As you begin this course, you may be asking yourself, “Why do I have to take statistics?” There are four reasons.
Statistics are a basic component of what we do as professionals—we ask questions and often use quantitative data to find answers. “To answer interesting questions you need data” (Field, 2013, p. 2). As professionals, we constantly ask questions about the world around us, usually to understand basic human and organizational processes and to help people in some way. As you begin this course, keep in mind that we use statistics to test our hypotheses, but what we are ultimately concerned with is finding answers to interesting questions. Collecting and analyzing data helps us to do this. An important goal of this course is to demonstrate how descriptive and inferential statistics allow you to investigate questions that you really care about answering.
Your field of study assumes that we learn about the world by accurately measuring it. Just like biologists and physicists, we find ways to quantify phenomena that interest us. We use statistics as part of our method of measuring and understanding a variety of constructs, such as leadership, intelligence, job satisfaction, and so on.
You have a professional obligation to become educated consumers of statistics. At one level, it does not matter if you are interested in basic research or in an applied area of research. As a professional, you will be routinely tasked with interpreting statistics to perform effectively at your job. Statistics appear in journal articles in your area of specialization. In basic research settings, professionals routinely rely on statistics to test their hypotheses. In applied settings, statistics are used to create reliable and valid survey instruments, to test treatment interventions, and to make assessments across a variety of topics.
Statistics sharpen our critical thinking skills, which are necessary in both basic and applied areas of research. Studying statistics requires you to think logically, study assumptions, calculate the probability of events, make inferences, and evaluate outcomes. All high-level professionals in your field engage in this form of mental activity in their work duties.
You have been given the reasons why you need to study statistics, but this does not mean that you have to like it. Many graduate students dread statistics; if you are not one of these people, count yourself fortunate. About four in five graduate learners experience “uncomfortable levels” of statistics anxiety, which is “defined simply as anxiety that occurs as a result of encountering statistics in any form and at any level” (Pan & Tang, 2004, p. 149).
Statistics anxiety can be a serious obstacle in terms of successfully completing a graduate degree. Often, learners associate a statistics course with a mathematics course. They believe they lack sufficient mathematical training to succeed in statistics (Pan & Tang, 2004). This belief can lead to avoidant behavior, such as avoiding research projects and postponing enrollment in a statistics course until absolutely necessary to graduate (Pan & Tang, 2004). Once enrolled, statistics anxiety is also related to negative emotional states that disrupt the learning process (Onweugbuzie, 1999), which can jeopardize a student’s ability to successfully complete required tasks.
Why is statistics anxiety so prevalent among graduate students? According to Pan and Tang (2004), statistics anxiety is possibly related to three factors:
1. Situational factors, such as math experience and computer experience.
Introduction to Statistics
2. Personality factors, such as perfectionism and procrastination.
3. Personal factors, such as age, gender, and ethnicity.
Graduate students who tend to be older, graduate students who have fewer background courses in mathematics and statistics, and graduate students who have little experience in conducting research tend to express moderate to high levels of statistics anxiety (Pan & Tang, 2004). Ask yourself if you fit any of these profiles. If you do, chances are that you have at least some statistics anxiety.
So how can we reduce uncomfortable levels of statistics anxiety at the outset of this course? According to Pan and Tang (2004), three important factors are associated with statistics anxiety:
1. A fear of asking for help.
2. Fear of statistics instructors.
3. Test anxiety.
We will address these factors one at a time.
The emphasis in this course is not on mathematics; instead, the focus is on translating data from SPSS output into inferences about samples and populations.
Take your time, carefully read the texts, and then apply what you have learned in the discussion and in your assignments. This is not an easy course.;
Organization of Course Content
This course is designed to introduce you to the fundamental theories, concepts, and applications of quantitative statistics as used by professionals. The components of this course include:
• Textbooks. You will read roughly the first third of the Warner text, Applied Statistics: From Bivariate Through Multivariate Techniques. (You will use other parts of the text in later courses.) This is a graduate- level textbook of statistics. Aside from being thick and heavy, it does include mathematical formulas, which can be intimidating at first glance. However, with careful reading and rereading when necessary, Warner will provide you with an excellent foundation in graduate-level statistics. You will also read chapters from the George and Mallery text, IBM SPSS Statistics 23 Step by Step: A Simple Guide and Reference. This book provides you with step-by-step instructions and screenshots for executing the SPSS commands necessary to complete assignments. You will use SPSS data files associated with the George and Mallery text to complete homework assignments.
• SPSS. You will use SPSS in this course to generate statistical output for your assignments. As you begin this course, ensure that you are running the SPSS Standard GradPack (version 22 or higher) and that your software license is up to date. Refer to the Course Materials section of the syllabus for additional details.
• Discussions. This course involves discussion activities with your classmates and the instructor. Discussion prompts test your understanding and application of various statistical topics, ranging from descriptive statistics, probability and null hypothesis testing, correlation, t tests, and analysis of variance (ANOVA).
• Written Assignments. There are six written assignments in this course. They are due in Units 3, 4, 6, 8, 9, and 10. Assignments will consist of data analysis and application with SPSS, as well as a journal article summary.
Field, A. (2013). Discovering statistics using IBM SPSS (4th ed.). Thousand Oaks, CA: Sage.
George, D., & Mallery, P. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference (14th ed.). New York, NY: Routledge.
Onweugbuzie, A. J. (1999). Statistics anxiety among African American graduate students: An affective filter? Journal of Black Psychology, 25, 189–209.
Pan, W., & Tang, M. (2004). Examining the effectiveness of innovative instructional methods on reducing statistics anxiety for graduate students in the social sciences. Journal of Instructional Psychology, 31, 149 –159. Other References http://davidmlane.com/hyperstat/ https://studysites.sagepub.com/warner2e/study/default.htm
To successfully complete this learning unit, you will be expected to:
1. Review the basic concepts of quantitative statistics.
2. Reflect on academic and professional plans for using statistics.
Study 1- Readings Use your textbooks and the Internet to complete the following:
• Read the Learner Expectations page for important information about your success in this course. • Read the Professional Communications and Writing Guide. You are expected to adhere to these
guidelines when writing a discussion post, peer response, or paper, as well as when using citations and references.
• Use your Warner text, Applied Statistics: From Bivariate Through Multivariate Techniques, to complete the following:
◦ Read Chapter 1, “Review of Basic Concepts,” pages 1–40. This reading addresses: ◾ Samples and populations. ◾ Descriptive versus inferential statistics. ◾ Levels of measurement and types of variables. ◾ Normal distribution. ◾ Research design. ◾ Selection of an appropriate statistic.
• Use your IBM SPSS Statistics Step by Step text to complete the following: ◦ Review Chapters 1–4. This material provides an overview of SPSS, including opening SPSS,
reviewing the layout of SPSS, becoming familiar with SPSS menus and icons, viewing and manipulating output, and managing data. Note that Chapter 2a is for PC users, and Chapter 2b is for Mac users.
PSY Learners – Additional Required Readings
In addition to the other required study activities for this unit, PSY learners are required to read the following:
Harraway, J. A., & Barker, R. J. (2005). Statistics in the workplace: A survey of use by recent graduates with
higher degrees. Statistics Education Research Journal, 4(2), 43–58.
Discussion 1 – Statistics Anxiety Identify your sources of statistics anxiety as you begin this course. Reflect on your thoughts, feelings, and behaviors related to statistics. How can you minimize statistics anxiety moving forward?
Articles Suggested Readings
Harrison, J., Thompson, B., & Vannest, K. J. (2009). Interpreting the evidence for effective interventions to increase the academic performance of students with ADHD: Relevance of the statistical significance controversy. Review of Educational Research, 79(2), 740–775. Stone, E. (2010). t test, independent samples. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 1552–1556). Thousand Oaks, CA: Sage. doi:10.4135/9781412961288.n475
Wabed, A., & Tang, X. (2010). Analysis of variance (ANOVA). In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 27–29). Thousand Oaks, CA: Sage. doi:10.4135/9781412961288.n11
Walk, M., & Rupp, A. (2010). Pearson product-moment correlation coefficient. In N. J. Salkind (Ed.), Encyclopedia of research design (pp. 1023–1026). Thousand Oaks, CA: Sage. doi:10.4135/9781412961288.n309
Young, J. R., Young, J. L., & Hamilton, C. (2014). The use of confidence intervals as a meta-analytic lens to summarize the effects of teacher education technology courses on preservice teacher TPACK. Journal of Research on Technology in Education, 46(2), 149–172.
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