, you will use what you have learned in this course to conduct three statistical tests: a Pearson correlation, an independent t-test, and an ANOVA.
You will use SPSS to conduct the statistical tests.
You will then prepare a PowerPoint presentation in which you share the results of each statistical test. Each slide should contain notes (at least 100 words) describing the analysis in narrative form.
Prepare the PowerPoint presentation and include a voice-over. For instructions on how to include a narration to a PowerPoint presentation, search for “how to create voice-over narration in MSWord PowerPoint.”
Use the attached SPSS file to complete the following:
Select variables appropriate to conduct each of the statistical tests below:
- Pearson Correlation for three variables
- Independent Samples t-test
- Recall from week 5 that the t-test compares means for two groups.
- ANOVA
- Recall from week 5 that the ANOVA test compares means for three or more groups.
PowerPoint
Introduction
Introduce the assignment by stating the importance of hypothesis testing in research and how statistics are used to accomplish this.
Present the statistical tests one at a time. For each statistical test:
- Identify the null and alternative hypothesis for the statistical test.
- Identify the Independent and Dependent variables, as appropriate. (Note: not applicable for the Pearson Correlation statistical test)
- Display appropriate graphics, and descriptive statistics for each of the variables in the statistical test. Describe the central tendency and dispersion of each variable in the slide notes using formal writing.
- Display the statistical output for the test.
a. Describe the statistical output in the slide notes using formal writing.
b. Reflect on the use and value of the statistical test.
Summary
Close the assignment with a formally written reflection on building a statistical mindset and developing statistical confidence.
Resources:
BUS-7105 Signature Assignment SPSS Data Set
Length: 12 to 15 slides not including title page and reference page
References: Include a minimum of 5 scholarly resources.
Your presentation should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University's Academic Integrity Policy.
SAGE Research Methods Video
ANOVA
Video Title: ANOVA
Originally Published: 2016
Publication Date: Sep. 30, 2016
Publishing Company: SAGE Publications, Inc.
City: Thousand Oaks, United States
ISBN: 9781506359144
DOI: https://dx.doi.org/10.4135/9781506359144
(c) SAGE Publications Inc., 2017
NARRATOR: Welcome to Practical Statistics for Nursing using SPSS. This video shows how to process the ANOVA test. You can watch the entire video, or use the time slider to navigate directly to any time point. The ANOVA test is similar to the t-test, but where is the t-test
NARRATOR [continued]: compares two groups of continuous variables to determine if one group statistically significantly outperformed the other. The ANOVA test can process three or more groups. In this example, we have three groups. The members of each group will be given an anti- hypertensive drug. The participants in Group 1 get Drug A, the participants
NARRATOR [continued]: in Group 2 get Drug B, and the participants in Group 3 get Drug C. After a month, we'll record a systolic blood pressure of each participant. Group 1 had a mean of 120.37. Group 2 had a mean of 122.37. And Group 3 had a mean of 122.70.
NARRATOR [continued]: The ANOVA test will compare the groups to each other and calculate a p- value for each pair of groups. If the p-value is less than or equal to 0.05, we would conclude that one drug statistically significantly outperformed the other. This example uses the data set, CH 0 6, Example 01, ANOVA.sav.
NARRATOR [continued]: This data set contains two variables. The first variable is group, which is a categorical variable containing three values. Group 1 gets Drug A, Group 2 gets Drug, and Group 3 gets Drug C. The second variable is systolic BP.
NARRATOR [continued]: This is a continuous variable that contains the systolic blood pressure reading of each participant. The ANOVA test will compute the mean for each group and then calculate the p-value for each pair of groups, which will tell us which groups statistically significantly outperformed which.
NARRATOR [continued]: The ANOVA test has three pretest criteria– normality, n quota, and homogeneity of variance. We'll check for normality now. The other two will be processed when we run the ANOVA test. To check for normality, we need to order a histogram with a normal curve for systolic BP for each group.
NARRATOR [continued]: We'll begin with Group 1– the group that got Drug A. Click "select cases." Click "if condition is satisfied," and click "if." In the dialog box, enter "group = 1." Click "continue."
NARRATOR [continued]: Click "OK." Now that only the records for Group 1 are active, we can order a histogram with normal curve. Click "Analyze, Descriptive Statistics, Frequencies." Move systolic BP to variables.
NARRATOR [continued]: Click "charts" and select histogram with normal curve. Click "continue," click "OK," and it'll process. The histogram for Group 1, Drug A, meets the normality criteria. Now we'll repeat this process for Group 2,
NARRATOR [continued]: Drug B. Click "select cases." Click "if." In the dialog box, enter "group = 2." Click "continue," and click "OK." Now that only the records for Group 2 are active,
NARRATOR [continued]: we can order a histogram with a normal curve. Click "Analyze, Descriptive Statistics, Frequencies." Notice that systolic BP is still in variables. Since the charts option hasn't been changed since our prior run, we don't need
NARRATOR [continued]: to revisit the charts menu. Click "OK," and it'll process. The histogram for Group 2, Drug B, meets their normality criteria. We'll repeat this one more time to process Group 3, Drug C. Click "select cases."
NARRATOR [continued]: Click "if." In the dialog box, enter "group = 3." Click "continue," and click
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"OK." Now that only the records for Group 3 are active, we can order the histogram with normal curve. Click "Analyze, Descriptive Statistics, Frequencies."
NARRATOR [continued]: Click "OK," and it'll process. The histogram for Group 3, Drug C, meets the normality criteria. Before we run the ANOVA test, we need to select all of the records. The simplest way to do this is to click on the filter
NARRATOR [continued]: underscore dollar sign variable, And press the "Delete" key. To run the ANOVA test, click on "Analyze, Compare Means, One way ANOVA." Move systolic BP to dependent list.
NARRATOR [continued]: And move "group" to "factor." Click "options," and click "descriptive and homogeny of variance test." Click "continue." Now click on "post-hoc." The post hoc menu allows us to select how the pairs of groups
NARRATOR [continued]: will be compared to each other. Since each group has the same number of participants, we'll opt for the two-key test. If the groups had had different ends, then we'd select the side deck test instead. Click "continue," click "OK," and it will process.
NARRATOR [continued]: We see that each group has an end of at least 30, hence the end quote is satisfied. We also see that the homogeneity of variance test returned a p-value of 0.656. Since this is greater than 0.05, this indicates that there is no statistically significant
NARRATOR [continued]: difference in the variances between the groups, hence the homogeny of variance test is satisfied. Back to the descriptive table, notice that this table contains the means for each group. These figures will be useful when it comes to documenting the results. Finally, we see the results of the ANOVA test.
NARRATOR [continued]: The p-value is 0.003. Since this is less than or equal to 0.5, this tells us that at least one group statistically significantly outperformed another. To determine which group outperformed which, we'll look at the pairwise comparisons on the multiple comparisons table.
NARRATOR [continued]: The first pair we'll assess is Drug A, with a mean of 120.37, and Drug B, with a mean of 122.37. The p-value of 0.019, is less than or equal to 0.05, indicating that Drug A statistically significantly
NARRATOR [continued]: outperformed Drug B. The next comparison is Drug A with a mean of 120.37, and Drug C, with a mean of 122.70. The p-value is 0.005. Since this is less than or equal to 0.05,
NARRATOR [continued]: this indicates that Drug A statistically significantly outperformed Drug C. The last pair of comparisons involves Drug B, with a mean of 122.37, and Drug C with a mean of 122.70. This comparison produced the p-value of 0.891.
NARRATOR [continued]: Since this is greater than 0.05, this tells us that there is no statistically significant difference between Drug B and Drug C. This concludes this video.
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References
Knapp, H. (Academic). (2017). T-test [Streaming video]. Retrieved from SAGE Research Methods
Knapp Ph.D., H. (Academic). (2016). Correlation and Regression – Pearson[Video]. SAGE Research Methods Video
Knapp Ph.D., H. (Academic). (2016). ANOVA [Video]. SAGE Research Methods Video
Knapp Ph.D., H. (Academic). (2016). Correlation and Regression – Pearson[Video]. SAGE Research Methods Video
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Week 8 – Assignment: Signature Assignment: Create a Presentation for Statistical Analysis in Research
Instructions
In this Signature Assignment, you will use what you have learned in this course to conduct three statistical tests: a Pearson correlation, an independent t-test, and an ANOVA.
You will use SPSS to conduct the statistical tests.
You will then prepare a PowerPoint presentation in which you share the results of each statistical test. Each slide should contain notes (at least 100 words) describing the analysis in narrative form.
Prepare the PowerPoint presentation and include a voice-over. For instructions on how to include a narration to a PowerPoint presentation, search for “how to create voice-over narration in MSWord PowerPoint.”
Use the attached SPSS file to complete the following:
Select variables appropriate to conduct each of the statistical tests below:
· Pearson Correlation for three variables
· Independent Samples t-test
· Recall from week 5 that the t-test compares means for two groups.
· ANOVA
· Recall from week 5 that the ANOVA test compares means for three or more groups.
PowerPoint
Introduction
Introduce the assignment by stating the importance of hypothesis testing in research and how statistics are used to accomplish this.
Present the statistical tests one at a time. For each statistical test:
1. Identify the null and alternative hypothesis for the statistical test.
2. Identify the Independent and Dependent variables, as appropriate. (Note: not applicable for the Pearson Correlation statistical test)
3. Display appropriate graphics, and descriptive statistics for each of the variables in the statistical test. Describe the central tendency and dispersion of each variable in the slide notes using formal writing.
4. Display the statistical output for the test.
a. Describe the statistical output in the slide notes using formal writing.
b. Reflect on the use and value of the statistical test.
Summary
Close the assignment with a formally written reflection on building a statistical mindset and developing statistical confidence.
Resources:
BUS-7105 Signature Assignment SPSS Data Set
Length: 12 to 15 slides not including title page and reference page
References: Include a minimum of 5 scholarly resources.
Your presentation should demonstrate thoughtful consideration of the ideas and concepts presented in the course and provide new thoughts and insights relating directly to this topic. Your response should reflect scholarly writing and current APA standards. Be sure to adhere to Northcentral University's Academic Integrity Policy.
,
SAGE Research Methods Video
Correlation and Regression – Pearson
Video Title: Correlation and Regression – Pearson
Originally Published: 2016
Publication Date: Sep. 30, 2016
Publishing Company: SAGE Publications, Inc.
City: Thousand Oaks, United States
ISBN: 9781506359212
DOI: https://dx.doi.org/10.4135/9781506359212
(c) SAGE Publications Inc., 2017
HERSCHEL KNAPP: Welcome to Practical Statistics for Nursing Using SPSS. This video shows how to process the Pearson correlation and regression. You can watch the entire video or use the time slider to navigate directly to any time point. [Correlation and Regression – Pearson, Overview] Correlation and regression analysis
HERSCHEL KNAPP [continued]: computes the nature of the relationship between two continuous variables. The relationship can be characterized using two parameters, direction and strength. The regression ranges between -1 and +1. The regression sign indicates the direction of the correlation. Positive correlations occur when the variables
HERSCHEL KNAPP [continued]: move in the same direction. When x goes up, y goes up. Or when x goes down, y goes down. Negative correlations occur when the variables move in opposite directions. When x goes up, y goes down. Or when x goes down, y goes up.
HERSCHEL KNAPP [continued]: The regression value indicates the strength of the correlation. Values nearer to -1 or +1 are stronger than values nearer to zero. To better conceptualize the data, a scatterplot with a regression line is useful. Each point represents two scores gathered from each individual.
HERSCHEL KNAPP [continued]: For example, this point represents two scores gathered on one of the patients surveyed. The patient had a length of stay of 12 days and a depression score of 58. The regression line can be thought of as the average pathway through the points. The positive slope suggests that lower length of stay is associated with lower depression scores,
HERSCHEL KNAPP [continued]: and higher length of stay is associated with higher depression scores for this group of patients. To better comprehend the notion of regression, consider these three examples. Here we see a strong positive correlation between number of homework hours in quiz scores, where lower homework hours are paired with lower quiz scores
HERSCHEL KNAPP [continued]: and higher homework hours are paired with higher quiz scores. In the second scatterplot, we see a strong negative correlation between alcohol consumption and quiz scores, where higher alcohol consumption is paired with lower quiz scores and lower alcohol consumption is paired with higher quiz scores.
HERSCHEL KNAPP [continued]: Finally, we see a fairly weak correlation between baseball-throwing skills and quiz scores, where baseball-throwing skills have virtually no correlation with quiz scores. [Correlation and Regression – Pearson, Pretest Checklist] Before running a Pearson correlation or regression analysis, there are three pretest criteria
HERSCHEL KNAPP [continued]: that need to be met. First, the data for each of the two groups should be normally distributed. We can check for this by observing a histogram with a normal curve for each group. The second and third criteria, linearity and homoscedasticity, can be verified by observing the scatterplot with the regression line.
HERSCHEL KNAPP [continued]: This example uses the dataset Ch 11 – Example 01 – Correlation and Regression.sav. This dataset contains three variables. Patient ID is a string variable, along with two continuous variables, Length of Stay
HERSCHEL KNAPP [continued]: and Depression scores for each patient. To check for normality, order histograms with normal curves for the two variables that will be involved in the correlation, Length of Stay and Depression. Click on Analyze, Descriptive Statistics, Frequencies. Move Length of Stay and Depression into Variables
HERSCHEL KNAPP [continued]: and click Charts. Select Histogram with Normal Curve. Click Continue, and uncheck Display Frequency Table. Click OK, and it'll process. The symmetrical curve
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on the histogram for Length of Stay shows a normal distribution.
HERSCHEL KNAPP [continued]: And the curve on the histogram for Depression also shows a normal distribution. The pretest criteria of normality is satisfied. To finalize the pretest checklist, we'll order a scatterplot with a regression line. This will also give us a more comprehensive understanding of the relationship between Length of Stay and Depression.
HERSCHEL KNAPP [continued]: Click on Graphs, Chart Builder. In the Choose From list, select Scatter/Dot and select the Simple Scatter option. Drag Length of Stay to the x-axis and Depression to the y-axis. Click OK, and it'll process.
HERSCHEL KNAPP [continued]: To order the regression line, double-click on the scatterplot and click on the Add Fit Line at Total icon. In terms of linearity, we see that the points lie in a fairly straight line. There are no unexpected curves or twists in the arrangement of the points. This satisfies the linearity criteria.
HERSCHEL KNAPP [continued]: As for homoscedasticity, we see that the field of points is thicker in the middle and tapers at the ends. This satisfies the homoscedasticity criteria. [Correlation and Regression – Pearson, Test Run] To process a Pearson correlational analysis, click on Analyze, Correlate, Bivariate.
HERSCHEL KNAPP [continued]: Move Length of Stay and Depression to variables. Click OK, and it'll process. [Correlation and Regression – Pearson, Results] The correlation table shows a strong positive correlation of 0.789 between Length of Stay and Depression. We also see that the P value is less than 0.05,
HERSCHEL KNAPP [continued]: suggesting that this is a statistically significant correlation. This concludes this video.
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SAGE Research Methods Video
t Test
Video Title: t Test
Originally Published: 2016
Publication Date: Sep. 30, 2016
Publishing Company: SAGE Publications, Inc.
City: Thousand Oaks, United States
ISBN: 9781506359137
DOI: https://dx.doi.org/10.4135/9781506359137
(c) SAGE Publications Inc., 2017
HERSCHEL KNAPP: Welcome to Practical Statistics for Nursing Using SPSS. [PRACTICAL STATISTICS FOR NURSING USING SPSS– HERSCHEL KNAPP] This video shows how to process the t Test. [t Test] You can watch the entire video or use the time slider to navigate directly to any time point. [t Test– Overview] The t Test compares two groups of continuous variables
HERSCHEL KNAPP [continued]: to determine if one group statistically significantly outperformed the other. In this example, we have two groups. The members of each group will be given an anti- hypertensive drug. The participants in Group 1 get Drug A, and the participants in Group 2 get Drug B. After a month, we'll record the systolic blood
HERSCHEL KNAPP [continued]: pressure of each participant. Group 1 had a mean of 120.37, and Group 2 had a mean of 122.37. The t Test will compare the groups to each other and compute the p value. If the p value is less than or equal to 0.05, we would conclude that one drug statistically significantly
HERSCHEL KNAPP [continued]: outperformed the other. [Since p is less than or equal to 0.05, we conclude that Drug A (mu = 120.37) stastitically outperformed Drug B (mu = 122.37)] This example uses the dataset Ch 05 Example 01 t Test.sav. [t Test– Ch 05 – Example 01 – t Test.sav] This dataset contains two variables. The first variable is Group, which is a categorical variable containing two values.
HERSCHEL KNAPP [continued]: Group 1 gets Drug A and Group 2 gets Drug B. The second variable is systolicBP. This is a continuous variable that contains the systolic blood pressure reading of each participant. The t Test will compute the mean for each group and then calculate the p value, which
HERSCHEL KNAPP [continued]: will tell us if one group statistically significantly outperformed the other. [t Test– Pretest Checklist] The t Test has three pretest criteria– normality, n quota, and homogeneity of variance. [t Test– Pretest Checklist] We'll check for normality now.
HERSCHEL KNAPP [continued]: The other two will be processed when we run the t Test. To check for normality, we need to order a histogram with normal curve for systolicBP for each group. We'll begin with Group 1, the group that got Drug A. Click Select Cases. Click If condition is satisfied.
HERSCHEL KNAPP [continued]: And click If. In the dialog box, enter Group = 1. Click Continue. And click OK. Now that only the records for Group 1 are active, we can order a histogram with a normal curve.
HERSCHEL KNAPP [continued]: Click Analyze, Descriptive Statistics, Frequencies. Move systolicBP to variables, click Charts, and select Histogram with normal curve. Click Continue. Click OK, and it'll process.
HERSCHEL KNAPP [continued]: The histogram for Group 1, Drug A, meets the normality criteria. Now we'll repeat this process for Group 2, Drug B. Click Select Cases and click If. In the dialog box, enter Group = 2.
HERSCHEL KNAPP [continued]: Click Continue. And click OK. Now that only the records for Group 2 are active, we can order a histogram with normal curve. Click Analyze, Descriptive Statistics, Frequencies. Notice that systolicBP is still in variables.
HERSCHEL KNAPP [continued]: Since the charts option hasn't been changed since our prior run, we don't need to revisit the Charts menu. Click OK and it'll process. The histogram for Group 2, Drug B, meets the normality criteria. [t Test– Run] Before running the t Test, we need to select all of the records.
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Page 2 of 3 t Test
HERSCHEL KNAPP [continued]: The simplest way to do this is to click on the filter_$ variable and press the delete key. To run the t Test, click on Analyze, Compare Means, One-way ANOVA. SPSS does have a t Test menu, but the One-way ANOVA menu
HERSCHEL KNAPP [continued]: is easier to operate, produces the same results, and will lay the foundation for processing datasets in the ANOVA chapter. Move systolicBP to dependent list and move Group to factor. Click Options and click Descriptive and Homogeneity
HERSCHEL KNAPP [continued]: of variance test. Click Continue. Click OK. And it'll process. [t Test– Results] We see that both groups have an n of at least 30, hence the n quota is satisfied. We also see that the Homogeneity of variance test
HERSCHEL KNAPP [continued]: returned a p value of 0.945. Since this is greater than 0.05, this indicates that there is no statistically significant difference in the variances between the groups. Hence, the Homogeneity of variance test is satisfied. Back to the descriptive table, notice that this table contains
HERSCHEL KNAPP [continued]: the means for each group. These figures will be useful when it comes to documenting the results. Finally, we see the results of the t Test. The p value is 0.014. Since this is less than or equal to point 0.05, this tells us that Drug A, with a mean of 120.37,
HERSCHEL KNAPP [continued]: statistically significantly outperformed Drug B, with a mean of 122.37, when it comes to lowering blood pressure. This concludes this video.
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Dataset Codebook BUS7105, Week 8
Name Source Representation Measurement Meaning
Subject’s Identification Number
Qualtrics Identification Number. Auto generated by Qualtrics software.
Anonymous identification of survey taker
N/A Sequential numbers in order of survey taker completion. Dataset organization purposes only.
Gender Self-reported by survey- taker: Survey Question #1
Survey-taker gender affiliation
Categorical, Dichotomous
1 = Female 2 = Male
Age Self-reported by survey- taker: Survey Question #2
Survey-taker reported age in years
Continuous, Scale Age in whole years.
Education Self-reported by survey- taker: Survey Question #3
Survey-taker education level
Categorical, Nominal 1 = High School Completion 2 = Bachelor’s degree Completion 3 = Master’s Degree Completion
Personality Self-reported by survey- taker: Average of Survey Questions: #4(Reverse Scored), 5, 6, 7 (Reverse Scored), 8, 9(Reverse Scored)
Composite score of Survey-taker degree of introversion to extroversion personality traits.
Likert scale 1 – 7, Interval*
1 = Survey Response: Highly Disagree (Introvert) To 7 = Highly Agree (Extrovert)
Job Satisfaction Self-reported by survey- taker: Average of Survey Questions: #10, 11, 12, 13
Composite score of Survey-taker satisfaction with their current job.
Likert scale 1 – 10, Interval
1 = Very Dissatisfied To 10 = Very Satisfied
Engagement Self-reported by survey- taker: Average of Survey Questions: #18, 19, 22(Reverse Scored)
Composite score of Survey-taker engagement in their current job.
Likert scale 1 – 7, Interval*
1 = Survey Response: Almost None of the Time (Very Low Engagement) To 7 = Survey Response: Almost All of the Time (Very High Engagement)
Trust in Leader Self-reported by survey- taker: Average of Survey Questions: # 15, 16, 17, 21
Composite score of Survey-taker trust in direct leader in their current job.
Likert scale 1 – 7, Interval*
1 = Survey Response: Almost None of the Time (Very Little Trust in Leader) To 7 = Survey Response: Almost All of the Time (Great Deal of Trust in Leader)
Motivation Self-reported by survey- taker: Average of Survey Questions: #14 (Reverse Scored), 20 (Reverse Scored), 23, 24, 25
Composite score of Survey-taker motivation in performing their current job.
Likert scale 1 – 7, Interval*
1 = Survey Response: Almost None of the Time (Not Motivated At All) To 7 = Survey Response: Almost All of the Time (Highly Motivation)
Intent to Quit Job Self-reported by survey- taker:
Composite score of Survey-taker intent to quit their current job.
Likert scale 1 – 7, Interval*
1 = Survey Response: Almost None of the Time (High Intent to Quit Job)
Average of Survey Questions: #26, 27, 28
To 7 = Survey Response: Almost All of the Time (Low Intent to Quit Job)
* Composite scores of multiple Likert scaled survey questions may be deemed interval in most situations. Exceptions include a single Likert
scaled survey question, scale range options less than 5, sample data that are not normally distributed, and certain variable characteristics
(Carifio & Perla, 2008; Sullivan & Artino, 2013). Measurement level assignment in SPSS is nominal, ordinal, and scale. Because meaningful
computations may be performed with interval data; SPSS assumes interval level variables as metric, and they are assigned to the scale
measurement level. See Measurement column in the Variable View tab.
Carifio, J., & Perla, R. (2008). Resolving the 50-year debate around using and misusing Likert scales. Medical Education, 42(12), 1150 1152. https://doi-org.proxy1.ncu.edu/10.1111/j.1365-2923.2008.03172.x Sullivan, G. M., & Artino, A. R., Jr. (2013). Analyzing and interpreting data from likert-type scales. Journal of Graduate Medical Education, 5(4), 541–542. https://doi-org.proxy1.ncu.edu/10.4300/JGME-5-4-18
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