CRIM 430

1. CRIM 430 Sampling & Data Collection

2. Simple Random • List of elements in sampling frame • Number each element • Select a number from the random numbers table arbitrarily • The number selected indicates which element should be selected first • Move in constant direction on the random number tables until all sample spots are filled

3. Simple Random Example

4. Systematic Sampling • List of elements from sampling frame • Put in order (alphabetical, chronological, etc.) • Calculate the nth element for selection by dividing the number desired by the total number in the sampling frame • Start a random point and go down the list, selecting every nth element until you’ve reached your sample size

5. Stratified Sampling • Modification to previous methods • Ensures a degree of representativeness • First, population is organized into homogeneous subsets (all males, all females) • Sample elements are selected using the simple random or systematic method • Once appropriate sizes for each group is me, the sample elements are placed back together to create the whole sample. • Sample distributions of stratified variable equals the population distribution

6. Disproportionate Stratified • Used when members of a population vary widely in size (e.g., 90% Male & 10% White)—it ensures that you receive an appropriate number of “rare” cases • Same methods as stratified sampling except the distribution of the stratified variable is greater in the sample than it is in the population (I.e., the sample may=50/50 males and females) • The “rare” cases are over-sampled • To compensate for this, data can be weighted during analysis to reflect population distributions

7. Multi-Stage Cluster Sampling • Used when there are many “layers” to the target population (e.g., police officers from all metropolitan departments across the United States) • First, apply sampling to select departments • Next, apply sampling to select police officers • Sampling is done in stages or clusters • More clustering results in potentially less representativeness

8. Non-Probability Sampling • Probability sampling designs are not possible in many situations • Non-probability sampling is an alternative; however, the samples are not representative of the population from which they are drawn • Non-probability sampling designs are prone to selection bias • Non-Probability sampling designs are, therefore, weaker than probability sampling designs

9. Non-Probability Sampling Designs • Purposive or Judgmental Sampling: Identifying a sample based on the presence of a particular characteristic • Quota Sampling: Identifying a sample using a matrix to represent the characteristics of the population • Convenience Sampling: Sample is selected because access is easy and convenient • Snowball Sampling: Using one respondent to provide contact to 2-3 additional respondents—continuous process to identify a larger sample

10. Types of Data Collection • Self-Report Data • Data derived from the respondent him/herself • Key=Ask questions to subjects • Example: National Crime Victimization Survey • Official Data • Data derived from agency records or databases • Key=Examine written records • Example: Uniform Crime Reports • Observation Data • Data derived from watching the activities of people or events; information is coded by observer • Key=Watching behavior • Example: Coding the behavior of detention officers and offenders at a correctional institution

11. Data Collection: Asking Questions Asking questions provides an indirect measure or substitute for making observations—Used to capture things such as experiences with crime, attitudes and beliefs • Self-administered surveys • Mailed surveys • In-person structured interviews • Telephone interviews • Focus groups

12. Asking Questions, Cont’d. • Types of questions included in surveys • Open-ended • Close-ended • Statements with levels of agreement • Contingency questions (if yes, proceed; if no, skip to) • Presentation of questions in a survey • Should be clear—avoid ambiguity & confusion • Keep items short and to the point • Keep items neutral and unbiased • Add disclaimers/introductions to provide respondent with direction

13. Assessment • Strengths • Useful in describing large populations • Standardized surveys improve strength of measurement • Flexible during planning • Provides opportunity to capture a lot of information • Weaknesses • Limited in the information it can capture • Does not capture the context of the situation • Not flexible during implementation • Relies on the truthfulness & memory of respondent

14. Data Collection: Written Records • Published Statistics • Compiled statistics produced and distributed for public consumption • Example: UCR • Nonpublic Agency Records • Records kept by agency for processing purposes • Not available for public consumption • Example: Probation case files • New Data Collected by Agency Staff • New information collected as part of the agency process in order to investigate a research question • Example: Use of a new screening tool

15. Written Records, Continued • Other Related Sources: • Content Analysis • Reviewing narratives, usually written, to identify patterns and themes • Example: Newspaper reports of crime over time • Secondary Data Analysis • Data are originally collected one set of researchers and then made available to other researchers for analysis • Example: Arrestee Drug Abuse Monitoring Data

16. Assessment • Strengths • In general, the availability of these data is much easier than self-report • The cost can be significantly less than self-report • Conducive to large numbers • Weaknesses • Access is sometimes limited especially with regard to cj information • Information is limited by agency priorities • Data are rarely flexible and are defined by agency not the research question • Quality of data is sometimes questionable due to missing and inconsistent reporting of information

17. Data Collection: Observation • Structured observation=quantitative • List of items that an observer will code while observing behavior • Observers use a standard code sheet that contains items and close-ended responses • Items are completed during or immediately after the observation occurs • Unstructured observation=qualitative • General descriptions recorded in a narrative • Transcripts of taped descriptions or written notes used to to identify themes and patterns

18. Assessment • Strengths • Flexible to the needs of the research question • Allows for more thorough investigation of certain situations • Weaknesses • Time consuming and expensive • Collection of data is potentially impacted by data collector’s bias • Analysis of data can be subjective and open to bias, especially in the case of unstructured observations • Limited sample size unless extremely well-funded

19. Multiple Measures • Many studies utilize different types of data to answer research questions • For example, using both official and self-report data to measure variables • Using multiple methods of data collection builds on the strengths of each method individually and minimizes their weaknesses • Multiple methods can increase the reliability and validity of the data you collect • Multiple methods, however, are often expensive and time-consuming

20. Deciding Which Method to Use • Decide on your method based on: • Research Question: What type of information does your research question require? • Availability/access to the sample: How available is a sample and what is your access? • Size of the sample: How large of a sample do you need? • Time and resources: How much time and money do you have at your disposal?

21. Ethical Issues • All research is bounded and defined by professional code of ethics • Social science research is particularly subject to ethical codes because it almost always includes humans subjects • When conducting research, it is necessary to balance the potential benefits from doing the research against the possibility of psychological, emotional, and physical harm

22. IRB • All human subject research conducted at a University must be reviewed and approved by the Institutional Review Board • The IRB ensures that federally defined safeguards are applied in all types of research with humans • Code of Federal Regulations Title 45, Chapter 46, U.S. Department of Health & Human Services • Additional rules apply to two populations considered particularly vulnerable: • Prisoners • Children

23. IRB Safeguards • Safeguards include: • Written consent form must be used to request participation in the study • Written list of benefits and costs of participation • Subject must voluntarily participate • Subject must be guaranteed anonymity or confidentiality • Analysis of data in the aggregate • Protection from deceit by researchers