Unit 3 Study Guide

Chapter 8: Sampling

Learning Objectives:

1. Understand how the logic of probability sampling makes it possible to represent large populations with small subsets of those populations.

2. Recognize that the chief criterion of a sample’s quality is the degree to which it is repre-sentative of the population from which it was selected.

3. Summarize the chief principle of probability sampling: every member of the population has a known, nonzero probability of being selected into the sample.

4. Describe how probability sampling methods make it possible to select samples that will be quite representative.

5. Understand how our ability to estimate population parameters with sample statistics is rooted in the sampling distribution and probability theory.

6. Recognize how simple random sampling is logically the most fundamental technique in probability sampling.

7. Distinguish the variety of probability sampling designs that can be used and combined to suit different populations and research purposes: systematic sampling, stratified sampling (proportionate and disproportionate), and multistage cluster sampling.

8. Understand the basic features of the National Crime Victimization Survey and the British Crime Survey, two national crime surveys based on multistage cluster sampling.

9. Recognize how nonprobability sampling methods are less statistically representative than probability sampling methods, and be able to offer appropriate examples for nonprobability sampling applications.

10. Distinguish the variety of nonprobability sampling types, including purposive sampling, quota sampling, and snowball sampling. Describe examples of each.

Chapter Summary:

• The logic of probability sampling forms the foundation for representing large populations with small subsets of those populations.

• The chief criterion of a sample’s quality is the degree to which it is representative-- the extent to which the characteristics of the sample are the same as those of the population from which it was selected. Samples should be unbiased. But some degree of sampling error always exists.

• The chief principle of probability sampling is that every member of the total population must have some known nonzero probability of being selected into the sample.

• Probability sampling methods provide one excellent way of selecting samples that will be quite representative. They make it possible to estimate the amount of sampling error that should be expected in a given sample.

• Our ability to estimate population parameters with sample statistics is rooted in the sam-pling distribution and probability theory. If we draw a large number of samples of a given size, sample statistics will cluster around the true population parameter. As sample size increases, the cluster becomes tighter.

• Simple random sampling is logically the most fundamental technique in probability sam-pling, although it is seldom used in practice. A variety of sampling designs can be used and combined to suit different populations and research purposes. Each type of sampling has its own advantages and disadvantages.

• Systematic sampling involves using a sampling frame to select units that appear at some specific interval-for example, every 8th, or 15th, or 1023rd unit. This method is functionally equivalent to simple random sampling.

• Stratification improves the representativeness of a sample by reducing the sampling error.• Disproportionate stratified sampling is especially useful when we want to select adequate

numbers of certain types of subjects who are relatively rare in the population we are studying.• Multistage cluster sampling is frequently used when there is no list of all the members of

a population.• The NCVS and BCS are national crime surveys based on multistage cluster samples.

Sampling methods for each survey illustrate different approaches to representing relatively rare events.

• Nonprobability sampling methods are less statistically representative and less reliable than probability sampling methods. However, they are appropriate for many research applica-tions.

• Purposive sampling is used when researchers wish to select specific elements of a popu-lation. This may be because the elements are believed to be representative, or extreme cases, or because the represent the range of variation expected in a population.

• In quota sampling, researchers begin with a detailed description of the characteristics of the total population and then select sample members in a way that includes the different com-posite profiles that exist in the population.

• Snowball samples accumulate subjects through chains of referrals and are most com-monly used in field research.

Key Terms:

Cluster sampling (Page 217)Confidence interval (Page 212)Confidence level (Page 212)Disproportionate stratified sampling (Page 216)Equal probability of selection method (EPSEM) (Page 205)Nonprobability sampling (Page 222)Population (Page 206)Population parameter (Page 206)Probability sampling (Page 203)Purposive sampling (Page 222)Quota sampling (Page 203)Sample element (Page 205)Sample statistic (Page 206)Sampling (Page 202)Sampling distribution (Page 206)Sampling frame (Page 208)Sampling units (Page 217)

Simple random sampling (Page 215)Snowball sampling (Page 225)Standard error (Page 211)Stratification (Page 215)Systematic sampling (Page 215)

Chapter 9: Survey Research

Learning Objectives:

1. Understand that survey research involves the administration of questionnaires in a sys-tematic way to a sample of respondents selected from some population.

2. Describe how survey research is especially appropriate for descriptive or exploratory studies of large populations.

3. Describe examples of surveys as the method of choice for obtaining victimization and self-reported offending data.

4. Summarize differences between open-ended or close-ended questions, and offer exam-ples of the advantages and disadvantages of each.

5. Recognize how bias in questionnaire items encourages respondents to answer in a partic-ular way or to support a particular point of view.

6. Describe the three different ways to administer questionnaires, and offer examples of how they can be varied.

7. Understand how computers can enhance each type of survey.8. Recognize why it is important for interviewers to be neutral in face-to-face surveys.9. Provide examples of the advantages and disadvantages of each method of survey admin-

istration.10. Discuss how survey data can be somewhat artificial and potentially superficial.11. Understand how specialized interviews with a small number of people and focus groups

are different from surveys as examples of collecting data by asking questions.

Chapter Summary:

• Survey research involves the administration of questionnaires in a systematic way to a sample of respondents selected from a population.

• Survey research is especially appropriate for descriptive or exploratory studies of large populations, but surveys have many other uses in criminal justice research.

• Surveys are the method of choice for obtaining victimization and self-reported offending data. Continuing efforts to improve self-report surveys include using confidential computer-as-sisted personal interviews.

• Questions may be open-ended or close-ended. Each technique for formulating questions has advantages and disadvantages.

• Bias in questionnaire items encourages respondents to answer in a particular way or to support a particular point of view. It should be avoided.

• Questionnaires may be administered in three basically different ways: self-administered questionnaires, face-to-face interviews, and telephone interviews. Each mode of administration can be varied in several ways.

• Computers can be used to enhance each type of survey. Computer-assisted surveys have many advantages, but they often require special skills and equipment.

• In face-to-face surveys, it is essential that interviewers be neutral. Their presence in the data collection process must not have any effect on the responses given to questionnaire items.

• Each method of survey administration has a variety of advantages and disadvantages. • Survey research has the weakness of being somewhat artificial and potentially superficial.• It is difficult to gain a full sense of social processes in their natural settings through the

use of surveys.• Although the particular tasks required to complete a survey are not especially difficult,

researchers must carefully consider whether to conduct surveys themselves or contract with a professional organization.

Key Terms:

Bias (Page 239)Close-ended questions (Page 237)Computer-assisted interviewing (CAI) (Page 241)Interview (Page 235)Interview survey (Page 237)Open-ended questions (Page 237)Questionnaire (Page 237)Respondent (Page 235)Response rate (Page 246)Survey (Page 234)

Chapter 10: Qualitative Interviewing

Learning Objectives:

1. Recognize when to use qualitative interviewing as a data-gathering tool.2. Understand that there are multiple meanings or constructions about reality.3. Know the advantages and disadvantages of semi-structured versus unstructured inter-

views.4. Understand the use of focus groups or interviewing a group of people simultaneously.5. Learn how to create interview questions so that data can effectively be gathered.6. Be able to describe how to approach and interact with participants.7. Learn how to record or log data.8. Understand ways to analyze and interpret qualitative data.9. Recognize how to enhance the quality of information gathered.

Chapter Summary:

• When you find yourself wondering about the “hows” involving human perspectives, emotions, or lifestyles, you are probably better off using qualitative interviews.

• The critical realist perspectives assumes that multiple perspectives about reality exist, rather than a single tangible reality.

• Semi-structured interviews have standardized questions but include unscheduled probes and spontaneous questions that allow us to explore emerging themes. More like a natural con-versation, unstructured interviews are best for exploratory studies.

• Focus groups involve interviewing groups of people and are best for examining dynamics between group members. The focus group moderator must guide the conversation and be aware of dominant group members and group think.

• We must think carefully about how questions are phrased and how they occur in conver-sations.

• When conducting qualitative interviewing, it is important to think about whether you are an insider or an outsider and how closely you are connected to your group of interest. This will shape how you gain access and how you establish rapport with participants.

• Active interviewing is a two-sided exchange that is dynamic and served a meaning-mak-ing context. Interactive interviewing also involves a meaning-making context.

• Recording data involves field notes, transcripts, memos, and written records of the re-search process. Research using qualitative interviews typically produces a large data log.

• Analysis or transforming data happens during and after data collection. Thinking units are important tools for guiding data collection.

• The three types of coding in grounded theory are open, axial, and selective. You start with open coding to find concepts in the data and may move on to selective coding, which in-volves combining multiple open codes and/or themes.

• Themes are higher-level concepts usually derived from the coding process. The most common way to find these concepts is through constant comparison of similarities and differ-ences in the data to establish themes.

• Qualitative data can be analyzed through description, analysis, and interpretation. De-scription is closest to raw data, whereas interpretation is more selective.

• The best way to ensure that you have quality data is to create an audit trail of the coding decisions. Member checks, using negative cases, transferability, and neutrality are tools for im-proving data quality.

Key Terms:

Analysis (Page 289)Audit trail (Page 284)Critical realist perspective (Page 271)Description (Page 293)Focus groups (Page 274)Grounded theory (Page 290)Insider/outsider status (Page 284)Interpretation (Page 293)Interview schedule (Page 272)Memoing (Page 289)Qualitative interview (Page 265)Rapport (Page 284)Reflexivity (Page 283)Semi-structured interview (Page 273)

Sensitizing concepts (Page 290)Structured interview (Page 272)Themes (Page 292)Thinking units (Page 290)Unstructured interview (Page 273)

Chapter 11: Field Observation

Learning Objectives:

1. Be able to describe field research as a data collection method that involves the direct ob-servation of phenomena in their natural settings.

2. Recognize the field observation is usually the preferred data collection method for obtain-ing information about physical or social settings, behavior, and events.

3. Understand that field research in criminal justice may produce either qualitative or quan-titative data.

4. Provide examples of how observations made through field research can be integrated with the data collected through interviews and from other sources.

5. Understand why field researchers may or may not identify themselves as researchers to the people they are observing.

6. Recognize what sampling techniques are best suited for field research, and when they can be used

7. Recognize the alternatives for recording field observations, ranging from video, audio, and other equipment to unstructured field notes.

8. Understand how field notes are taken, and be able to describe different ways to combine structure and flexibility in field notes.

9. Summarize how field research measures up on validity and reliability.

Chapter Summary:

• Field research is a data collection method that involves the direct observation of phenom-ena in their natural settings.

• Field observation is usually the preferred data collection method for obtaining informa-tion about physical or social settings, behavior, and events.

• Field research in criminal justice may produce either qualitative or quantitative data. Grounded theory is typically built from qualitative field observations. Or observations that can be quantified may produce measures for hypothesis testing.

• Observations made through field research can often be integrated with data collected from other sources. In this way, field observations can help researchers interpret other data.

• Field researchers may or may not identify themselves as researchers to the people they are observing. Being identified as a researcher may have some effect on what is observed.

• Controlled probability sampling techniques are not usually possible in field research. But in some circumstances, researchers can combine probability and nonprobability sampling.

• Alternatives for recording field observations range from video, audio, and other equip-ment to unstructured field notes. In between are observations recorded on structured forms; en-vironmental surveys are examples.

• Field notes should be planned in advance to the greatest extent possible. However, note taking should be flexible enough to make records of unexpected observations.

• Compared with surveys, field research measurements generally have more validity but less reliability, and field research results cannot be generalized as safely as those based on rig-orous sampling and standardized questionnaires.

Key Terms:

Environmental survey (Page 311)Ethnography (Page 302)Reactivity (Page 304)Safety audit (Page 315)