STAT8028 Business Statistics  
(3 credits)  
Syllabus Effective Date: 2/17/2010 
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.
You may purchase books at www.ncubooks.com.
Credit Hours:
With the facultymentored approach at Northcentral University, credit hours are amassed in a course through studenttofaculty interaction, contact with coursespecific content, assignments, and other asynchronous activities. At Northcentral, students can expect to devote between 135144 hours for each 3credit 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 nonparticipant. 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/PreCourse Survey:
Students should review the Student web site and Course Catalog, which contains all relevant policies and procedures. Students should also complete the PreCourse 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 (DoeJXXX00001) 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): DoeJXXX00001. 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 TurnAround Schedule:
Faculty will return graded assignments with feedback within 4 calendar days of assignment submission.
Note: Turnaround 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.
Selfplagiarism is the act of presenting one’s previously used work as an original work. Selfplagiarism 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 selfplagiarism, 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, selfassessments, 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 
10094 
A 
10094 
A 
9390 
A 
9390 
A 
8987 
B+ 
8987 
B+ 
8683 
B 
8683 
B 
8280 
B 
8280 
B 
7977 
C+ 
7977 
C+ 
7673 
C 
7673 
C 
7270 
C 
720 
F 
6967 
D+ 


6663 
D 


620 
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.
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, selfassessment questions, flashcard glossary, additional materials, answers, etc. You can access the companion site at: http://www.sagepub.com/field3e/
SelfTests – Embedded within each chapter, you will see an icon and the label SELFTEST (http://www.sagepub.com/field3e/additionalwebmaterial.htm). These are questions that can quickly assess your mastery of the material just covered. Answers to all selftests 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 2030 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 fastpaced, 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 nonacademic 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 realtime 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 “inperson” – 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.
4Course Work
Required Reading:
Discovering Statistics Using SPSS: Preface, How to Use This Book, Chapters 1, 2, 3, 4
SelfTests
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
1. Briefly describe your area of research interest (13 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?
1. Considering your area of research interest, briefly state your area and a possible research project related to the area (150500 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.
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.
• Downloadfestival.sav
• Chickflick.sav
• Hiccups.sav
• Textmessages.sav
• ExamAnxiety.sav
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 Yaxis, 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 Yaxis, 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.
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 ttest. 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 pvalue (probability value) is the probability that the observed outcome (or a value more extreme than what we observe) would happen. The pvalue is a value we obtain after calculating a test statistic. The smaller the pvalue, the stronger the evidence against the H0. If we set alpha at .05, then the pvalue 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 pvalue tells us the probability we would expect our result (or one more extreme) GIVEN the null is true. If our pvalue 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 pvalue falls below these alpha levels.
If the pvalue 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 pvalue 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
SelfTests
Smart Alex's Quizzes
SPSS Data Sets:
Downloadfestival.sav
SPSSExam.sav
Chickflick.sav
ChamorroPremuzic.sav
Activity6a.sav
Activity6c.sav
Optional Resources:
Interactive Multiple Choice Questions
Flashcards
• Downloadfestival.sav
• SPSSExam.sav
• Chickflick.sav
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 probabilityprobability (PP) 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 PP Plots into your Activity #4 Word document (you do not need to copy the beginning output nor the Detrended Normal PP Plots).
4. Examining the histograms and PP 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.
• ChamorroPremuzic.sav
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 twotailed 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 twotailed 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 twotailed 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?
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. SemiPartial Correlation. Identify three variables for which you may be interested calculating either a partial or semipartial 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?
• 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 SelfTests 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 LastnamefirstinitialSTAT80286a and the SPSS document as 6b.
Part A. Dependent ttest
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 pretest scores, and “CreativityPost” contains creativity posttest scores. A total of 40 students completed the pretest, took the creativity course, and then took the posttest.
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 ttest to assess your hypotheses above (note that many versions of SPSS use the term “paired samples ttest” rather than dependent ttest; 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 ttest. Be sure that your writing conforms to APA style.
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 ttest to assess your hypotheses above (note that many versions of SPSS use the term “independent samples ttest” rather than simply “independent ttest”.
b. Write one or two paragraphs that describe the dataset, gives your hypothesis, and presents the results of the dependent sample ttest. 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 withinsubjects design. Create a single paragraph (using the material you wrote above), that presents both sets of results.
b. Explain, in 300500 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?
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.
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 lifesaver 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 Twoway repeatedmeasures ANOVA, Twoway Mixed ANOVA, Threeway 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.
RepeatedMeasures. If you are examining multiple groups but the same people belong to each group, you will use a repeatedmeasures 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 carryover effects, practice, and counter balancing, etc.) then you have a repeatedmeasures design. There are some great advantages to repeatedmeasures 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 wellbeing. A MANOVA allows you to make this comparison with one elegant analysis.
NonParametric Tests. Now that you have learned a number of parametric techniques…what do you do if your data do not meet parametric assumptions? Nonparametric tests to the rescue! Tests covered under this category include: Chi square, Wilcoxon ranksum test, MannWhitney tests, KruskalWallis 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
SelfTests
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.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 SelfTests 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 statewide 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.
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.
• 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 repeatedmeasures designs applied. How might you use this analytical strategy in your dissertation research?
Complete the SelfTests 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 pretest (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)
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.
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?
• 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 nonparametric methods applied. How might you use these analytical strategies in your dissertation research?1. What are the most common reasons you would select a nonparametric test over the parametric alternative?
2. Discuss the issue of statistical power in nonparametric 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 nonparametric counterpart:
a. Dependent ttest
b. Independent samples ttest
c. Repeated measures ANOVA (onevariable)
d. Oneway ANOVA (independent)
e. Pearson Correlation
1. Activity 6A: nonparametric version of the dependent ttest
2. Activity 6B: nonparametric version of the independent ttest
3. Activity 6C: nonparametric version of the single factor ANOVA
4. Activity 7: nonparametric version of the factorial ANOVA
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.
• 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?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 posthoc 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.
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?
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?