naSTAT8028 Business Statistics
(3 credits)
Syllabus Effective Date: 2/17/2010

Course Description:
Doctoral Learners in this course will address statistical techniques that may be useful for analyzing quantitative data in a dissertation. Topics include Descriptive Statistics, one and two sample Hypothesis Testing, Analysis of Variance, Correlation & Regression, and Nonparametric Techniques. Learners will use SAS software to conduct statistical analysis. Assessment will be through a series of problems designed to demonstrate competence in techniques and various data analysis projects.

Number Of Activities: 11

Learning Outcomes:
1.  Interpret research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
2.  Develop appropriate null and alternative hypotheses given a research question.
3.  Evaluate descriptive statistical analysis and visual displays of data.
4.  Apply appropriate statistical tests based on levels of measurement.
5.  Calculate the appropriate use of inferential statistical analysis.
6.  Demonstrate how population, sampling, and statistical power are related to inferential analysis.
7.  Analyze the assumptions required for valid inferential tests.
8.  Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
9.  Demonstrate proficiency in the use of SAS and the reporting of statistical output in APA format.
10. Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

Course Concepts:
1. Statistical terminology
2. Levels of data variables
3. Sampling
4. Descriptive statistics (central tendency and dispersion)
5. Normal probability distribution
6. p-values and significance levels
7. Hypothesis testing for means and proportions
8. Hypothesis testing for relationships
9. Nonparametric hypothesis testing

Primary Resources:
These resources are required to complete the course.

Please make sure that you purchase the primary textbook(s) that match the syllabus you are issued. Please let your assigned Mentor know through the Northcentral University messaging system what text(s) you have purchased. Northcentral cannot be responsible for Learner purchase of books that do not match assigned syllabi.

Book
Field, A.   (2009).  Discovering statistics using SPSS  (3rd ed.).   Los Angeles:   Sage.   ISBN: 9781847879073  
Note: Please note that PASW (formerly called SPSS) software is specified; however, any version of the graduate software from 14 onward may be used in this course. Please purchase a version that is compatible with your operating system.  
Other
PASW Statistics Grad Pack 19.0 for Windows.  ID#: 44W5412  
This dual disc contains both MAC and Window versions.  

You may purchase books at www.ncubooks.com.


Additional Resources:
These resources must be used to complete the assignments.

File
Education.sav 
Education.sav
Website
Field text companion site  http://www.sagepub.com/field3e/  
Website
Self Tests  http://www.sagepub.com/field3e/additionalwebmaterial.htm  
Website
Multiple Choice Questions  http://www.sagepub.com/field3e/MCQ.htm  
Website
Flashcard Glossary  http://www.sagepub.com/field3e/Flashcard.htm  
Website
Flash SPSS Movies  http://www.sagepub.com/field3e/SPSSstudentmovies.htm  
Website
Smart Alex’s Quizzes  http://www.sagepub.com/field3e/SmartAlexAnswers.htm  
File
Chi-Square Tutorial 
Chi-Square tutorial.pdf
File
Activity 6a.sav 
Activity 6a.sav
File
Activity 6c.sav 
Activity 6c.sav
File
Activity 7.sav 
Activity 7.sav
File
Activity 8.sav 
Activity 8.sav
File
Gss.sav 
gss.sav
File
Activity 10.sav 
Activity 10.sav
File
A note about statistical significance 
A note about statistical significance.doc
Website
Reliability vs. Validity video  http://www.youtube.com/watch?v=56jYpFkdqW8  

Supplemental References & Readings:
These resources are not required, but may provide assistance in completing your work for this course. Please copy and paste any web links listed below into your browser to view the websites.
Northcentral University Library Guide
http://library.ncu.edu/research_help/guide.aspx?guide_id=51
General Information:

Credit Hours:

With the faculty-mentored approach at Northcentral University, credit hours are amassed in a course through student-to-faculty interaction, contact with course-specific content, assignments, and other asynchronous activities. At Northcentral, students can expect to devote between 135-144 hours for each 3-credit course.

Course Participation:

Federal Financial Aid regulations, which Northcentral observes for all students, require that students regularly participate in courses in which they are enrolled. All students must log into the course room at least once per week in order to avoid being noted as a non-participant. Students must use the Northcentral messaging system on the course web site to contact faculty. Should you be unable to participate in your course, you must contact your Academic Advisor who can advise you on the consequences of withdrawing from your course.

Preliminaries/Pre-Course Survey:

Students should review the Student web site and Course Catalog, which contains all relevant policies and procedures. Students should also complete the Pre-Course Survey. The survey goes directly to the faculty and gives the faculty information about new students entering the course.

Assignment Submissions:

The assignment header should include the student's last name, first initial, course code, dash, and assignment number (DoeJXXX0000-1) justified to the left and the page number justified to the right. Faculty may request students to submit an assignment cover sheet, located under University Documents on the Students site. Assignments that do not include cover sheets should have an APA style title page.

The file submittal format consists of the student's last name, first initial, course code, dash, and assignment number (no spaces between characters): DoeJXXX0000-1. Files may be submitted in Word or in the program with which the file was created. Faculty may request resubmission of an assignment using a different file format or program if they cannot access a submitted assignment. In the event that the student is unable to submit the assignment to the professor on the date due through any of the above referenced methods because of computer problems, the student is required to email the assignment to the faculty on or before the assignment due date. In such cases, the student should also communicate with the professor to inform of the assignment transmission.

Northcentral University has adopted the 6th edition of the APA Publication Manual as the style guide for all coursework. Students are expected to follow the APA manual when completing assignments, unless instructed otherwise.  Although the APA manual does not apply to syllabi, NCU attempts to adhere to the manual in its syllabi within technical limitations.

Faculty have the discretion to allow and request resubmission of any assignment, with these stipulations: Comprehensive Exam courses are excluded; graded assignments with objectively correct answers (e.g., statistics assignments) may not be resubmitted; the bulk loading policy may not be violated; the policy that assignments may not be submitted after a course end date may not be violated. Students may decline to resubmit assignments. Faculty cannot request resubmissions in cases of suspected academic integrity violations.

 

Recommended Schedule for Course Completion:

Students may submit assignments early, but may not submit the next assignment until they have received a grade on the previous one. Faculty will not accept bulk assignments. mitting assignments in the order assigned and reviewing faculty feedback before completing the next assignment ensures progression according to academic standards and follows the design of the course.

Submittal Turn-Around Schedule:

Faculty will return graded assignments with feedback within 4 calendar days of assignment submission.

Note: Turn-around time for courses in the dissertation sequence, excluding CMP courses, range up to 21 calendar days.

Academic Integrity:

Academic integrity includes the commitment to the values of honesty, trust, fairness, respect, and responsibility. Appropriate credit of others for the scientific work and ideas applies to all forms of scholarship, not just publications. The submission of another person’s work represented as that of the student’s without properly citing the source of the work will be considered plagiarism and will result in an unsatisfactory grade for the work submitted or for the entire course, and may result in academic dismissal. Assignments will be submitted by the faculty member to TurnItIn.com for originality evaluation.

 

Self-plagiarism is the act of presenting one’s previously used work as an original work. Self-plagiarism is inconsistent with honesty and truthfulness in scholarship. Northcentral University faculty and students should discuss the expectations of each activity at the beginning of the class. There should be a clear understanding between the faculty member and student regarding the use of prior work in the class. The faculty member must indicate if the student’s response must be an original work or if the student may use prior work in their response to a new activity. For further information on self-plagiarism, review this guide noted in the NCU Writing Center on the subject.

 

Course Learning Assessment/Course Grade:

Students are expected to complete all performance requirements for the course and to demonstrate mastery of the course concepts and course learning outcomes. This may require students to use library resources and to document research with citations, bibliographies, and references as applicable in completing their coursework. Mastery of course concepts may require demonstration of critical thinking and communication skills by a combination of term papers, self-assessments, quantitative reasoning, interviews, observations, written assignments, or other activities.

Mastery of course concepts as demonstrated by successfully completing the performance requirements will determine the grade for this course. Students must follow directions and assignment requirements in the syllabus.

Grading Scale:

The following chart shows the percentages of points awarded to the letter grade for Undergraduate and Graduate grades.

Undergraduate Scoring

 

Graduate Scoring

 

Numerical Points

Letter Grade

Numerical Points

Letter Grade

100-94

A

100-94

A

93-90

A-

93-90

A-

89-87

B+

89-87

B+

86-83

B

86-83

B

82-80

B-

82-80

B-

79-77

C+

79-77

C+

76-73

C

76-73

C

72-70

C-

72-0

F

69-67

D+

 

 

66-63

D

 

 

62-0

F

 

 

 

Northcentral Grading Rubric:

The grading of each assignment is based on the percentages in the Northcentral Grading Rubric: 70% content and 30% presentation. The percentage is calculated by dividing the actual points earned by the total number of points possible for an activity, with the resulting percentage determining the letter grade for the activity or course. View the Northcentral Grading Rubric.

Exceptions to the Rubric:

Certain courses/activities do not warrant a written product. Examples include math courses involving solving equations or courses that contain multiple choice exams. In these cases, the writing portion of the rubric does not apply. Scoring for these courses will be based on how many items were answered correctly out of the total number of items possible.

 

Course Overview

Section 1: Review of Research Methods, Basic Statistics, and the Fundamentals of SPSS
Activity 1:   Reviewing Research Methods & Basic Statistics   (5 Points)
Activity 2:   Entering Data in SPSS   (5 Points)
Activity 3:   Exploratory Data Analysis   (10 Points)
Section 2: Understanding Assumptions and Common Statistical Strategies – Correlation, Regression, and Comparing Means
Activity 4:   Understanding and Exploring Assumptions   (10 Points)
Activity 5:   Correlation and Regression   (10 Points)
Activity 6:   t test and ANOVA   (10 Points)
Section 3: Advanced Statistical Techniques
Activity 7:   ANCOVA & Factorial ANOVA   (10 Points)
Activity 8:   Repeated-Measures   (5 Points)
Activity 9:   Non-Parametric Tests   (10 Points)
Activity 10:   MANOVA and Reflection   (10 Points)
Activity 11:   Signature Assignment   (15 Points)
Section 1: Review of Research Methods, Basic Statistics, and the Fundamentals of SPSS

Please note: the SPSS software has recently been renamed PASW. However, references in this course will retain the SPSS name.

Course Basics

The Field text includes a companion website that contains sample data files, flash movies, podcasts, self-assessment questions, flashcard glossary, additional materials, answers, etc. You can access the companion site at: http://www.sagepub.com/field3e/

Self-Tests – Embedded within each chapter, you will see an icon and the label SELF-TEST (http://www.sagepub.com/field3e/additionalwebmaterial.htm). These are questions that can quickly assess your mastery of the material just covered. Answers to all self-tests are available on the companion website under the heading: Additional Web Material in the Student Resource section.

Multiple Choice Questions—The companion web site (http://www.sagepub.com/field3e/MCQ.htm) contains quizzes under the heading Interactive MCQs (multiple choice questions). You are encouraged to use these quizzes to assess your mastery of material in each chapter.

Flashcard Glossary - The companion web site (http://www.sagepub.com/field3e/Flashcard.htm) contains a flashcard glossary to assist in reviewing key concept for each chapter.

Flash SPSS Movies—Need to obtain a little more help about using SPSS and entering data? The companion web site (http://www.sagepub.com/field3e/SPSSstudentmovies.htm) contains flash movies to guide you in the use of the SPSS software.

Tips for a Successful Statistics Course
Preparing to complete an online intermediate statistics course may cause you some anxiety. However, it is important to realize that this course is critical to the successful completion of your PhD program. Below are some tips on how to not only make it through this course, but enjoy the journey.

1. Keep a positive attitude. Believe it or not, this can be fun. The text for this course was chosen based on exceptional reviews by other statistics students. You may find it is somewhat unconventional. The text uses images/icons, provides a wealth of SPSS output examples, and has an extensive companion website. While many of the examples/stories used in the text are targeted to a 20-30 something age group, the text is easy to read and highly understandable.

2. Make sure you have all the required materials prior to the first day of the course. The course is fast-paced, so if you are not ready to “hit the ground running”, you will likely find yourself short of time at the end of the course. Take time to read the syllabus, browse the text, install the software and download data sets from the companion site, as well as browse the companion website. Spending 1 or 2 hours familiarizing yourself with the course materials, setting up short cut icons on your desktop and developing an organizational system will save you time when completing the first activities (and the first activities are somewhat extensive).

3. Clear your schedule (some find they must read the material two or three times before it really sinks in). If you have struggled with statistics in the past, please do yourself a favor and limit your non-academic commitments during this course.

4. Stay on schedule! Falling behind is OK from time to time in some courses. This is not true for statistics.
5. If you find yourself at risk of falling behind please contact your Mentor as soon as possible. Your Mentor is your advocate and here to assist you in mastering this material.

6. If you feel terribly confused, consider a tutor. Northcentral University offers free real-time tutoring (you can access the SMARTHINKING tutoring service via the Writing Center). Or, you may have a learning style that benefits from having statistics explained “in-person” – if this is true, locate a tutor in your home town (local colleges and universities can often assist you in locating a tutor).

Section Overview
Section 1 of this course will review research methodology, basic statistics, and the fundamentals of SPSS. This section provides the foundation for the rest of the course.

Many people pursuing a PhD come into their program with an area of interest that they will explore during their dissertation, while others are less clear regarding their possible dissertation topic. You are not expected to know what you will do for your dissertation at this point in your program, but if you have not settled on a general area, now is a good time to consider a viable one. Throughout this course you will be asked to consider your general area of research interest as you complete the activities. Some examples of general areas include: leadership, organizational behavior, market research, and organizational culture.

All quantitative research will assess variables related to your hypotheses. Example of such variables include: age, gender, hours of physical activity per week, type of illness, social support, organizational culture, work satisfaction, stress, anxiety, burnout, etc.

Once you decide what and how to assess your variables of interest, you will need to not only describe the data you collect, but use the data to make inferences about a population.

This section will answer questions like: When do you report a median rather than a mean score? What does the standard deviation say about my sample? Are my data normally distributed? What does it mean to say something is significant? And, probably most importantly: Why do I need to know and understand statistics?

Although much of the mathematics behind descriptive data techniques is quite simple, this does not minimize their importance. Descriptive statistical analysis is often the starting point for more advanced statistical techniques. Such statistics are useful in summarizing various aspects of a data set. When it’s time to analyze your dissertation data, for example, it can be quite illuminating to look at things like measures of central tendency, standard deviations, and other descriptive measures. Even in more advanced classes, such as this course, it is important to start with a review of descriptive statistical techniques as they will be a required part of every activity (as well as your dissertation research results). Finally, after reviewing basic research methods and descriptive statistics, you will practice entering data into SPSS.

Thus, while this section should be a refresher, it contains a fair amount of information. If it has been a while since your last statistics course, you may find it useful to access the supplemental materials found on the companion website for the Field text.

This section lays the foundation for the rest of the course, so take the time you need to fully understand the concepts covered.

Note: While some may pursue a qualitative dissertation, much of the research in the field of business is based on quantitative research. Thus, whether your dissertation is rooted in a quantitative or qualitative tradition, you must understand the concepts taught in this course to understand much of the published research in the field.

Course Resources
The Resources area for this course contains a variety of reference materials that may help you to complete the course Activities. It is suggested that you become familiar with these resources before you begin the Activities.

NCU Library
References used for research need to be peer reviewed/scholarly journals which can be found by searching the NCU Library databases. These journals typically have the following characteristics:
- Articles are reviewed by a panel of experts before they are accepted for publication.
- Articles are written by a scholar or specialist in the field.
- Articles report on original research or experimentation.
- Articles are often published by professional associations.
- Articles utilize terminology associated with the discipline.

NCU Writing Center
NCU values your progress and success as a scholarly writer. Please access the NCU Writing Center from your Learner home page to see a wide variety of writing tips and examples to help you as you compose written submissions for this and other NCU courses.

The Writing Center also contracts with SmartThinking, an online 24/7 tutoring service that offers assistance in mathematics, statistics, finance, and writing. You can contact SmartThinking from the home page of the NCU Writing Center.

NCU Dissertation Center
The Dissertation Center is a valuable reference area for research methods and products specific to NCU standards. You will find a rich variety of resources that will help you through the scholarly research process, as well as a complete collection of dissertations written by NCU Ph.D. Learners.

4-Course Work

Required Reading:
Discovering Statistics Using SPSS: Preface, How to Use This Book, Chapters 1, 2, 3, 4

Self-Tests
Smart Alex's Quizzes
SPSS Movies:
- Entering Data
- The Syntax Window
- The Viewer Window
- Exporting SPSS Output into Word
- Editing Graphs

SPSS Data Sets:
Downloadfestival.sav
Chickflick.sav
Hiccups.sav
Textmessages.sav
ExamAnxiety.sav

Optional Resources:
Reliability and Validity video
Interactive Multiple Choice Questions
Flashcards

Activity 1:   Reviewing Research Methods & Basic Statistics   (5 Points)
4Basic Research Methods and Statistical Concepts
In Activity #1 you will brush up on basic research methods and statistical concepts. You will be asked to articulate the area of research you plan to explore for your dissertation research and create some associated hypotheses. You will also review concepts like statistical significance, probability, reliability, validity, sample versus population, and Type I and Type II error. While these concepts should be ones have learned before, please take the time to reorient yourself to the field of statistics. The basic are the foundation to this course.

To Prepare for Activity #1:
Read the Preface, How to Use This Book, and Chapters 1 and 2 in the text. Think about your biases/preconceived notions regarding statistics. Consider your area of research interest and the types of constructs/variables related to your topic that could be assessed quantitatively (i.e. hours of exercise, depression level, etc.). Pay careful attention to the section on statistical significance the following interpretation of meaningfulness. If something is significant, is it necessarily important?

Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapters and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

Optional Preparation for Activity #1
After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from the following activities those that will assist you in mastering the core concepts.

Video. The following is a video on reliability and validity:
http://www.youtube.com/watch?v=56jYpFkdqW8

Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

Activity #1
You will submit one file for this Activity, a Word document. Please limit each response to 250 words or less, noting than many questions can be answered in 100 words or less.

1. Briefly describe your area of research interest (1-3 sentences is sufficient).


2. List 4 variables that you might assess in a research project related to your research area. List one for each type of measurement scale: Nominal, ordinal, interval, and ratio. If you cannot think of a variable for each measurement scale, explain why the task is difficult.


3. Create one alternate hypothesis and its associated null hypothesis related to your research area.


4. Briefly describe whether you think your area of interest is more conducive to experimental or correlational research. What are the costs/benefits of each as they relate to your research area?


5. Reliability vs. Validity. Considering your area of research interest, discuss the importance of reliability and validity. Can you have one without the other? Why or why not?


6. Sample vs. Population. Considering your area of research interest, describe the difference between a sample and population. Why is it important to understand the difference between a sample and population in a statistics course?


7. Measures of Central Tendency.

Below is a set of data that represents weight in pounds for a particular sample. Calculate the mean, median and mode. Which measure of central tendency best describes this data and why? You may use Excel, SPSS, some other software program, or a hand calculator for this problem.


110.00

117.00

120.00

118.00

104.00

100.00

107.00

115.00

115.00

115.00

114.00

100.00

117.00

115.00

103.00

105.00

110.00

115.00

250.00

275.00


8. Measures of dispersion. For the data set above, calculate the range, the interquartile range, the variance, and the standard deviation. What do these measures tell you about the “spread” of the data? Why is it important to spend time performing basic descriptive statistics prior to conducting inferential statistical tests?


9. Descriptive Statistics. Why is it important to perform basic descriptive statistics prior to conducting inferential statistical tests?


10. Statistical Significance. Revisit the hypotheses you created above in #5. If you conducted a statistical test based on these hypotheses and found a statistically significant result, what would that mean from both a statistical and practical standpoint? (be sure to use the phrases “null hypothesis” and “effect size” in your answer).


11. Type I and Type II Error. The concept of Type I and Type II Error is critical and will come into play not only with each and every statistical test you perform, but when you are asked to conduct an a priori power analysis for your Dissertation Proposal. Considering your answer to #10, discuss the implications of making both a Type I and Type II error.


12. After completing Activity #1, are there any areas of concern you have that you would like to share with your Mentor?


Submit your file in the Course Work area below the Activity screen.

Learning Outcomes: 1, 2, 3, 7
  • Interpret research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
  • Develop appropriate null and alternative hypotheses given a research question.
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Analyze the assumptions required for valid inferential tests.

  • Activity 2:   Entering Data in SPSS   (5 Points)
    4Fundamentals of SPSS
    This activity reviews the fundamental of SPSS. From this point forward SPSS will be used in most activities, so take the time to learn (or refresh) yourself on the basics. Spending time learning how to navigate SPSS will save you a ton of time (and frustration) later. Even if you have no intention of conducting quantitative analyses for your dissertation research, it is expected that PhD graduates from a Business program will have a working knowledge of SPSS. Even if you are not very excited about learning (or relearning) a software program (especially one that has a history of not being overly user friendly) think of this as an opportunity to gain a skill worthy of being showcased on your resume. SPSS has worked hard on becoming more intuitive and the resources available through your text set the stage for SPSS becoming your friend (really!).

    To Prepare for Activity #2:
    Install SPSS and the data sets on your computer. If you have not yet installed SPSS on your computer, please do it now.

    Read Chapter 3 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 3. With each section, explore the exercises in SPSS so that you become familiar with the interface. Feeling comfortable in the SPSS environment will go far in your successful completion of this course.

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    View SPSS Movies. View the first three SPSS movies: Entering Data, The Syntax Window, The Viewer Window. These movies are available on the companion web site at: http://www.sagepub.com/field3e/SPSSstudentmovies.htm

    Optional Preparation for Activity #2
    After completing the above activities, if you feel you need for additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #2
    You will submit a total of three files for this activity: two SPSS data files and one Word document.

    Part A. Creating a Data File. Open a data file in SPSS and enter the data presented in Table 3.1 on page 72. Name this SPSS data file as LastnameFirstinitialSTAT8028-2a

    Part B. Create a mock research project. Submit your answers to the three questions below in a Word doc. Name this SPSS data file as LastnameFirstinitialSTAT8028-2b

    1. Considering your area of research interest, briefly state your area and a possible research project related to the area (150-500 words)


    2. Pose one or more null and alternative hypotheses that follow from the possible research project.


    3. List at least 10 variables that would be collected in your mock research project that would be used to answer the hypotheses. After each variable list the variable name you will use in SPSS (Part C), the level of measurement (binary, nominal, ordinal, interval, or ratio), and the possible range of scores. Feel free to be creative.


    Part C. Create a mock SPSS data set. Name this SPSS data file as LastnameFirstinitial STAT8028-2c

    1. Open a data file in SPSS and enter in a set of mock data for the research project you describe in Part B. (Note: It is important that you do not collect real data for this activity; you cannot collect data without IRB approval).


    2. You must enter in 10 rows of data for all 10 variables (that is, create data for 10 mock participants).


    3. Participant #1 must have missing data for Variable #3. Ensure this is coded correctly.


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 1, 10
  • Interpret research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 3:   Exploratory Data Analysis   (10 Points)
    4Exploratory Data Analysis
    Prior to conducting statistical tests that will evaluate your hypotheses, it is imperative to do what can be described as exploratory data analysis (EDA). Essentially, this entails visually examining your data and exploring, at a high level, any relationships intrinsic to the data. The end result is a comprehensive understanding of your data – this is a must prior to doing any hypothesis testing. Please remember this when you get to your dissertation. Spending time getting to know your data will expedite completion of your results sections.

    To Prepare for Activity #3:
    View the SPSS Movie 4: Exporting SPSS Output into Word. It will be helpful for you to view this prior to reading the chapter, as understanding how to export output as you go will save you time in the completion of Activity #3. This movie is available on the companion web site at: http://www.sagepub.com/field3e/SPSSstudentmovies.htm

    Download SPSS Data Sets. The visual displays you will be asked to create as part of Activity #3 are ones you will work through in this chapter. If you have not yet done so, download the zip folder of SPSS data from the companion web site. Open the Zip file and save the data sets, as you need them, to your computer (or you can right click on the zip file and select “extract all”). This activity requires the following data sets:

    • Downloadfestival.sav

    • Chickflick.sav

    • Hiccups.sav

    • Textmessages.sav

    • ExamAnxiety.sav


    Read Chapter 4 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 4. While you are reading through this chapter and creating visual displays of data, consider the importance of understanding data at this basic level.

    View the SPSS Movie 8: Editing Graphs. This movie will help you understand how to edit your graphs. This movie is available on the companion web site at: http://www.sagepub.com/field3e/SPSSstudentmovies.htm

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #3
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #3
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word.

    Part A. Creating Visual Displays of Data. For this activity you will copy and paste output you created while working in Chapter 4 into a Word document. Please read the instructions below to ensure you are pasting the correct material into your activity document (this chapter has you create many charts and not all are required for Activity #3).

    1. Using the data set: DownloadFestival.sav, create a boxplot for males and females for the variable Day1. It is important that you change the outlier identified to 2.02 prior to creating the boxplot. Be sure to save the data set with a new name, indicating it is the corrected data set (outlier identified and corrected). Save this boxplot, with an appropriate title in your Activity #3 Word document.


    2. Using the data set: ChickFlick.sav, create a clustered bar chart for independent means. The variables you will use are: Arousal, Film, and Gender (grouping variable). Be sure to display error bars and save your chart with an appropriate title in your Activity #3 Word document.


    3. Using the data set: Hiccups.sav, create a clustered bar chart for related means. The variables you will use are: Baseline, Tongue Pulling, Carotid Artery Massage, Digital Rectal Massage. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your Activity #3 Word document.


    4. Using the data set: Text Messages.sav (note: you may see an additional data set with the same name: TextMessages.sav – either will create the correct output), create a clustered bar chart for mixed designs. The variables you will use are: Time1, Time2, and Group. Be sure to display error bars, include labels for the X- and Y-axis, and save your chart with an appropriate title in your Activity #3 Word document.


    5. Using the data set: Exam Anxiety.sav, create a scatterplot that includes a regression line. The variables you will use are: Exam Performance and Exam Anxiety. Be sure to include the regression line and save your chart with an appropriate title in your Activity #3 Word document.


    Part B. Why Exploratory Data Analysis?
    Write a short paragraph that highlights your understanding of why exploratory data analysis is a critical part of any analytical strategy (500 Word limit). This answer is worth 5 points (half the assigned points for this activity). To receive full credit you must show a high level of understanding the importance of exploring data visually.

    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 3, 4, 10
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Section 2: Understanding Assumptions and Common Statistical Strategies – Correlation, Regression, and Comparing Means

    This section begins with exploring assumptions and why they are important (and what to do if your data do not meet required assumptions). Prior to conducting statistical tests you examine your dataset to ensure that it does not violate the assumptions upon which the intended tests are based. Using the procedures outlined in Section 1, you may already have a good idea about your dataset with regard to the necessary assumptions, however, in this section we will formalize the evaluation of these assumptions. In your dissertation it will be expected that you both understand and acknowledge assumptions, and that you are able to make modifications in your proposed analytical strategy, as necessary.

    Once a firm understanding of assumptions related to statistical tests is gleaned, we jump into actually performing and interpreting common statistical tests; now the fun really begins!

    The tests covered in this section include:

    Correlation. Are two variables related? If so, how? A correlation tells you how and to what extent two variables are linearly related. A Correlation coefficient will always fall between -1 and +1 with 0 indicative of no relationship between the variables. Rule of thumb effect sizes are as follows: Small (+.1), Medium (+.3) and Large (+.5), although these effect sizes should always be evaluated relative to research. An important point to remember: correlation does not equal causation!

    Regression. A regression analysis is very similar to a correlation, but is the framework commonly used when one wants to predict one variable from another. For example: How much variance in happiness scores are predicted by hours of physical activity performed each week? With the simple regression framework you have one predictor variable and one outcome variable and the outcome variable is measured on a continuous scale (soon you will learn how multiple regression can handle multiple predictor variables simultaneously).

    Logistic Regression. A logistic regression is the framework one would use for prediction when the outcome variable is categorical. For example: Do numbers of hours spent in voluntary corporate training during the first year of employment predict whether an employee is still at the company in two years (yes/no).

    Comparing Means and ANOVA. While many questions can be answered by correlation and regression, frequently questions require the comparison of mean scores. For example: Are standardized test scores higher in a school that uses one reading method compared to another? Do men or women reap a greater benefit, in terms of pounds lost, from a certain exercise program? Questions that compare two groups can be answered with a simple t-test. An Analysis of Variance (ANOVA) can handle designs that compare more than two groups, like: Does Drug A, B, or C result in better life expectancies for people diagnosed with cancer? Or does Diet A, B, C, or D result in better cholesterol levels?

    A lot of information is covered in these chapters, so please plan accordingly. Also, pay attention to how these techniques are fundamentally similar – it seems like a ton of information, but if you master the statistical models at this level the rest of the course will be a breeze (well, nearly a breeze).

    Activities #5 and #6 simply hit the high points, but you are expected to have gained an understanding of all analyses presented in the text. That is, should you require the use of an analytical strategy covered in the text but not performed in the Activity for your dissertation, you will have the core competencies to perform these alternative techniques.

    A note about statistical significance (what it means/does not mean).

    Most everyone appreciates a refresher on this topic.

    Statistical Significance: An observed effect that is large enough we do not think we got it on accident (that is, we do not think that the result we got was due to chance alone).

    How do we decide if something is statistically significant?
    If H0 is true, the p-value (probability value) is the probability that the observed outcome (or a value more extreme than what we observe) would happen. The p-value is a value we obtain after calculating a test statistic. The smaller the p-value, the stronger the evidence against the H0. If we set alpha at .05, then the p-value must be smaller than this to be considered statistically significant; if we set alpha at .01, then it must be smaller than .01 to be considered statistically significant. Remember, the p-value tells us the probability we would expect our result (or one more extreme) GIVEN the null is true. If our p-value is less than alpha, we REJECT THE NULL HYPOTHESIS and say there appears to be a difference between groups/a relationship between variables, etc.

    Conventional alpha (a) levels
    p < .05 and p < .01
    What do these mean?
    p < .05 = this result would happen no more than 5% of the time (so 1 time in 20 samples), if the null were true.
    p < .01 = this result would happen no more than 1% of the time (so 1 time in 100 samples), if the null were true.
    Because these are low probabilities (events not likely to happen if the null were true), we reject the null when our calculated p-value falls below these alpha levels.

    If the p-value is greater than alpha, you fail to reject the null. You never accept the null, simply fail to reject it. Failure to reject the null as false does not prove that it is true. It means simply that there is insufficient evidence to determine if the null if false or not; further research might be indicated.

    What if your p-value is close to alpha, but slightly over it (like .056)? You cannot reject the null. However, you will want to look at your effect size to determine the strength of the relationship and also your sample size. Often, a moderate to large effect will not be statistically significant if the sample size is low (low power). In this case, it suggests further research with a larger sample.

    Please remember that statistical significance does not equal importance. You will always want to calculate a measure of effect size to determine the strength of the relationship. Another thing to keep in mind is that the effect size, and how important it is, is somewhat subjective and can vary depending on the study at hand.

    Required Reading:
    Discovering Statistics Using SPSS: Preface, How to Use This Book, Chapters 5, 6, 7, 8, 9, 10

    Self-Tests
    Smart Alex's Quizzes

    SPSS Data Sets:
    Downloadfestival.sav
    SPSSExam.sav
    Chickflick.sav
    Chamorro-Premuzic.sav
    Activity6a.sav
    Activity6c.sav

    Optional Resources:
    Interactive Multiple Choice Questions
    Flashcards

    Activity 4:   Understanding and Exploring Assumptions   (10 Points)
    4Evaluation of Assumptions
    In Activity #3, you used SPSS to create visual representations of your dataset. As you will see in Activity #4, each statistical procedure that you will use is based on one or more assumptions about the dataset. Prior to conducting statistical tests that will evaluate your hypotheses, you need to check your dataset to ensure that it does not violate the assumptions upon which the intended tests are based. Using the procedures outlined in Activity #3, you may already have a good idea about your data set with regard to the necessary assumptions. Now we will formalize the evaluation of these assumptions.

    To Prepare for Activity #4:
    Download SPSS Data Sets. The visual displays you will be asked to create as part of Activity #4 are ones you will work through in this chapter.

    You will need to download the following data sets:

    • Downloadfestival.sav

    • SPSSExam.sav

    • Chickflick.sav


    Read Chapter 5 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 5. While you are reading through this chapter and testing the assumptions of various statistical procedures, consider various types of datasets and whether they would run the risk of violating these assumptions.

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #4
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #4
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word.

    1. Why do we care whether the assumptions required for statistical tests are met? (You might want to write your answer on a note card you paste to your computer).


    2. Open the data set that you corrected in activity #3 for DownloadFestival.sav. You will use the following variables: Day1, Day2, and Day3 (hygiene variable for all three days). Create a simple histogram for each variable. Choose to display the normal curve (under Element Properties) and title your charts. Copy these plots into your Activity #4 Word document.


    3. Now create probability-probability (P-P) plots for each variable. This output will give you additional information. Read over the Case Processing Summary. Notice that there is missing data for Days 2 and Day 3? Copy only the Normal P-P Plots into your Activity #4 Word document (you do not need to copy the beginning output nor the Detrended Normal P-P Plots).


    4. Examining the histograms and P-P plots describe the dataset, with particular attention toward the assumption of normality. For each day, do you think the responses are reasonably normally distributed? (just give your impression of the data). Why or why not?


    5. Using the same dataset, and the Frequency command, calculate the standard descriptive measures (mean, median, mode, standard deviation, variance and range) as well as kurtosis and skew for all three hygiene variables. Paste your output into your Activity #4 Word document (you do not need to paste the Frequency Table). What does the output tell you? You will need to comment on: sample size, measures of central tendency and dispersion and well as kurtosis and skewness. You will need to either calculate z scores for skewness and kutosis or use those given in the book to provide a complete answer. Bottom line: is the assumption of normality met for these three variables? Does this match your visual observations from question #2?


    6. Using the dataset SPSSExam.sav, and the Frequency command, calculate the standard descriptive statistics (mean, median, mode, standard deviation, variance and range) plus skew and kurtosis, and histograms with the normal curve on the following variables: Computer, Exam, Lecture, and Numeracy for the entire dataset. Complete the same analysis using University as a grouping variable. Paste your output into your Activity #4 Word document (you do not need to paste the Frequency Table). What do the results tell you with regard to whether the data is normally distributed?


    7. Using the dataset SPSSExam.sav, determine whether the scores on computer literacy and percentage of lectures attended (with University as a grouping variable) meet the assumption of homogeneity of variance (use Levene’s test). You must remember to unclick the “split file” option used above before doing this test. What does the output tell you? (be as specific as possible).


    8. Describe the assumptions of normality and homogeneity of variance. When these assumptions are violated, what are your options? Are there cases in which the assumptions may technically be violated, yet have no impact on your intended analyses? Explain.


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 3, 4, 8, 10
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 5:   Correlation and Regression   (10 Points)
    4Common Analytical Strategies
    In Activity #4 you gained an understanding of the importance of examining assumptions prior to conducting statistical analyses that test hypotheses. In this activity you will move from descriptive analyses and the examination of assumptions to actually conducting such analyses. We will cover common analytical strategies : Correlation and Regression.

    Correlation is a method used to express the relationship between two variables – that is, as one variable changes, how does the other? For example, you might be interested in studying whether there is a relationship between leadership styles and organizational effectiveness.

    Regression is a method that uses one variable to predict another (continuous) variable. So, perhaps you are interested in studying stress and want to know if the number of hours spent in yoga can explain a significant amount of variance in stress scores. A simple regression can answer this question for you. For most research questions, you will want to add in other explanatory variables, like number of hours at work each week, how much caffeine they drink, personality style, etc. We will learn about multiple regression soon and mastering these simple techniques will lay a solid foundation for the more advanced ones (so don’t short change yourself on this activity).

    Finally, this activity covers three chapters, so plan accordingly.

    To Prepare for Activity #5:
    Download SPSS Data Sets. The visual displays you will be asked to create as part of Activity #5 are ones you will work through in this chapter.

    You will need to download the following data set:

    • Chamorro-Premuzic.sav


    Read Chapters 6, 7 and 8 in the text. It will be to your advantage to have SPSS open on your computer as you work through these chapters. While you are reading consider your area of research interest and when you have seen correlation and regression applied. How might you use these analytical strategies in your dissertation research?

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapters and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #5
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #5
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word. Please answer the questions first and include all output at the end of the activity in an Appendix.

    Part A. SPSS Activity
    Part A of Activity #5 has you really getting to know a set of data and allows you the opportunity to perform statistical tests and then interpret the output. You will rely on all you have learned to this point and add correlation and regression strategies to your tool kit.
    Using the data set: Chamorro-Premuzic.sav you will focus on the variables related to Extroversion and Agreeableness (student and lecturer).

    Do the following:

    1. Exploratory Data Analysis.

    a. Perform Exploratory Data Analysis on all variables in the data set. Because you are going to focus on Extroversion and Agreeableness, be sure to include scatterplots for these combinations of these variables (Student Agreeableness/Lecture Agreeableness; Student Extroversion/Lecture Extroversion; Student Agreeableness/Lecture Extroversion; Student Extroversion/Lecture Agreeableness) and include the regression line on the chart.

    b. Give a one to two paragraph write up of the data once you have done this.

    c. Create an APA style table that presents descriptive statistics for the sample.


    2. Make a decision about the missing data. How are you going to handle it and why?


    3. Correlation. Perform a correlational analysis on the following variables: Student Extroversion, Lecture Extroversion, Student Agreeableness, Lecture Agreeableness.

    a. Ensure you handle missing data as you decided above.

    b. State if you are using one or two-tailed test and why.

    c. Write up the results APA style and interpret them.


    4. Regression. Calculate a regression that examines whether or not you can predict if a student wants a lecturer to be extroverted using the student’s extroversion score.

    a. Ensure you handle missing data as you decided above.

    b. State if you are using one or two-tailed test and why.

    c. Include diagnostics

    d. Discuss assumptions; are they met?

    e. Write the results in APA style and interpret it.

    f. Does this result differ from the correlation result above?


    5. Multiple Regression. Calculate a multiple regression that examines whether age, gender, and student’s extroversion and predict if a student wants the lecturer to be extroverted.

    a. Ensure you handle missing data as you decided above.

    b. State if you are using one or two-tailed test and why.

    c. Include diagnostics

    d. Discuss assumptions; are they met?

    e. Write the results in APA style and interpret it.

    f. Does this result differ from the correlation result above?


    Part B. Applying Analytical Strategies to an Area of Research Interest
    1. Briefly restate your research area of interest.

    a. Pearson Correlation. Identify two variables for which you could calculate a Pearson correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a Pearson correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding in APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.


    b. Spearman’s Correlation. Identify two variables for which you could calculate a Spearman’s correlation coefficient. Describe the variables and their scale of measurement. Now, assume you conducted a correlation and came up with a significant positive or negative value. Create a mock r value (for example, .3 or -.2). Report your mock finding APA style (note the text does not use APA style) and interpret the statistic in terms of effect size and R2 while also taking into account the third variable problem and well as direction of causality.


    c. Partial Correlation vs. Semi-Partial Correlation. Identify three variables for which you may be interested calculating either a partial or semi-partial correlation coefficient. Compare/contrast these two types of analyses, using your variables and research example. Which would you use and why?


    d. Simple Regression. Identify two variables for which you could calculate a simple regression. Describe the variables and their scale of measurement. Which variable would you include as the predictor variable and which as the outcome variable? Why? What would R2 tell you about the relationship between the two variables?


    e. Multiple Regression. Identify at least 3 variables for which you could calculate a multiple regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would R2 and adjusted R2 tell you about the relationship between the variables?


    f. Logistic Regression. Identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 3, 4, 5, 6, 8, 10, 11
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 6:   t test and ANOVA   (10 Points)
    4Conducting Tests
    In Activity #5 you learned how to examine relationships between variables, conduct analyses related to correlation and regression, and interpret the output associated with each.

    In this section you will learn how to conduct tests that determine if there are differences between mean scores for groups. For example, you might be interested in studying whether there are mean differences in heart rate between two groups: those who exercise and those who do not (t-test), or you might a slightly more complicated design and compare mean heart rates for three groups: non-exercisers, occasional exercisers, and regularly exercisers (ANOVA).

    To Prepare for Activity #6:
    Download SPSS Data Sets.

    • Activity 6a.sav (found on the “additional resources” page)

    • Activity 6c.sav (found on the “additional resources” page)

    NOTE: You may experience an error message when attempting to run the analysis using SPSS of the .sav file used in this assignment. The error message says:

    Warnings
    Command name: DESCRIPTIVES
    Input error when reading a case.
    This command not executed.

    If you experience this error, click on the data view tab of the opened .sav file, then click on the line separating the labels of the first and second column. Drag the width of the first column out approximately 25% from its initial width. Save the file. The analysis should now work as intended.

    Read Chapters 9 and 10 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapters 9 and 10. While you are reading through this chapter and testing the assumptions of various statistical procedures, consider various types of datasets and whether they would run the risk of violating these assumptions.

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #6
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #6
    You will submit one Word document and one SPSS data file for this activity. You will create the Word document by cutting and pasting SPSS output into word. Please read the instructions below to ensure you are pasting the correct material into your activity document. The Word document will be named LastnamefirstinitialSTAT8028-6a and the SPSS document as -6b.

    Part A. Dependent t-test
    In this activity, we are interested in finding out whether participation in a creative writing course results in increased scores of a creativity assessment. For this part of the activity, you will be using the data file “Activity 6a.sav”. In this file, “Participant” is the numeric student identifier, “CreativityPre” contains creativity pre-test scores, and “CreativityPost” contains creativity post-test scores. A total of 40 students completed the pre-test, took the creativity course, and then took the post-test.

    1. Exploratory Data Analysis/Hypotheses.

    a. Perform exploratory data analysis on CreativityPre and CreativityPost. Using SPSS, calculate the mean and standard deviation of these two variables.

    b. Construct an appropriate chart/graph that displays the relevant information for these two variables.

    c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).


    2. Comparison of Means

    a. Perform a dependent t-test to assess your hypotheses above (note that many versions of SPSS use the term “paired samples t-test” rather than dependent t-test; the test itself is the same.

    b. Write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t-test. Be sure that your writing conforms to APA style.


    Part B. Independent t-test
    In this activity, we will start with the data file used in Part A (“Activity 6a.sav”). Suppose, however, you [the researcher] encountered a small problem during data collection: after the post-tests were collected, you realized that the post-test form did not ask for the students’ identification number. As such, it will be impossible to match pre-test scores to post-test scores. Rather than simply give up, you start thinking about the data you do have, and try to determine whether you can salvage your project. In assessing the situation, you realize that you have 40 pre-test scores and 40 post-test scores, but no way to link them. While it will result in a weaker comparison, you determine that you are still able to compare pre-test vs. post-test scores; you will use a between-subjects design rather than a within-subjects design.

    1. Create the data set.

    a. Using the “Activity 6a.sav” file as a starting point, create a new dataset that you can use with the between subjects design. Hint: you will no longer need the variables CreativePre and CreativeTest. Instead, you have only one variable for the score on the creativity test. A second (or grouping) variable will serve to indicate which test the student took.

    b. Submit the dataset as one of the Activity 6 files.


    2. Exploratory Data Analysis/Hypotheses.

    a. Perform exploratory data analysis on CreativityPre and CreativityPost. Using SPSS, calculate the mean and standard deviation of these two variables.

    b. Construct an appropriate chart/graph that displays the relevant information for these two variables.

    c. Write the null and alternative hypotheses used to test the question above (e.g., whether participation in the course affects writing scores).


    3. Comparison of Means

    a. Perform an independent t-test to assess your hypotheses above (note that many versions of SPSS use the term “independent samples t-test” rather than simply “independent t-test”.

    b. Write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample t-test. Be sure that your writing conforms to APA style.


    4. Comparison of Designs

    a. In this activity you used the same dataset to analyze both a between- and within-subjects design. Create a single paragraph (using the material you wrote above), that presents both sets of results.

    b. Explain, in 300-500 words, whether the two tests resulted in the same findings. Did you expect this to be the case? Why or why not? What have you learned in this activity?


    Part C. ANOVA
    All of us have had our blood pressure measured while at our physician’s office. How accurate are these measurements? It may surprise you to learn that there is something called “White coat syndrome”—the tendency of some people to exhibit elevated blood pressure in clinical (medical) settings only. In other words, for these people the very fact that the physician is taking their blood pressure causes it to increase (for more information about white coat syndrome see http://www.webmd.com/anxiety-panic/features/beyond-white-coat-syndrome). In this activity, you will be using the “Activity 6c.sav” data file to determine whether you find support for the existence of white coat syndrome. In this study, 60 participants were randomly assigned to one of three groups. The “settings” variable indicates the location in which the participant’s blood pressure was recorded: 1=home, 2=in a doctor’s office, and 3=in a classroom setting. The “SystolicBP” variable contains the participant’s systolic pressure (the “upper” number). The “DiastolicBP” variable contains the participant’s diastolic pressure (the “lower” number).

    1. Exploratory Data Analysis/Hypotheses.

    a. Perform exploratory data analysis on both the SystolicBP and DiastolicBP variables. Using SPSS, calculate the mean and standard deviation of these two variables. Be sure that your analysis is broken down by setting (e.g., you will have six means, six SD’s, etc.).

    b. Create two graphs—one for systolic and one for diastolic pressure. Each graph should clearly delineate the three groups.

    c. Write a null and alternative hypothesis for the comparison of the three groups (note that your hypothesis will state that the three groups are equivalent; be sure to word your null hypothesis correctly).


    2.ANOVA.

    a. Using the “Activity 6c.sav” data file, perform two single factor ANOVAs: one using SystolicBP and one using DiastolicBP as the dependent variable.

    b. If appropriate for either or both of the ANOVAs, perform post hoc analyses to determine which groups actually differ.

    c. Write one paragraph for each ANOVA (be sure to use APA style). At a bare minimum, each paragraph should contain the three means, three SD’s, ANOVA results (F, df), post hoc tests (if applicable), effect size, and an interpretation of these results.


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 2, 3, 4, 5, 6, 10, 11
  • Develop appropriate null and alternative hypotheses given a research question.
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Section 3: Advanced Statistical Techniques

    Sections 1 and 2 have served to prepare you for the understanding of advanced statistical techniques. This is the section you have been waiting for, but it could not come too prematurely. To introduce these concepts without a solid understanding of research, exploratory data analysis, assumptions, and simple statistical techniques would really make your head spin. In any case, the course is 2/3 over and you should at least briefly pause to celebrate what you have learned and how your perseverance has paid off.

    This section will contain three activities and will cover the following analytical strategies (if it becomes difficult to keep all the techniques you are learning straight, refer to the last page of your text – there is a great table that can help you out):

    ANCOVA. The Analysis of Covariance technique is a life-saver when you are comparing means between defined groups and have an additional variable (or variables) that you would like to ‘control’ for. An example might be: Are mean productivity scores for three groups of work teams different, when you control for length of time on the job? Or: Are depression scores for young, middle, and older adults different after controlling for health, gender, and social support?

    Factorial ANOVA. When you have more than one predictor variable a Factorial ANOVA design might be just what you are looking for. These techniques include Two-way repeated-measures ANOVA, Two-way Mixed ANOVA, Three-way independent ANOVA, and so on. For example: Perhaps you are going to design a social support study for people suffering from chronic pain. Your study includes two treatment groups and control group. Further, you have every reason to believe (based on past research and theory) that men and women will respond differently to the treatment groups. A factorial design can handle such complexities.

    Repeated-Measures. If you are examining multiple groups but the same people belong to each group, you will use a repeated-measures design. For example, instead of randomly assigning people to either Treatment A or Treatment B, if you choose to have all participants in both treatments (of course you would need to consider carry-over effects, practice, and counter balancing, etc.) then you have a repeated-measures design. There are some great advantages to repeated-measures design (key among them the ability to reduce the statistical impact of individual differences).

    MANOVA. With the tests you have learned this far, we have been constrained by one requirement of one outcome variables. A MANOVA allows for a design in which you have groups being compared on multiple outcome variables. For example, if you are interested in comparing men and women and their psychological health. You may have a number of measures that assess the construct of psychological health: depression, life satisfaction, and well-being. A MANOVA allows you to make this comparison with one elegant analysis.

    Non-Parametric Tests. Now that you have learned a number of parametric techniques…what do you do if your data do not meet parametric assumptions? Non-parametric tests to the rescue! Tests covered under this category include: Chi square, Wilcoxon rank-sum test, Mann-Whitney tests, Kruskal-Wallis test for independent conditions and Freidman’s ANOVA for related conditions.

    Once you master these additional techniques (and you are well rested) you will be asked to complete the signature assignment which will give you an opportunity to do research on a set of supplied data.

    Congratulations on completing this graduate level statistics course. You will now have the core competencies related to statistics that will allow you to more fully glean knowledge from your content courses. Statistics is not like riding a bike – if you stop using it, you lose it. So, please do not skip over the results sections in peer reviewed articles…be sure to use all that you have worked so hard for. When you get to your dissertation, you will be glad that you did!

    Required Reading:
    Discovering Statistics Using SPSS: Preface, How to Use This Book, Chapters 11, 12, 13, 15, 16

    Self-Tests
    Smart Alex's Quizzes

    SPSS Data Sets:
    Activity7.sav
    Activity8.sav
    Activity6a.sav
    Activty6b.sav
    Activty6c.sav
    Activity10.sav
    Education.sav

    Gss.sav

    Optional Resources:
    Interactive Multiple Choice Questions
    Flashcards

    Activity 7:   ANCOVA & Factorial ANOVA   (10 Points)
    4Advanced Techniques
    In Activity #6 you learned how to conduct tests that determine if there are differences between mean scores for groups. However, many research questions require more complex designs that include the ability to control for confounding variables and/or include multiple independent variables. For example: You are interested in outcomes for three different leadership styles and want to control for the type of personality. Or: You want to examine attitudes towards a new federal law and believe that political affiliation and gender are relevant factors to consider.

    In this activity you will learn these advanced techniques.

    To Prepare for Activity #7:
    Download SPSS Data Set.

    • Activity 7.sav (found on the “additional resources” page)

    NOTE: You may experience an error message when attempting to run the analysis using SPSS of the .sav file used in this assignment. The error message says:

    Warnings
    Command name: DESCRIPTIVES
    Input error when reading a case.
    This command not executed.

    If you experience this error, click on the data view tab of the opened .sav file, then click on the line separating the labels of the first and second column. Drag the width of the first column out approximately 25% from its initial width. Save the file. The analysis should now work as intended.

    Read Chapters 11 and 12 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapters 11 and 12. While you are reading consider your area of research interest and when you have seen these more advanced ANOVA models applied. How might you use these analytical strategies in your dissertation research?

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #7
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #7
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word. Activity #7 consists of two parts. In the first part, you will utilize an existing dataset to compute a factorial ANOVA. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a hypothetical ANCOVA output table (for variables related to your area of interest).

    Part A. SPSS Activity
    The “Activity 7a.sav” file contains a dataset of a researcher interested in finding the best way to educate elementary age children in mathematics. In particular, she thinks that 5th grade girls do better in small class sizes while boys excel in larger classes. Through the school district, she has arranged a pilot program in which some classroom sizes are reduced prior to the state-wide mathematics competency assessment. In the dataset, you will find the following variables:

    Participant: unique identifier

    Gender: Male (M) or Female (F)

    Classroom:

    Small (1) – no more than 10 children

    Medium (2) – between 11 and 19 children

    Large (3) – 20 or more students

    Score – final score on the statewide competency assessment.


    In Activity #7, do the following:

    1. Exploratory Data Analysis.

    a. Perform exploratory data analysis on all variables in the data set. Realizing that you have six groups, be sure that your exploratory analysis is broken down by group. When possible, include appropriate graphs to help illustrate the dataset.

    b. Give a one to two paragraph write up of the data once you have done this.

    c. Create an APA style table that presents descriptive statistics for the sample.


    2. Factorial ANOVA. Perform a factorial ANOVA using the “Activity 7a.sav” data set.

    a. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).

    b. Is there a main effect of classroom size? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).

    c. Is there an interaction between your two variables? If so, using post hoc tests, describe these differences.

    d. Is there support for the researcher’s hypothesis that girls would do better than boys in classrooms with fewer students? Explain your answer.

    e. Write up the results APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.


    Part B. Applying Analytical Strategies to an Area of Research Interest
    3. Briefly restate your research area of interest.
    Analysis of Covariance. Using your area of interest, identify one independent and two dependent variables, such that the dependent variables would likely be covariates. Now, assume you conducted an ANCOVA that shows both the first independent variable as well as the covariate significantly predicts the dependent variable. Create a mock ANCOVA output table (see SPSS Output 11.3 in your text for an example) that supports the relationship shown above. Report your mock finding APA style.

    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 2, 3, 4, 5, 6, 10, 11
  • Develop appropriate null and alternative hypotheses given a research question.
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 8:   Repeated-Measures   (5 Points)
    4Repeated Measures Designs
    In Activity #7 you learned how to analyze research questions that require complex designs allowing for the ability to control for confounding variables and/or include multiple independent variables. In this Activity you will add to your knowledge of advanced techniques by learning about repeated-measures designs.

    Repeated-measures designs are useful when you have a pre-post design (measuring people prior to and after an intervention, treatment, etc.), or people exposed to all levels of your independent variable. For example: Which of three types of guided imagery is most effective in lowering heart rate?

    In this activity you will learn this technique.

    To Prepare for Activity #8:
    Download SPSS Data Set.

    • Activity 8.sav (found on the “additional resources” page)

    NOTE: You may experience an error message when attempting to run the analysis using SPSS of the .sav file used in this assignment. The error message says:

    Warnings
    Command name: DESCRIPTIVES
    Input error when reading a case.
    This command not executed.

    If you experience this error, click on the data view tab of the opened .sav file, then click on the line separating the labels of the first and second column. Drag the width of the first column out approximately 25% from its initial width. Save the file. The analysis should now work as intended.

    Read Chapter13 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 13. While you are reading consider your area of research interest and when you have seen repeated-measures designs applied. How might you use this analytical strategy in your dissertation research?

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #8
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #8
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word. Activity #8 consists of two parts. In the first part, you will utilize an existing dataset to analyze dataset from repeated measure experimental design. All SPSS output should be pasted into your Word document. In the second part, you will be asked to create a dataset for a hypothetical repeated measures experimental design. Finally, you will answer questions about your hypothetical dataset.


    Part A. SPSS Activity
    The “Activity 8a.sav” file contains a dataset of a high school teacher interested in determining whether his students’ test scores increase over the course of a 12 week period. In the dataset, you will find the following variables:

    Participant: unique identifier

    Gender: Male (M) or Female (F)

    Score_0 – score on the initial course pre-test (first day of class)

    Score_2 – score at the end of week 2

    Score_4 – score at the end of week 4

    Score_6 – score at the end of week 6

    Score_8 – score at the end of week 8

    Score_10 – score at the end of week 10

    Score_12 – score at the end of the course (week 12)


    In Activity #8, do the following:

    1. Exploratory Data Analysis.

    a. Perform exploratory data analysis on the relevant variables in the dataset. When possible, include appropriate graphs to help illustrate the dataset.

    b. Give a one to two paragraph write up of the data once you have done this.

    c. Create an APA style table that presents descriptive statistics for the sample.

    2. Repeated Measures ANOVA. Perform a repeated measures ANOVA using the “Activity 8.sav” data set. You will use Score_0 through Score_12 as your repeated measure (7 levels), and gender as a fixed factor.

    a. Is the assumption of sphericity violated? How can you tell? What does this mean in the context of interpreting the results?

    b. Is there a main effect of gender? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case).

    c. Is there a main effect time (i.e., an increase in scores from Week 0 to Week 12)? If so, explain the effect. Use post hoc tests when necessary (or explain why they are not required in this specific case). Examine the output carefully, and give as much detail as possible in your findings.

    d. Write up the results APA style and interpret them. Be sure that you discuss both main effects and the presence/absence of an interaction between the two.



    Part B. Applying Analytical Strategies to an Area of Research Interest

    3. Briefly restate your research area of interest.

    a. Identify at least 2 variables for which you would utilize a repeated measures ANOVA in your analysis. Describe the variables and their scale of measurement. Identify whether each factor is fixed or repeating. Where on the SPSS output would you look to find out if you violated the assumption of sphericity? If the data did violate this assumption, what would the impact be on your analysis?


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 2, 3, 4, 5, 6, 9, 10, 11
  • Develop appropriate null and alternative hypotheses given a research question.
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Demonstrate proficiency in the use of SAS and the reporting of statistical output in APA format.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 9:   Non-Parametric Tests   (10 Points)
    4Non-Parametric Tests
    While you have learned a number of parametric statistical techniques, you are also aware that if the assumptions related to the tests are violated, then the tests are not valid. Because many phenomenon examined in business are not normally distributed, it is critically important to understand the role of non-parametric tests. It is possible you will need to use one or more of the methods covered in this chapter in your dissertation.

    To Prepare for Activity #9:
    Download SPSS Data Sets.

    • Activity 6a.sav (found on the “additional resources” page)

    • Activity 6b.sav (file you created in Activity 6B)

    • Activity 6c.sav (found on the “additional resources” page)

    • Activity 7.sav (found on the “additional resources” page)

    • Gss.sav (found on the “additional resources” page)

    NOTE: You may experience an error message when attempting to run the analysis using SPSS of the .sav file used in this assignment. The error message says:

    Warnings
    Command name: DESCRIPTIVES
    Input error when reading a case.
    This command not executed.

    If you experience this error, click on the data view tab of the opened .sav file, then click on the line separating the labels of the first and second column. Drag the width of the first column out approximately 25% from its initial width. Save the file. The analysis should now work as intended.

    Read Chapter 15 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 15. While you are reading consider your area of research interest and when you have seen non-parametric methods applied. How might you use these analytical strategies in your dissertation research?

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #9
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #9
    You will submit one Word document for this activity. In the first part your activity #9 document, provide short answers to the following questions (250 words or less).

    Part A. Questions about non-parametric procedures

    1. What are the most common reasons you would select a non-parametric test over the parametric alternative?


    2. Discuss the issue of statistical power in non-parametric tests (as compared to their parametric counterparts). Which type tends to be more powerful? Why?


    3. For each of the following parametric tests, identify the appropriate non-parametric counterpart:

    a. Dependent t-test

    b. Independent samples t-test

    c. Repeated measures ANOVA (one-variable)

    d. One-way ANOVA (independent)

    e. Pearson Correlation


    Part B. SPSS Activity
    In this part of Activity #9, you will perform the non-parametric version of the tests you used in Activities 6, 7, and 8. In each case, assume that you opted to use the non-parametric equivalent rather than the parametric test. Using the data files from earlier activities, complete the following tests and paste your results into the assignment Word document:

    1. Activity 6A: non-parametric version of the dependent t-test

    2. Activity 6B: non-parametric version of the independent t-test

    3. Activity 6C: non-parametric version of the single factor ANOVA

    4. Activity 7: non-parametric version of the factorial ANOVA


    Part C. Contingency tables
    Sometimes a researcher is only interested in the following: Whether or not two variables are dependent on one another, (e.g. are death and smoking dependent variables; are SAT scores and high school grades independent variables?)

    To test this type of claim a contingency table could be used, with the null hypothesis being that the variables are independent. Setting up a contingency table is easy; the rows are one variable the columns another. In contingency table analysis (also called two-way ANOVA) the researcher determines how closely the amount in each cell coincides with the expected value of each cell if the two variables were independent.

    The following contingency table lists the response to a bill pertaining to gun control.


     In favor  Opposed 
     Northeast  10  30
     Southeast  15  25
     Northwest  35  10
     Southwest  10  25

    Notice that cell 1 indicates that 10 people in the Northeast were in favor of the bill.


    Example: In the previous contingency table, 40 out of 160 (1/4) of those surveyed were from the Northeast. If the two variables were independent, you would expect 1/2 of that amount (20) to be in favor of the amendment since there were only two choices. We would be checking to see if the observed value of 10 was significantly different from the expected value of 20.

    To determine how close the expected values are to the actual values, the test statistic chi-square is determined. Small values of chi-square support the claim of independence between the two variables. That is, chi-square will be small when observed and expected frequencies are close. Large values of chi-square would cause the null hypothesis to be rejected and reflect significant differences between observed and expected frequencies. This part of the activity is not included in the text book. See the tutorial Chi-square pdf file in the additional resources section of the course room for details on how to perform a chi-square test in SPSS.

    For part C, download the gss.sav file, and following the steps described in the Chi-Square tutorial.pdf (both located in the additional resources section of the course room), examine the relationship between education (degree) and perception of life (life). Can you reject the null that education and perception of life are independent? Make a bar chart that graphically summarizes your findings. Be sure to include the relevant portions of the chi-square test output in your explanation.

    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 5, 6, 7, 9, 10, 11
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Analyze the assumptions required for valid inferential tests.
  • Demonstrate proficiency in the use of SAS and the reporting of statistical output in APA format.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 10:   MANOVA and Reflection   (10 Points)
    4Comparison of Multiple Outcome Variables
    This activity introduces you to a very common technique – MANOVA. MANOVA is simply an extension of an ANOVA and allows for the comparison of multiple outcome variables (again, a very common situation in research and what luck that instead of have to perform a series of analysis one MANOVA can do it all for you!).

    In this activity you will also reflect on all the knowledge you have gained over your time in this course, and final take an ungraded post test. The post test is NCU’s way of assessing how well the course did in teaching you and your fellow doctoral Learners the core competencies in statistics. So, while the test is not graded, please take your time and do your best.

    To Prepare for Activity #10:
    Download SPSS Data Sets.

    • Activity 10.sav (found on the “additional resources” page)

    NOTE: You may experience an error message when attempting to run the analysis using SPSS of the .sav file used in this assignment. The error message says:

    Warnings
    Command name: DESCRIPTIVES
    Input error when reading a case.
    This command not executed.

    If you experience this error, click on the data view tab of the opened .sav file, then click on the line separating the labels of the first and second column. Drag the width of the first column out approximately 25% from its initial width. Save the file. The analysis should now work as intended.

    Read Chapter 16 in the text. It will be to your advantage to have SPSS open on your computer as you work through chapter 16. While you are reading consider your area of research interest and when you have seen a MANOVA framework applied. How might you use these analytical strategies in your dissertation research?

    Complete the Self-Tests within each chapter. Answers are available on the companion web site under the heading Additional Web Material in the Student Resource section (http://www.sagepub.com/field3e/additionalwebmaterial.htm).

    Complete Smart Alex’s Quizzes. Be sure to take Smart Alex’s Quiz at the end of the Chapter and spend time learning the concepts related to questions you answered incorrectly. Answers are available at: http://www.sagepub.com/field3e/SmartAlexAnswers.htm

    Optional Preparation for Activity #10
    After completing the above activities, if you feel you need additional instruction on the concepts covered, please choose from any of the following activities that will assist you in mastering the core concepts.

    Interactive Multiple Choice Questions. You might find it helpful to complete the multiple choice quizzes available at: http://www.sagepub.com/field3e/MCQ.htm

    Flashcards. If what you need is gain a basic, definitional understanding of the topics, visit the Flashcard Glossary at: http://www.sagepub.com/field3e/Flashcard.htm

    Activity #10
    You will submit one Word document for this activity. You will create this Word document by cutting and pasting SPSS output into word.

    Part A. SPSS Activity
    In this exercise, you are playing the role of a researcher that is testing new medication designed to improve cholesterol levels. When examining cholesterol in clinical settings, we look at two numbers: low-density lipoprotein (LDL) and high-density lipoprotein (HDL). You may have heard these called “good” (HDL) and “bad” (LDL) cholesterol. For LDL, lower numbers are better (below 100 is considered optimal. For HDL, 60 or higher is optimal.

    In this experiment, the researcher is testing three different versions of the new medication. In data file “Activity 10.sav” you will find the following variables: group (0=control, 1=Drug A, 2=Drug B, 3=Drug C), LDL, and HDL (cholesterol numbers of participants after 12 weeks).

    Using a MANOVA, try to ascertain which version of the drug (A, B or C) shows the most promise. Perform the following analyses, and paste the SPSS output into your document.

    1. Exploratory Data Analysis.

    a. Perform exploratory data analysis on the relevant variables in the dataset. When possible, include appropriate graphs to help illustrate the dataset.

    b. Give a one to two paragraph write up of the data once you have done this.

    c. Create an APA style table that presents descriptive statistics for the sample.


    2. Perform a MANOVA. Using the “Activity 10.sav” data set perform a MANOVA. “Group” is your fixed factor, and LDL and HDL are your dependent variables. Be sure to include simple contrasts to distinguish between the drugs (group variable). In the same analysis, include descriptive statistics, and parameter estimates. Finally, be certain to inform SPSS that you want post-hoc test to help you determine which drug works test best.

    a. Is there any statistically significant difference in how the drugs perform? If so, explain the effect. Use the post hoc tests as needed.

    b. Write up the results using APA style and interpret them.


    Part B. Reflection
    Reflect on your experience throughout the course. In your Activity #10 document, include a brief assessment of what you have learned. In 2-3 paragraphs, cover the following:

    1. What were the three most important things you learned?

    2. How will the material in this course help you in your dissertation work?

    3. What would you like to have seen covered that wasn’t?


    Submit your files in the Course Work area below the Activity screen.

    Learning Outcomes: 3, 4, 5, 6, 10, 11
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Activity 11:   Signature Assignment   (15 Points)
    4Signature Assignment
    For the final activity, please thoroughly answer each of the questions below. Your grade on this activity will be based on accuracy and comprehensiveness. Your paper should be between 3500-4200 words using APA formatting.

    Review the file education.sav. Using the data contained in this 500 sample data set, synthesize an integrated understanding about education in four different areas. In this assignment, you will need to examine the data, determine the appropriate test method being sure that the conditions required for that method have been met, perform the analysis, then interpret the results. Synthesize your findings into an integrated report. Be sure to support your position with data and the appropriate statistical tests as needed. Locate two peer review journal articles that deal with each question; compare and contrast your findings with the peer review research. Prepare a paper suitable for submission to a non-statistician academic conference on adult education using graphs, tables, and figures as necessary while still maintaining appropriate academic rigor. Place all relevant statistical output in an appendix.

    1. What is the relationship, if any between education and gender? Discuss any differences that may exist and describe the characteristics of the sample.


    2. What is the relationship, if any, between parental education and the education of the respondent? If a relationship exists, which parent has the strongest effect on the educational level of the respondent?


    3. Is there a linear relationship between age and education, and if so, how strong is that relationship? Is it possible to predict educational level based on age? If so, what limitations exist for the developed method?


    4. What is the relationship of marital status on education? Do singles or married persons tend to be more highly educated?


    Your writing should demonstrate thoughtful consideration of the ideas and concepts that are presented in the course and provide new thoughts and insights relating directly to this topic. Responses should reflect doctoral-level writing standards and have no spelling, grammar, or syntax errors.

    Submit your paper in the Course Work area of the Activity screen.

    Learning Outcomes: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12
  • Interpret research methods and basic statistics as they relate to planning, conducting, and interpreting inferential statistics.
  • Develop appropriate null and alternative hypotheses given a research question.
  • Evaluate descriptive statistical analysis and visual displays of data.
  • Apply appropriate statistical tests based on levels of measurement.
  • Calculate the appropriate use of inferential statistical analysis.
  • Demonstrate how population, sampling, and statistical power are related to inferential analysis.
  • Analyze the assumptions required for valid inferential tests.
  • Evaluate the difference between parametric and non-parametric data analysis and how to apply the correct statistical procedure.
  • Demonstrate proficiency in the use of SAS and the reporting of statistical output in APA format.
  • Synthesize various statistical concepts to compare and contrast positions with published peer review research on the same topic.

  • Post Course Survey:
    Complete the Post Course Survey after submitting your final assignment. The Post Course Survey goes directly to the University and provides information used in both course and Mentor evaluation and assessment. The Post Course Survey is located in the Course Review section of the Learner web site. THE RESPONSES ARE ANONYMOUS.

    Receiving Your Final Grade:
    The final grade should be posted by your Mentor within one week following the course end date. The registrar will send an e-mail notifying you of your grade, and the grade will appear under the Course Review section on your Learner site.
    Syllabus Effective Date: 2/17/2010
    Syllabus Details