Assignment #1: Research Question and Hypotheses

Attached Files:

• RES 7605_Survey.pdf

Submit one proposed research question that could be created from the "RES 7605_Survey." (See Attached.) Your research question must be created from items (and their possible responses--which need not be collected) that are included in the survey. You will need to interpret and identify the variables based on available question items. For example, if an item asks respondents “How long have you been employed in your current field?,” you may decide to describe this item as the variable “years of experience.” Below are the main components of the assignment:

• Clearly state your research question. (Make it clear, concise, and answerable using the possible responses from the survey.)

• Describe the items/variables you have included in your research question and the level of measurement (e.g., nominal, ordinal, interval/ratio) used to measure the items/variables on the survey. (Specify the item number so that the reader is clear as to which item you are referring to.)

• Specify which variable is being treated as the independent variable and which variable is the dependent variable in your research question.

• Identify the null and research hypotheses that align clearly to your research question.

• Explain whether your research hypothesis is directional or non-directional.

This assignment should be no more than two to three pages and must include definitions of key concepts and course citations. The assignment must be written in paragraph form using APA format.

Below is an abbreviated example:

Say that within the survey, the participants were asked about their political affiliation (i.e., Democrat, Republican, Independent) in item #5 and their hair length (by inches) in item #6. In this situation, you can state your RQ as: "Among students taking RES 7605, is there a difference in hair lengths based on political affiliation?"

• In this RQ, hair length is interval and political affiliation is nominal. (Explain why the two variables are at their respective levels of measurement here.)

• Hair length is the dependent variable, and political affiliation is the independent variable. (Explain why here.)

• Your hypotheses would be:

  • Null Hypothesis: "Among students taking RES 7605, there is no difference in hair lengths based on political affiliation."

  • Research Hypothesis: "Among students taking RES 7605, there is a difference in hair lengths based on political affiliation." (Do you see how the null and research hypotheses are clearly aligned to the research question?)

• The research hypothesis is non-directional. (Explain here.)

Assignment #2: Descriptive Statistics & Data Displays

Attached Files:

• survey.sav (19.305 KB)

For Assignment #2, you need to download and open a data file that is attached, titled “survey.sav." Below are your steps:

• Perform analyses in SPSS on THREE variables (one nominal, one ordinal, and one interval/ratio) to obtain appropriate descriptive statistics (e.g., measures of central tendency, dispersion, and distribution). Be sure to choose appropriate descriptive statistics for each variable, separate and independent of one another.

• Write out the key descriptive information (e.g., mean, median, mode, standard deviation) in the body of the text, and not just include the tables and not write about them. Also include a clear rationale or justification as to why each of your three variables is nominal, ordinal, interval, or ratio.

• Create appropriate and useful data displays (e.g., histogram, bar graphs, pie charts) for each chosen variable. (Be sure to include the percentages and raw numbers within the graphs for the pie charts and bar graphs. See Model Paper.)

• Export or paste your SPSS output into a Word document and write up the results of these analyses as they would appear in a research report. Use the APA 7th manual or the OWL at Purdue for formatting guidance. Be sure to discuss your analyses.

• Write a paragraph (200 to 400 words) at the end of your paper to address why exploratory data analysis (or a descriptive analysis of the variables) is a critical part of any statistical analysis. The explanation must include key concepts and course citations from Module 3: Readings and Resources to demonstrate your understanding.

Attached below is a model paper. (Do not use any of the variables used in this model paper for your assignment.) Note that the model paper does not contain discussion on exploratory data analysis, but you are still responsible for completing that portion of the assignment in your submission.

Assignment #2_Model Paper.pdf

Test One

This test is open book. Questions are entirely multiple choice responses. You will receive your score (with correct answers for any items missed) at the end of the test. You must complete this test during ONE login, so please plan accordingly. Test #1 covers Learning Modules 1-3 ONLY.

Test Two

This is the second of two tests taken in Blackboard. This test is open book. Questions are entirely multiple choice responses. You will receive your score (with correct answers for any items missed) at the end of the test. You must complete each test during ONE login, so please plan accordingly. Tests are NOT cumulative. Test #2 covers Learning Modules 4-8 ONLY.

For Week 4 (Correlational Analysis / Chapters 5 and 15) we are examining computing correlations and testing relationships using correlation. Correlation coefficients are a measure of association between variables and that this association can be tested for statistical significance. Also, when interpreting the correlation there is an important distinction between significance and meaningfulness when interpreting your results.

As you are working to complete your Module 4 – Correlational Analysis activity, remember that the correlation coefficient value, a numerical index that reflects the relationship between two variables, can range from −1.00 to +1.00. The correlation coefficient represents the dynamic relationship of two variables. If your analysis indicates that as the values of the independent variable increase the values of the dependent variable increase, the relationship is said to be a direct correlation or a positive correlation. Positive correlations are indicated by coefficients greater than zero. If the values of the independent variable increase and the values of the dependent variable decrease, the relationship is said to be an indirect correlation or a negative correlation. Negative correlations are indicated by coefficients less than zero. The absolute value of the coefficient reflects the strength of the correlation.

Remember, a scatterplot or scattergram is a simple way to visually represent a correlation. The scatter-plot represents each set of scores on separate axes. A positive correlation is indicated when the data points align in a roughly positive slope. A negative correlation is indicated when the data points align in a roughly negative slope. Stronger correlations are represented by clusters of data points following a tightly clustered, straight line. Weaker correlations are represented by loosely packed data points. Linear correlation is a correlation represented by a straight line.

For reference, on page 8 of your Module 4 lecture is a guide for interpreting the strength of the correlation based on the value of the correlation. Do not forget that correlation does not mean causation. Just because there is a relationship between two variables (direct or indirect), that does not mean that one variable causes the change in the other variable. Also, even though the correlation may be significant, it may not be very meaningful. You can square the correlation coefficient to determine the amount of variance in one variable that is determined by the other variable. For example, if the squared correlation coefficient only accounts for 25% of the variance, 75% is still unaccounted for.

Finally, keep in mind that you report the Pearson’s correlation when examining the relationship between two continuous variables and the Spearman’s correlation when examining the relationship between two ordinal variables.

Week 4 update

As you are working on your correlation activity (Module 4) I want to share a few things to remember when conducting a correlation test.

Even with a very weak correlation, if you find the p value (sig) is statistically significant at the significance level set a priori (usually p = .05), you may reject the null hypothesis. The negative correlation only indicates the direction of the relationship or change in the variable. It does not influence whether you would reject or not reject the null hypothesis.

You do not need to run a histogram or scatter-plot for ordinal and nominal variables. Since these are categorical measures, interpreting these visual representations for categorical data can be confusing and at times inaccurate.

If SPSS provides you with a p value of .000, then in your write-up you should report p < .001. Keep in mind p (sig) is never zero. SPSS only presents the first three decimal places. You can see the actual p value by clicking on the .000 value in your SPSS output.

Pearson’s r is used for interval/ ratio (continuous) variables and Spearman’s rho is used for ordinal (categorical) and nominal (categorical) variables.

For your descriptive statistics, be sure you report the appropriate measure of central tendency and distribution information for nominal variables (mode and frequencies and/ or percentages), ordinal variables (mode or mean and frequencies/ and/ or percentages), and interval/ ratio variables (mean or median or mode and frequencies and/ or percentages and standard deviation and skewness and kurtosis). You can also report the range for all three levels of measurement.

Also, when writing your null and research hypotheses, be sure you use the same wording in each statement, and write these statements using the wording you used in your research question.

Feedback from Assignment #2

Remember, you do not need to report the mean and standard deviation for ordinal data (categorical data). These measures are appropriate for continuous data (interval/ ratio). Also, the histogram is a data visualization that should only be used with interval/ ratio data. For categorical data (nominal and ordinal) use a bar chart.

For your interval variable, you need to explain the mean, skewness, kurtosis, and standard deviation. These are measures used with continuous data (interval/ratio).

descriptive statics vs inferential statistics

Course Introduction; Getting to Know SPSS

Watch/read

• Course Introduction.pdf

• Lecture: Data and Statistics

Textbook Readings: Salkind & Frey, Chapter 1

Hoonuit SPSS Tutorials Hoonuit SPSS Tutorials

"Getting Started" with SPSS [link in BlackBoard]

What you will learn

• Opening Dialogue Box overview and creating a new file

• Data Interface overview

• Output Window overview

Click on the link above to access the SPSS tutorials -OR- go through Concordia Connect --> Resources (on the left side of the screen) and scroll down to "Hoonuit" on the right side of the screen. Type "SPSS 24 - Basic" in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under "Learnit." Select the appropriate videos as listed above.

How to Create an SPSS File

How to Create an SPSS Data File.pdf How to Create an SPSS Data File.pdf - Alternative Formats (357.493 KB)

Discussion

As part of Learning Module 1, introduce yourself to the class. This is not the usual professional background overview. Instead, please pick one word that describes you, your beliefs, and your passions. Put only this one word in the subject line of your post; then in the text box, write a short paragraph as to why you picked that specific word. In the next paragraph, describe your experiences (if any) with quantitative analysis, and your hopes and expectations for this class.

Once completed review the words listed in the subject lines of your fellow classmates. Pick one that resonates with you and reply to that person, explaining why their word choice struck a chord in you. See if you can find an image online that you can upload representing that person and his/her passions and interests. Post that image within their discussion board thread by the end of the week.

Read through everyone's posts, feeling free to engage and comment wherever you see fit. If you'd like, add a photo of yourself so we can associate a name with a face. Or, if you'd rather, upload a photo that you feel best represents you.

Next, create or update your Grad Studeent Repository Google Doc and share the viewing link set to “Anyone with the link can view” to your response so we can learn about your research interests.

In addition to providing the link to your repository, share what kind of feedback you would like from your peers. Lastly, based on your reading of the syllabus, how might this course help you address your research questions/interests? What do you hope to learn?

Note: This student repository is an ongoing document that you will use throughout your doctoral program. You should update this each term during the first week to share and build upon it, and most importantly, work on it throughout each term. When updating, use a different color font to show what is new/revised.

Please read at least two peers’ repositories and provide them with feedback in the discussion forum: What interests you in what was shared? What connects with your own interests? What reading recommendations can you offer?

Constructing Hypotheses for Statistical Testing & Probability Theory

Read/Watch

• Lectures: Variables & Hypotheses; Hypothesis Testing & Significance

• Readings: Salkind Chapters 7-9; Developing Hypotheses; Writing Quantitative Research Questions

• Atomic Learning Experimental Analysis with SPSS 24 Tutorials

• A. Getting Started: Modules 2-4

Discussion Board Activity

Initial Post Due Thursday by 11:59 PM

Critical feedback to 2 peers by Noon on Sunday

Assignment #1

• Submit in Blackboard Assignments

• Due Sunday by 11:59 PM

*See the At a Glance doc

Descriptive Statistics and Data Displays

• Lecture: Descriptive Statistics PDF / Video

• Lecture: Summarizing Data PDF / Video

• Textbook Readings: Salkind & Frey, Chapters 2, 3, & 4

• APA 7th Manual: Chapter 7

• Four Levels of Measurement video
  Please view this video to help you better understand the four levels of measurement

• Frequencies and Descriptive Statistics in SPSS video
  You will find this video very helpful in guiding you through various procedures in SPSS related to getting frequency tables and basic descriptive information.

• SPSS & Descriptive Statistics video
  Another helpful video on SPSS here.

• Mean, Median, & Mode PDF

• Pallant Chapter 7 - Using Graphs to Describe and Explore the Data (Optional reading) PDF

• Guide for Selecting Appropriate Statistics PDF

• Atomic Learning SPSS Tutorial
  Connect > Resources (on the left side of the screen) and scroll down to “Atomic Learning” on the right side of the screen. Click on Atomic Learning (which takes you to “HOONUIT”), and type “SPSS 24 - Basic” in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under “Learnit.” Select appropriate videos related to activities in Module 3.

Discussion

Descriptive Statistics & Data Displays

As part of Learning Module 3, post a DRAFT of your Descriptive Statistics Assignment here no later than 11:59 PM on the Thursday of this Learning Module. Reply to one classmate (be clear whom you are pairing up with so everyone gets peer feedback) by posting written feedback in the form of a peer review by noon on the Sunday of this Learning Module. It is expected that you will give constructive feedback based on the principles of good data analysis. Please include 300–500 words of written comments on the content and format of the descriptive statistics presented by a classmate. It is expected that you will incorporate the comments of your peer(s) in your assignment, due to the Assignment Link by 11:59 PM on Sunday at the end of Learning Module 3.

To summarize the plan for this Module…

1. Post a draft of your descriptive statistics assignment on this Discussion Board thread no later than 11:59 PM on Thursday of this learning module.

2. Write a 300–500 word peer review on a classmate’s descriptive statistics draft. Post it on their Discussion Board thread by NOON on Sunday of this learning module (this gives your classmate at least 12 hours to incorporate any revisions you suggested).

3. Revise your draft based on the peer review (if deemed appropriate and useful) and post your Descriptive Statistics Assignment in the Assignment Link by 11:59 PM on Sunday of this Learning Module.

Assignment 1

Submit one proposed research question that could be created from the “RES 7605_Survey.” (See Attached.) Your research question must be created from items (and their possible responses–which need not be collected) that are included in the survey. You will need to interpret and identify the variables based on available question items. For example, if an item asks respondents “How long have you been employed in your current field?,” you may decide to describe this item as the variable “years of experience.” Below are the main components of the assignment:

* Clearly state your research question. (Make it clear, concise, and answerable using the possible responses from the survey.)

* Describe the items/variables you have included in your research question and the level of measurement (e.g., nominal, ordinal, interval/ratio) used to measure the items/variables on the survey. (Specify the item number so that the reader is clear as to which item you are referring to.)

* Specify which variable is being treated as the independent variable and which variable is the dependent variable in your research question.

* Identify the null and research hypotheses that align clearly to your research question.

* Explain whether your research hypothesis is directional or non-directional.

This assignment should be no more than two to three pages and must include definitions of key concepts and course citations. The assignment must be written in paragraph form using APA format. Below is an abbreviated example:

Say that within the survey, the participants were asked about their political affiliation (i.e., Democrat, Republican, Independent) in item #5 and their hair length (by inches) in item #6. In this situation, you can state your RQ as: “Among students taking RES 7605, is there a difference in hair lengths based on political affiliation?”

In this RQ, hair length is interval and political affiliation is nominal. (Explain why the two variables are at their respective levels of measurement here.)

Hair length is the dependent variable, and political affiliation is the independent variable. (Explain why here.)

Your hypotheses would be: Null Hypothesis: “Among students taking RES 7605, there is no difference in hair lengths based on political affiliation.”

Research Hypothesis: “Among students taking RES 7605, there is a difference in hair lengths based on political affiliation.” (Do you see how the null and research hypotheses are clearly aligned to the research question?).

The research hypothesis is non-directional. (Explain here.)

Correlational Analysis

Correlation Coefficients

Required Reading/Viewing

• Lecture: Measures of Association PDF/video

• Textbook Reading - Salkind & Frey, Chapters 5 & 15

• APA 7th Manual: Chapter 6

• Pearson Correlation in SPSS [video]

• Pearson, Kendall, and Spearman Analyses in SPSS [video]

• SPSS for questionnaire analysis: Correlation analysis [video]

• Interpreting and Reporting Correlation Results in SPSS Interpreting and Reporting Correlation Results in SPSS [link]

You may find the link very helpful in making sense of the correlation results in SPSS. Please click both "Interpret Data" and "Report Data" to get the full picture.

How to Report Pearson's r in APA Style How to Report Pearson's r in APA Style [PDF]

Please read this carefully to interpret your p-values in Module 4 Discussion post. If you see your p-value in SPSS as ".0000," do not report it as "p = .0000." Rather, it should be reported as "p < .001."

• Impact of Sample Size [PDF]

• File rejection region example [PDF]

• Atomic Learning SPSS Tutorials

Go through Concordia Connect --> Resources (on the left side of the screen) and scroll down to "Atomic Learning" on the right side of the screen. Click on Atomic Learning (which takes you to "HOONUIT"), and type "SPSS 24 - Basic" in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under "Learnit." Select appropriate videos related to activities in Module 4.

Discussion

Correlational Analysis

Using the attached survey, perform one analysis (i.e., Part A or B) using Pearson correlation and the other (i.e., Part A or B) using Spearman correlation. Determine the appropriate test for each relationship, and then proceed with your analyses.

Part A — Using a sample of 575 adults in River Forest, IL, assess the relationship between the years of school completed (variable name = “educ”) and the years of school completed by the mother (variable name = “maeduc”).

Part B — Using a sample of 575 adults in River Forest, IL, assess the relationship between the highest degree earned (variable name = “degree”) and the stress level associated with work (variable name = “stress”).

For BOTH Part A and B, you need the following:

• A clear and concise research question: (e.g., "Among population A, is there a relationship between X and Y?”)

• A clear and concise null hypothesis that aligns to the research question: "Among population A, there is no relationship between X and Y.”)

• A clear research hypothesis that aligns to the research question: "Among population A, there is a relationship between X and Y.”)

• Basic descriptive analysis of the variables used (e.g., mean, median, SD, range, % for each group, etc.) in a paragraph form. (Don't just include a number of SPSS tables and not talk about it.)

• State the rationale for applying either Pearson or Spearman correlation in this investigation using appropriate readings and resources in Module 4. (Please cite specific references.)

• Findings in an APA format using the linked example write up to serve as a template. EXAMPLE CORRELATION WRITE UP.pdf

• Please include the SPSS output (e.g., tables and such) used in your analyses, both descriptive and inferential.

• Repeat steps/elements 1 to 7 above for both Parts A and B.

To double check that you have included all the necessary components, use this checklist for yourself before posting:

1. State the research question and hypotheses.

2. Provide appropriate statistics for each variable (e.g., mean, median, SD, etc.)

3. State the rationale for applying either Pearson or Spearman correlation for each investigation.

4. Report the r or rho value, and interpret its strength and direction.

5. Report the p-value, interpreting whether it is statistically significant. (Please read this link to help you better understand, report, and interpret p-values: https://www.socscistatistics.com/tutorials/correlation/default.aspx

6. State whether you reject or fail to reject the null hypothesis (and why).

7. Interpret the practical meaning by reporting and interpreting the r or rho value.

8. Also include the coefficient of determination results and its interpretation in a clear, understandable fashion (only for the Pearson correlation).

9. Include the correlation table from your SPSS output (cut and paste onto Word document).

*Post your discussion submission by 11:59 pm Thursday, and give critical feedback to at least two peers by noon Sunday. After you have received your comment, upload a revised discussion post (if you feel that it is warranted based on your peer feedback and other information you have gathered since the initial post) by 11:59pm Sunday. Please keep the old version(s) in the discussion post so that revision and progress can be documented.

Please note: This discussion is set up so that you must post your results/posts before you are permitted to view others' posts.

survey [link]

Test One

Linear Regression

Readings and Resources

• Video/PDF: Lecture on Measures of Association Part 2.

• Textbook Readings: Salkind & Frey, Chapter 16

• YouTube Video: Simple Linear Regression in SPSS

• Simple Linear Regression – Step by Step

• Atomic Learning SPSS Tutorials

• Go through Connect –> Resources (on the left side of the screen) and scroll down to “Atomic Learning” on the right side of the screen. Click on Atomic Learning (which takes you to “HOONUIT”), and type “SPSS 24 - Basic” in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under “Learnit.” Select appropriate videos related to activities in Module 5.

Discussion

Using the attached data (“Car Weight and MPG.sav”), compute and interpret the relationship between:

• Car Weight (“Weight”) and miles per gallon consumed (“CityMPG”).

For this regression analysis, state the following:

• A clear research question appropriate for a linear regression: (e.g., Is X a significant predictor of Y?")

• Null and research hypotheses that align clearly and neatly with your RQ.

• Description of the variables used.

• Your dependent and independent variables. (Clearly explain your rationale as to why which is which.

  • State the sample size, along with the mean, standard deviation, and range (min & max) for each variable, in a paragraph format. (Do not just include a table, and not talk about it.)

  • State the rationale for applying regression analysis in this investigation using appropriate readings and resources in Module 5. (Please cite specific references.)

• Findings in an APA format. (This includes the wording of the finding and the tables/figures included.).

  • Use the following link to provide the template.

  • Also add a clear interpretation of the R-square value in your finding. See last page.

  • Note: Even if the p-value in the SPSS output shows “.000,” write it out as “p < .001.”

• Write out your regression equation, and state the predicted miles per gallon for cars that weighs 3,000 and 4,000 pounds. (Clearly show the calculations and steps involved.)

• Include the SPSS output in your appendix.

Post your discussion submission by 11:59 PM Thursday, and give critical feedback to two peers by noon Sunday. After you have received your comment, upload a revised discussion post (if you feel that it is warranted based on your peer feedback and other information you have gathered since the initial post) by 11:59 PM Sunday. Please keep the old version(s) in the discussion post so that revision and progress can be documented.

Please note: This discussion is set up so that you must post your results before you are permitted to view others’ posts.

Car Weight and MPG.sav

Difference Between Groups (t-Tests)

Readings and Resources

• Lecture: t-Tests [video and PDF]

• Textbook Reading: Salkind & Frey, Chapters 10, 11, & 12

• Quick Overview of t-Tests [video]

• Independent Samples t-Test in SPSS (Part 1)

• Independent Samples t-Test in SPSS (Part 2) [video].

• Interpreting SPSS Output: Independent-Samples t-Test [video]

• Dependent (Paired) Samples t-Test in SPSS (Part 1)

• Dependent (Paired) Sample t-Test in SPSS (Part 2)

• Interpreting SPSS Output: Dependent (Paired-Samples) t-test

• Basics Behind Effect Size [video]

• Effect Size Calculator

• Here is a useful link to help you with calculating the effect size. Below are the guidelines:

  • Report Cohen's d as your effect size for Module 6 Discussion, not "effect-size r."

  • For the independent samples t-test, report the two standard deviations of the respective groups in the effect size calculator.

  • For dependent samples (or paired-sample) t-test, use the standard deviation in the "Paired Samples Test" box in your SPSS output. (Here is a useful video: https://www.youtube.com/watch?v=yVbYvn_cT5w)

  • Atomic Learning SPSS Tutorials Atomic Learning SPSS Tutorials

    • Go through Connect --> Resources (on the left side of the screen) and scroll down to "Atomic Learning" on the right side of the screen. Click on Atomic Learning (which takes you to "HOONUIT"), and type "SPSS 24 - Basic" in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under "Learnit." Select appropriate videos related to activities in Module 6.

Discussion

t-Tests

Using the attached data (i.e., “job satisfaction” and “practice”), perform one analysis (i.e., Part A or B) using a dependent (or paired) samples t-test and the other (i.e., Part A or B) using the independent samples t-test. Determine appropriate test for each relationship, and then proceed with your analyses. (To be clear, you are required to perform TWO t-tests in this post.)

Part A

Teachers at ACE Charter School were given a 20% raise in their salary in August of 2015. Of all the teachers at ACE, 30 of them were asked to participate in a study to assess the effect of the pay raise on their overall job satisfaction. How was their job satisfaction (“0” = very unsatisfied to “50” = very satisfied) affected as a result? (Note: You can make causal inferences in this analysis.)

• What is the research question? (Use “difference” language as we are no longer talking about “relationship” among variables but differences between groups.)

• What is the null hypothesis?

• What is the research hypothesis? (Directional)

• Basic descriptive analysis appropriate for the variables used (e.g., mean, median, range, SD, etc.) in a paragraph form. (Don’t just include a number of SPSS tables and not talk about it.)

• State the rationale for applying either independent or dependent t-test in this investigation using appropriate readings and resources in Module 6. (Please cite specific references.)

• Write out the results in an APA format. (Use the appropriate example below as your template.)

• Please include appropriate tables from the SPSS output used in your analyses.

Part B

Using a sample of 575 participants from River Forest, IL, compare the average years of education (variable name = “educ”) between men and women in this city. (Note: Avoid causal inferences here, and just talk about the differences between men and women in the analysis.).

• What is the research question? (Use “difference” language as we are no longer talking about “relationship” among variables but differences between groups.)

• What is the null hypothesis?

• What is the research hypothesis? (Non-Directional)

• Basic descriptive analysis appropriate for the variables used (e.g., mean, median, range, SD, etc.) in a paragraph form. (Don’t just include a number of SPSS tables and not talk about it.)

• State the rationale for applying either independent or dependent t-test for in this investigation using appropriate readings and resources in Module 6. (Please cite specific references.)

• Write out the results in an APA format. (Use the appropriate example below as your template.)

• Please include appropriate tables from the SPSS output used in your analyses.

An Example of a Paired-Samples t-Test Result Write Up

“Using a sample of 99 participants, a paired-sample t-test was conducted to compare the number of words recalled (from 0 to 15) in ginkgo and placebo conditions. There was a significant difference in the number of words recalled for ginkgo (M = 6.23, SD = 3.91) and placebo (M = 4.35, SD = 3.45) conditions; t(99) = 4.12, p < .001. These results suggest that consumption of ginkgo is related to a significant or meaningful increase in one’s memory, by about two points. The size of the effect (using Cohen’s d) is medium at .51.”

An Example of an Independent Samples t-Test Result Write Up

“Using a sample of 200 participants, an independent samples t-test was conducted to compare the number of words recalled (from 0 to 15) in ginkgo (n = 100) and placebo (n = 100) groups. There was a significant difference in the number of words recalled for ginkgo (M = 7.95, SD = 3.91) and placebo (M = 5.85, SD = 3.60) groups; t(198) = 1.99, p = .03. These results suggest that ginkgo group had greater word recall than their placebo counterparts, by about two words. The size of the effect (using Cohen’s d) is medium at .56.”

• Post your discussion post by 11:59 PM Thursday, and give critical feedback to at least two peers by noon Sunday. After you have received your comment, upload a revised discussion post (if you feel that it is warranted based on your peer feedback and other information you have gathered since the initial post) by 11:59 PM Sunday. Please keep the old version(s) in the discussion post so that revisions and progress can be documented.

  • job_satisfaction.sav

  • practice.sav

Please note: This discussion is set up so that you must post your results/posts before you are permitted to view others’ posts.

Week 6 intro

We are now shifting from examining relationships between variables to examining group differences in mean values for a particular variable. This week your readings and activity will focus on the t-test and next week will focus on ANOVA (analysis of variance). The Correlation and Linear Regression Review is a document that provides a review of correlation and regression now that we are moving to examining group differences.

The t-test for independent samples is a statistical procedure that allows for an examination of group differences where the groups are unrelated (e.g., gender or grade level). The t-test for dependent samples is a statistical procedure examining group differences where the groups are related. A typical example of an appropriate use of this repeated-measures statistic is when you want to test the difference between pretest and posttest scores for the same group.

Your readings and t-test activity will focus on computing the observed t value, interpreting the t value and understanding what it means, and computing the effect size for a t-test for independent samples and the effect size for a t-test for dependent samples. As with your Correlation activity, you need to decide which analysis to run for Part A and Part B based on the type of t-test analysis required for each part.

Keep in mind, the first table in your SPSS output is simply the descriptive statistics for the two periods of time or the two groups. It includes the sample sizes, means, standard deviations and standard errors of the means.

The “Test” table provides your data for the test you conducted and informs you as to whether or not you can reject the null hypothesis. For an Independent Samples Test, because you are comparing two means, two different variances are obtained. There is a long equation used to determine which variance to use, but SPSS does this for you by running the Levene’s Test for Equality of Variances. To determine which row to use, look at the large column labeled Levene’s Test for Equality of Variances. This is a test that determines if the two conditions have about the same or different amounts of variability between scores.

A Levene’s sig. value greater than .05 means that the variability in your two conditions is about the same. The scores in one condition do not vary too much more than the scores in your second condition. Put scientifically, it means that the variability in the two conditions is not significantly different.

• If the Levene’s sig. value is above .05 use the top row.

• If the Levene’s sig. value is .05 or below, use the bottom row, or “equal variances not assumed.”

I mention this because there are two sig. values for the Independent Samples t-test and the sig. value for Levene’s Test simply directs you to which row of data to use. The top and bottom rows provide the similar information, they just use different tests to calculate the test statistic, which results in slightly different calculations. Once you have selected the appropriate row, then check the sig, for the t-test to determine if your findings are statistically significant.

Also, when calculating Cohen’s d (the effect size) for an Independent Samples t-test you may use this website: https://lbecker.uccs.edu/ When calculating Cohen’s d (the effect size) for a Paired Samples t-test you do not want to use this calculator. Instead, divide the Paired Differences Mean (in your output) by the Paired Differences Standard Deviation (in your output).

Remember, generally a small effect size ranges from 0.01 to .20; a medium effect size ranges from .21 to .50; a large effect size is any value above .51.

Analysis of Variance

Reading & Resources

• Lecture: Analysis of Variance ANOVA.pdf & video

• Textbook Reading: Salkind & Frey, Chapter 13

• How To Calculate and Understand Analysis of Variance (ANOVA) F Test YouTube Video

• One-Way ANOVA with Post Hoc Test YouTube Video & Post Hoc

• Atomic Learning SPSS TutorialsAtomic Learning SPSS Tutorials

• Go through Connect > Resources (on the left side of the screen) and scroll down to "Atomic Learning" on the right side of the screen. Click on Atomic Learning (which takes you to "HOONUIT"), and type "SPSS 24 - Basic" in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under "Learnit." Select appropriate videos related to activities in Module 7.

Chi Square Tests

Readings and Resources

• Chi-Square.pdf/video

• Salkind & Frey, Chapter 17

• Chi-Square Test in SPSS YouTube Video

• Performing Cross-tabulation in SPSS YouTube Video

• Atomic Learning SPSS TutorialsAtomic Learning SPSS Tutorials

• Go through Connect --> Resources (on the left side of the screen) and scroll down to "Atomic Learning" on the right side of the screen. Click on Atomic Learning (which takes you to "HOONUIT"), and type "SPSS 24 - Basic" in the search bar at the top. It will then take you to a screen that will have a list of videos on the right side of the screen under "Learnit." Select appropriate videos related to activities in Module 8.