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Table of Contents

Chapter 1 Science and Research……………………………………………………………………. 5

Chapter 2 Elements of Research……………………………………………………………………. 8

Chapter 3 Research Ethics…………………………………………………………………………… 11

Chapter 4 Sampling……………………………………………………………………………………. 15

Chapter 5 Qualitative Research Methods……………………………………………………… 18

Chapter 6 Content Analysis…………………………………………………………………………. 22

Chapter 7 Survey Research…………………………………………………………………………. 26

Chapter 8 Longitudinal Research…………………………………………………………………. 29

Chapter 9 Experimental Research………………………………………………………………… 33

Chapter 10 Introduction to Statistics……………………………………………………………… 36

Chapter 11 Hypothesis Testing……………………………………………………………………… 40

Chapter 12 Basic Statistical Procedures…………………………………………………………. 43

Chapter 13 Newspaper and Magazine Research………………………………………………. 47

Chapter 14 Research in the Electronic Media…………………………………………………. 51

Chapter 15 Research in Advertising………………………………………………………………. 55

Chapter 16 Research in Public Relations………………………………………………………… 59

Research in Media Effects (website chapter)…………………………………. 63

**Chapter 4 – Sampling**

Overview

Scientific research that examines every member or element of a population is called a *census*. However, in most situations, it is impossible to examine every member of a population. In these instances, researchers draw a sample from the population. A sample is defined as “a subset of the population that is representative of the entire population.”

There are two types of samples: probability samples and nonprobability samples. A probability sample follows mathematical guidelines and allows researcher to calculate the estimated sampling error present in a study. Probability samples include random samples, where each subjects in a population has an equal chance of being selected; systematic random samples, stratified samples, and cluster samples.

In addition, this chapter explains several types of nonprobability samples, including available samples, volunteer samples, purposive samples, and quota samples. The process of sampling and the advantages/disadvantages of each sampling methods are described in detail.

Sampling methods must be selected carefully by considering issues such as the purpose of the study, cost versus value, time constraints, and the amount of acceptable sampling error. In this chapter, two concepts related to sampling error are described—confidence level and confidence interval. In addition, the process of computing sampling error is explained.

**Exercises**

- Have students discuss (or write) the problems that can emerge in political polls if the wrong respondents are interviewed about who they will vote for in an upcoming election.
- Using a table of “random numbers,” let students generate a simple random sample from the students’ list in your class or department.

**Multiple Choice**

- The biggest difference between a probability and a non-probability sample is:

- Non-probability samples have smaller measurement errors
- Probability samples have smaller measurement errors
- There is no sampling error in a probability sample
*It is impossible to calculate the amount of sampling error present in a non-probability sample.*

- Which of following is not an example of a non-probability sample?

- Convenience sample
*Multistage sample*- Volunteer sample
- Quota sample

- Which of the following is
__not__an advantage of a simple random sample?

- Detailed knowledge of population not required
- External validity can be inferred
*Cheaper than other methods*- Representative group easily attainable

- Sampling interval is most closely associated with what kind of sample?

- Simple random
- Quota
*Systematic random*- Convenience

- All other things equal, as sample size increases:

*Standard error decreases*- Confidence level increases
- Confidence level decreases
- Random error becomes constant

- Which of the following research techniques usually uses the smallest sample size?

- Multivariate studies
- Panel surveys
*Focus groups*- Phone surveys

- Which of the following is
*not*an advantage of a stratified random sample?

- Representativeness of relevant variables is assured
*No prior knowledge of population is needed*- Sampling error is reduced
- Selection is made from a homogenous group

- Which of the following are measurement errors?

- Faulty data collection equipment
- Asking respondents the wrong questions or asking questions incorrectly
- Data input errors
*All of the above*

- Screener questions are used in questionnaires to . . .

- Make sure that the sample is randomly selected
*Qualify a respondent for a research study*- Identify questions that may cause confusion
- Find new marker variables for future research studies

- In reference to the quality of a sample, The “Law of Large Numbers” (a huge sample) guarantees . . .?

- A greater chance that the data are reliable and valid
- A reduction in sampling error
*Nothing*- Reduces measurement error

**True/False**

- A census is a subset of the population. (F)
- Non-probability samples are usually adequate for pilot studies. (T)
- The confidence interval and the confidence level are two different names for the same concept. (F)
- One of the problems associated with random digit dialing (RDD) is the large number of invalid telephone numbers generated. (T)
- Sampling error occurs when measurements taken from a sample do not correspond to what exists in the population. (T)

**Fill in the Blank**

- The process of examining every member of a population is called a (census).
- A characteristic of the population is called a (parameter).
- A telephone directory is an example of a (sampling frame).
- To calculate the (.95) confidence interval, you multiply the standard error by 1.96.
- A statistical procedure known as (weighting) or (sample balancing) is sometimes used to overcome sampling inadequacies.

**Short Answer**

- Describe the differences between the major types of probability sampling.
- What are the stages in multistage sampling?
- What is sampling error?
- What is the difference between confidence level and confidence interval?
- What is the purpose of sample balancing or sample weighting?
- In the conclusion of a research study, the author wrote, “This research proved . . .” What, if anything, is wrong with that statement?
- Someone says to you, “The larger the sample in a research, the better the results will be.” What would you say to that person?

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