Module One: What is Statistics? Online Session

Transcription

1 Module One: What is Statistics? Online Session Introduction The purpose of this online workshop is to aid you in better understanding the materials you have read within the class textbook. The goal of this lesson is NOT to turn you into a statistician. Instead, the goal is to provide you with a working knowledge of statistics. It is beneficial to have a fundamental understanding of statistics in order to critically evaluate research studies. Additionally, you want to enhance your skills in using statistics because some jobs require a background in measurement and statistics. For example, the training and development field requires practitioners to use statistical tools to validate their needs assessments and training evaluations. Practitioners in marketing and sales often use statistics within their employment. Therefore, as you work your way through this and subsequent lessons, think in terms of both theoretical and practical uses of employing statistical techniques, both to your research paper and to your job. Objectives At the end of this lesson, you will be able to: Distinguish between descriptive and inferential statistics; Define sample; Define variable; and, Differentiate between scales of measurement. Outline Descriptive and Inferential Statistics Basic Definitions 1 Module 1 Online Mary L. Lanigan, Ph.D.

2 Descriptive and Inferential Statistics Descriptive Statistic A descriptive statistic is a number that can be catalogued and depicts a data set (Spatz, 1993, p. 2). Examples of descriptive statistics include the mean, mode, and median. Typically when you answer a survey of some sort, there is a section asking about demographic data. Example: After seeing a play at the Steppenwolf theatre, attendees were given this survey to complete. Peruse the instrument above. Notice that gender, age, ethnicity, income, and attendance are demographic questions. If the survey creators were going to run statistics on this information, then these items would fall under the category of descriptive statistics. In addition to seeing descriptive statistics cited in research studies, you will often see inferential statistics reported. 2 Module 1 Online Mary L. Lanigan, Ph.D.

3 Inferential Statistics An inferential statistic is a technique that considers chance or the probability of error when samples are employed to investigate assumptions about populations (Spatz, 1993, p. 2). Therefore, statistical tools testing for statistical significance are more than likely inferential statistics such as correlations, T-tests, ANOVA, etc. According to Creswell (2014), statistical significance conveys whether a test result is better than a chance outcome. Example: If researchers were investigating the relationship between exercise and blood pressure, they would employ inferential statistics. The illustration below shows an output of a correlation. Correlations Blood_Pressure Exercise Blood_Pressure Pearson Correlation ** Sig. (2-tailed).001 N 8 8 Exercise Pearson Correlation ** 1 Sig. (2-tailed).001 N 8 8 You will learn more about correlations and how to read such outputs in the next lesson. For now, simply grasp the difference between inferential statistics and descriptive statistics. Next, let s address two additional concepts used in research studies. Basic Definitions There are two fundamental concepts used in many research studies: (1) sample and (2) variable. Sample A sample is simply a subset of a larger group, referred to as a population. A population is a whole. In the majority of cases, it is impractical to study a whole population. Thus, most research studies focus on obtaining samples. Therefore, don t get too caught up in worrying about samples and populations because from a practical point of view, you are likely to carry out a study using a sample. An even more practical reality is: Your sample is likely to be one of convenience, that is, subjects you have access to such as undergraduate students at a university. 3 Module 1 Online Mary L. Lanigan, Ph.D.

4 Variable A second term you will frequently read about is a variable. A variable is an entity that is quantifiable beyond one. For example, gender has male and female and for the truly progressive transgendered and so forth. Even though gender is considered nominal data, it is still a variable and when you enter the information into a statistical software package, you will label each gender variable with a number. For example, male may be labeled as one; female as two; transgendered as three; and so forth, if you have additional categories. Nominal data is categorical data in which numbers are assigned to things in order to label them. For example, the category of household pets may include: dogs = 1; cats = 2; birds = 3; other= 4 A variable could also be items you are measuring such as attitude, self-efficacy, IQ, etc. Let s say you are using the variable of attitude and you are measuring it with an attitudinal instrument. The scale employed within the instrument is a Strongly Agree to Strongly Disagree response set in which the former is represented by a 5 and the latter is represented by a 1. Thus, you have the variable of attitude and the variable labels, which you will enter into your statistical software packages, as 5 = Strongly Agree; 4 = Agree; 3 = Neutral; 2 = Disagree; and 1 = Strongly Disagree. Nominal data is one of four types of scales of measurement. Let s differentiate amongst the various scales. Differentiate Between Scales of Measurement The four scales of measurement are: Nominal, Ordinal, Interval, and Ratio. These four scales play a big role in determining what type of statistical analysis a researcher can carry out. To learn more about these scales, let s watch the video produced by Statistics Learning Centre (2011). 4 Module 1 Online Mary L. Lanigan, Ph.D.

5 Summary You now have grasped some of the fundamental concepts of statistics. In the next lesson, you will further develop your understanding of research methods by learning more about the most popular statistical tools researchers are likely to use. 5 Module 1 Online Mary L. Lanigan, Ph.D.

6 References Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4 th ed.). Los Angeles, CA: SAGE. Spatz, C. (1993). Basic statistics: Tales of distributions (5 th ed.). Pacific Grove, CA: Cole Publishing Co. Statistics Learning Centre. (2011). Types of Data: Nominal, Ordinal, Interval/Ratio - Statistics Help [Video file]. Retrieved from Steppenwolf. (n.d.). Sixty-second survey. Chicago, IL: Steppenwolf. 6 Module 1 Online Mary L. Lanigan, Ph.D.