Concepts Operationalization And Measurementchapter 4introductionwe ✓ Solved

Concepts, Operationalization, and Measurement Chapter 4 * Introduction We want to move from vague ideas of what we want to study to actually being able to recognize and measure it in the real world Otherwise, we will be unable to communicate the relevance of our idea and findings to an audience Conceptions and Concepts Conception: mental image we have about something Concepts: words, phrases, or symbols in language that are used to represent these mental images in communication e.g., serious crime Example of Concept According to Gottfredson and Hirschi's General Theory of Crime, low levels of self-control is the primary cause of crime. Because self-control is a concept, how to conceptualize and measure it has been debated extensively among academics.

Furthermore, the measuring of symptoms of levels of self-control and the inability to measure self-control directly has led some academics to argue that the General Theory of Crime is a tautology and is, therefore, not testable. Conceptualization Conceptualization: mental process of making concepts more precise to specify what we mean Results in a set of indicators and dimensions of what we have in mind Indicates a presence or absence of the concept we are studying Serious crime = offender uses force (or threatens to use force) against a victim Indicators and Dimensions Dimension – specifiable aspect of a concept “Crime Seriousness†– can be subdivided into dimensions e.g., dimension – victim harm Indicators – physical injury, economic loss, psychological consequences Specification leads to deeper understanding Creating Conceptualization Order Conceptual definition: working definition specifically assigned to a term, provides focus to our observations Gives us a specific working definition so that readers will understand the concept E.g., Which dimensions of SES will be included?

Operational definition: spells out precisely how the concept will be measured E.g., How will we measure SES? Progression of Measurement Steps Conceptualization Conceptual Definition Operational Definition Measurements in the Real World * Operationalization Choices Operationalization – the process of developing operational definitions Moves us closer to measurement Requires us to determine what might work as a data-collection method Measurement as “Scoring†Measurement – assigning numbers or labels to units of analysis in order to represent the conceptual properties Make observations, and assign scores to them Different measurement can produce different results E.g., Time frame in which recidivism is measured might produce different results Exhaustive and Exclusive Measurement Every variable should have two important qualities: Exhaustive – you should be able to classify every observation in terms of one of the attributes composing the variable Mutually exclusive – you must be able to classify every observation in terms of one and only one attribute Example – Measure for Marijuana Use Not exclusive or exhaustive How many times in the last year have you smoked marijuana?

Reworded to be exclusive or exhaustive How many times in the last year have you smoked marijuana? or more times Levels of Measurement Nominal: offer names or labels for characteristics (e.g., race, gender, state of residence) Ordinal: attributes can be logically rank-ordered (e.g., education, opinions, occupational status) Interval: meaningful distance between attributes (e.g., temperature, IQ score from an intelligence test) Ratio: has a true zero point (e.g., age, number of priors, sentence length, income) Implications of Levels of Measurement Different analytical analysis require certain levels of measurement Higher levels can be converted to lower levels Lower levels cannot be converted to higher levels Therefore, seek the highest level of measurement possible Criteria for Measurement Quality Measurements can be made with varying degrees of precision The more precise, the better Should not sacrifice accuracy Reliability Reliability: whether a particular measurement technique, repeatedly applied to the same object, would yield the same result each time Problem – even if the same result is retrieved, it may be incorrect every time Reliability does not insure accuracy Observer’s subjectivity might come into play Methods of Dealing with Reliability Issues Test-retest method – make the same measurement more than once – should expect same response both times Interrater reliability – compare measurements from different raters; verify initial measurements Validity The extent to which an empirical measure adequately reflects the meaning of the concept under consideration Are you really measuring what you say you are measuring?

Demonstrating validity is more difficult than demonstrating reliability Methods of Dealing with Validity Issues Face validity: on its face, does it seem valid? Does it jibe with our common agreements and mental images? Criterion-related validity: compares a measure to some external criterion Construct validity: whether your variables related to each other in the logically expected direction Multiple measures: compare measure with alternative measures of the same concept Measuring Crime Crime can be a dependent variable in exploratory, descriptive, explanatory, and applied studies Crime can also be an independent variable, as in a study of how crime affects fear and other attitudes It can be both: the relationship between drug use and other offenses General Issues in Measuring Crime How are do you conceptualize crime?

What units of analysis? Specific entities about which researchers collect information Offender, victim, offenses, incidents What purpose? e.g., monitoring, agency accountability, research Measures Based on Crimes Known to Police Most widely used measures of crime are based on police records Certain types are detected almost exclusively by observation (traffic and victimless offenses) Most crimes reported by victim or witnesses What crimes are not measured well by police records? Uniform Crime Reports (UCR) Originally, reporting voluntary, but now very common Type I offenses (index crimes/offenses): murder, rape, robbery, larceny, burglary, aggravated assault, motor vehicle theft and arson (added in 1979) Type II offenses: a compilation of less serious crimes Summary-based, group level unit of analysis Assumptions of UCR Citizens know an offense has occurred Citizen reports offense to the police Officer can verify that the offense occurred Officer decides the offense deserves to be reported Agency’s numbers end up being forwarded to FBI on time Positives of UCR Can compare agencies Quick, easy, and efficient Index offenses are valid indicators of public’s crime concerns Negatives of UCR Doesn’t count ALL crimes reported to police Jurisdictions vary in completeness of crime data they provide to FBI; voluntary Can suffer from clerical, data processing, political problems Hierarchy rule – only most serious crime counted in an incident Summary-based measure: UCR data include summary crime counts from reporting agencies Incident-Based Police Records Incident-based measures: the incidence of crime is the unit of analysis Supplementary Homicide Reports (SHR) Police agencies submit detailed info about individual homicide incidents Can conduct a variety of studies that examine individual events National Incident-Based Reporting System (NIBRS) Joint effort by FBI and BJS to convert UCR to a NIBRS NIBRS reports each crime incident rather than the total number of certain crimes for each LE agency Many features are reported individually about each incident, offenses, offenders, victims UCR – 7 Part I offenses, NIBRS – 46 Group A offenses Other Revisions with NIBRS Hierarchy rule dropped Victim type (individual, business, government, society/public) Attempted/completed.

Computer-based submission Drug-related offenses Computers and crime Quality control; states require certification National Crime Victimization Survey (NCVS) Victimization survey: asks people whether they have been the victim of a crime Since 1972 by Census Bureau Sought to illuminate the “dark figure of crime†Longitudinal panel study: households agree to participate for 3 years (7 interviews; one every 6 months) and then replaced Does not measure all crime Respondents are asked screening questions Positives of NCVS Measures both reported and unreported crime Independent of changes in reporting More information about how crime impacted victim than UCR Provides more victim characteristics than UCR Negatives of NCVS Telescoping incident dates Faulty memory Little information on offenders No information on CJS response if reported Excludes crimes against commercial establishments Only includes residents of US Surveys of Offending Self-report surveys: ask people about crimes they may have committed Useful in measuring crimes that are poorly measured by other techniques (prostitution, drug abuse, public order, delinquency) Useful in measuring crimes rarely reported to police (shoplifting, drunk driving) Two ongoing self-report studies – NSDUH & MTF National Survey on Drug Use and Health (NSDUH) Based on a national sample of households Conducted since 1971; 2004 sample had n=68,000 Includes questions to distinguish between lifetime use, current use, and heavy use Encourages candid responses via procedures Includes residents of college dorms, rooming houses, and homeless shelters Monitoring the Future (MTF) Conducted since 1975 by the National Institute on Drug Abuse Includes several samples of high school students and others, totaling about 50,000 respondents each year Questions concern self-reported use of alcohol, tobacco, illegal drugs, delinquency, other acts A subset of 2,400 MTF respondents receive follow-up questionnaire Composite Measures Allows us to combine individual measures to produce more valid and reliable indicators Typology: produced by the intersection of two or more variables to create a set of categories or types e.g., Typology of Delinquent/Criminal Acts (Time 1 and 2) None, Minor (theft of items worth less than , vandalism, fare evasion), Moderate (theft over , gang fighting, carrying weapons), Serious (car theft, breaking and entering, forced sex, selling drugs Nondelinquent, Starter, Desistor, Stable, Deescalator, Escalator Index of Disorder What is disorder? (Skogan, 1990) Distinguish b/w physical presence & social perception Physical disorder: abandoned buildings, garbage and litter, graffiti, junk in vacant lots Social disorder: groups of loiterers, drug use and sales, vandalism, gang activity, public drinking, street harassment Index created by averaging scores for each measure Experimental and Quasi-Experimental Designs Chapter 5 * Introduction Experiments are best suited for explanation and evaluation research Experiments involve: Taking action Observing the consequences of that action Especially suited for hypothesis testing Often occur in the field The Classical Experiment Classical experiment: a specific way of structuring research Involves three major components: Independent variable and dependent variable Pretesting and posttesting Experimental group and control group Independent and Dependent Variables The independent variable takes the form of a dichotomous stimulus that is either present or absent It varies (i.e., is independent) in our experimental process The dependent variable is the outcome, the effect we expect to see Might be physical conditions, social behavior, attitudes, feelings, or beliefs Pretesting and Posttesting Subjects are initially measured in terms of the DV prior to association with the IV (pretested) Then, they are exposed to the IV Then, they are remeasured in terms of the DV (posttested) Differences noted between the measurements on the DV are attributed to influence of IV Experimental and Control Groups Experimental group: exposed to whatever treatment, policy, initiative we are testing Control group: very similar to experimental group, except that they are NOT exposed Can involve more than one experimental or control group If we see a difference, we want to make sure it is due to the IV, and not to a difference between the two groups Placebo We often don’t want people to know if they are receiving treatment or not We expose our control group to a “dummy†independent variable just so we are treating everyone the same Medical research: participants don’t know what they are taking Ensures that changes in DV actually result from IV and are not psychologically based Double-Blind Experiment Experimenters may be more likely to “observe†improvements among those who received drug In a double-blind experiment, neither the subjects nor the experimenters know which is the experimental group and which is the control group Selecting Subjects First, must decide on target population – the group to which the results of your experiment will apply Second, must decide how to select particular members from that group for your experiment Cardinal rule – ensure that experimental and control groups are as similar as possible Randomization Randomization: produces an experimental and control group that are statistically equivalent Essential feature of experiments Eliminates systematic bias Experiments and Causal Inference Experimental design ensures: Cause precedes effect via taking posttest Empirical correlation exists via comparing pretest to posttest No spurious 3rd variable influencing correlation via posttest comparison between experimental and control groups, and via randomization Example of Research Using an Experimental Design Researchers at the University of Maryland conducted an evaluation of the Baltimore Drug Court using an experimental design.

For their research, eligible offenders were randomly assigned to either the drug court or to ‘â€treatment as usualâ€. The results of the randomization process were given to the judges as a recommendation. In most cases, the judges, who agreed to participate in the study beforehand, sentenced offenders in accordance with the randomization. The results showed that participants in the drug court were less likely to recidivate than those in the control group. For more information see Gottfredson, D.C., Najaka, S.S. & Kearley, B. (2003).

Effectiveness of drug treatment courts: Evidence from a randomized trial. Criminology, 2(2), . Internal Validity Threats Internal Validity: refers to the possibility that conclusions drawn from experimental results may not reflect what went on in experiment History: external events may occur during the course of the experiment Maturation: people constantly are growing Testing: the process of testing and retesting Instrumentation: Changes in the measurement process Internal Validity Threats: Continued Statistical regression: extreme scores regress to the mean Selection bias: the way in which subjects are chosen Experimental mortality: subjects may drop out prior to completion of experiment Ambiguous Casual Time Order: the dependent variable actually caused the change in the stimulus Generalizability and Threats to Validity Researchers also face problems with generalizing results from experiments Generalizability: do the results of an experiment really tell us what would happen in the real world?

Construct Validity Threats Construct validity: the correspondence between the empirical test of a hypothesis and the underlying causal process that the experiment represents Link construct and measures to theory Clearly indicate what constructs are represented by what measures Decide how much treatment is required to produce change in DV External Validity Threats External validity: whether the results from experiments in one setting will be obtained in other settings Significant for experiments conducted under carefully controlled conditions rather than more natural conditions But, this reduces internal validity threats! A conundrum! Internal validity must be established before external validity is an issue Statistical Conclusion Validity Threats Statistical conclusion validity: whether we are able to determine if two variables are related Becomes an issue when findings are based on small samples More cases allows you to reliably detect small differences; less cases result in detection of only large differences Variations in the Classical Experimental Design Post-test Only design No pretest measure is used Used when pretest might bias results Factorial Design Two experimental groups are used Used to determine necessary amount of treatment Quasi-Experimental Designs When randomization is not possible quasi = “to a certain degree†Quasi-Experiment: an experiment to a certain degree Do not have as stringent of a control over internal validity threats as true experiments Two categories: non-equivalent-groups designs and time series designs Nonequivalent-Groups Designs When we cannot randomize, we cannot assume equivalency; hence the name We take steps to make groups as comparable as possible Match subjects in E and C groups using important variables likely related to DV under study Aggregate matching – comparable average characteristics Cohort Designs Cohort – group of subjects who enter or leave an institution at the same time Necessary to ensure that two cohorts being examined against one another are actually comparable Time-Series Designs Examine a series of observations over time Interrupted – observations compared before and after some intervention Instrumentation threat to internal validity is likely because changes in measurements may occur over a long period of time Often use measures produced by CJ organizations Variations in Time-Series Designs Interrupted Time Series Design with a Non-Equivalent Comparison Group Time-Series Design with Switching Replications Variable-Oriented Research, Case Studies and Scientific Realism Case-oriented research: many cases are examined to understand a small number of variables Variable-oriented research: a large number of variables are studied for a small number of cases Case studies: researcher centers on an in-depth examination of one or a few cases on many dimensions In-depth examinations of a few cases *

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Assignment Solution: Concepts, Operationalization, and Measurement in Research

Introduction

Understanding the principles of concepts, operationalization, and measurement is essential for researchers in the field of social sciences, especially when dealing with constructs such as crime and self-control. This paper will explore these key concepts in detail, using the General Theory of Crime by Gottfredson and Hirschi as an example, followed by a discussion of measurement techniques relevant to crime research.

Moving from Ideas to Measurement

Researchers often begin their work with vague ideas that need to be translated into measurable constructs (Bryman, 2016). For instance, self-control is a concept that requires precise definitions to allow for accurate empirical investigation. While Gottfredson and Hirschi propose that low levels of self-control lead to crime, the challenge lies in operationalizing self-control itself (Sampson & Laub, 1990).

Conceptions and Concepts

A conception refers to a mental image of what a phenomenon entails, while a concept is a more communicable representation, often expressed through words or symbols (Babbie, 2016). For instance, "serious crime" encapsulates various behaviors that can invoke fear in society.
Gottfredson and Hirschi’s General Theory of Crime exemplifies the complexity of turning a concept into a variable worthy of measurement. Here, self-control is the primary variable but operationalizing it involves delineating dimensions and indicators associated with it (Gottfredson & Hirschi, 1990).

Conceptualization

Conceptualization entails refining our mental construct into precise definitions that can direct our research (Rudestam & Newton, 2015). For example, when examining crime seriousness, researchers need to define indicators like "victim harm," which can manifest through physical injury or psychological trauma.

Operationalization

Operationalization refers to the process of developing operational definitions that will measure the conceptual framework (Best & Kahn, 2006). When discussing the operationalization of self-control, researchers utilize specific measures such as self-report surveys, behavioral observations, or psychometric tests.

Measurement Principles

Measurement includes assigning numbers or labels to units of analysis (Trochim, 2006). This process can produce various results depending on the nature of the measurement tools used (Babbie, 2016). It's also crucial that measurements are exhaustive and mutually exclusive (Bryman, 2016).
For example, when measuring marijuana use, a poorly framed question may overlook exclusive categories of frequency. A refined question like, "How many times in the last year have you smoked marijuana?" ensures exclusivity and exhaustiveness.

Levels of Measurement

Researchers categorize variables into nominal, ordinal, interval, and ratio levels (Babbie, 2016). Each level carries implications for the type of statistical analysis one can conduct. For instance, measuring race (nominal) cannot be ranked, while educational attainment can be ranked logically (ordinal). Importantly, using higher operational levels of measurement increases the robustness of findings (Trochim, 2006).

Reliability and Validity

The quality of measurements is determined by reliability and validity. Reliability refers to the consistency of a measurement approach (Best & Kahn, 2006). Researchers often employ test-retest methods to ensure that results remain stable over time. Validity, on the other hand, determines if a measure accurately reflects the concept being studied (Burns & Grove, 2005).
Methods for ensuring validity include face validity, where measures appear to be valid; criterion-related validity, which compares measures against external benchmarks; and construct validity, ensuring variables relate logically (Bryman, 2016).

Measuring Crime

Crime serves as a dependent or independent variable in various studies (Hindelang, 1978). Researchers can utilize police records, victimization surveys, or self-report surveys to gather data (National Institute of Justice, 2010).
The Uniform Crime Reports (UCR) and the National Crime Victimization Survey (NCVS) are two critical measures in understanding crime. While UCR provides a snapshot of crimes reported to law enforcement, NCVS seeks to unveil the "dark figure of crime," accounting for unreported incidents (Hindelang, 1978).
However, both measures have limitations; UCR's hierarchy rule may dismiss lesser offenses, while NCVS is dependent on respondents' memory and willingness to disclose certain information (Bureau of Justice Statistics, 2023).

Self-Report Surveys

Self-report surveys offer a novel approach for measuring crimes that often go unreported. Studies like the Monitoring the Future (MTF) and the National Survey on Drug Use and Health (NSDUH) reveal behavioral patterns among populations that could be overlooked by traditional measures (Chilenski & Greenberg, 2009).

Composite Measures

Researchers may also create composite measures that group multiple indicators to form robust constructs. For instance, an index of disorder captures both physical and social disorder in a community, creating a holistic perspective of crime environments (Skogan, 1990).

Conclusion

Operationalization and measurement are fundamental processes in empirical research. Properly conceptualizing and measuring constructs allows researchers not only to derive meaningful insights but also facilitates effective communication of findings to broader audiences. Despite the complexities involved in measurements, including reliability and validity, the systematic application of these concepts enables researchers to contribute significantly to their fields, particularly in studies of crime and social behavior.

References

1. Babbie, E. R. (2016). The Basics of Social Research. Cengage Learning.
2. Best, J. W., & Kahn, J. V. (2006). Research in Education. Pearson.
3. Bureau of Justice Statistics. (2023). Crime Data. Retrieved from https://www.bjs.gov/.
4. Burns, N., & Grove, S. K. (2005). The Practice of Nursing Research: Conduct, Critique, & Utilization. Elsevier.
5. Bryman, A. (2016). Social Research Methods. Oxford University Press.
6. Chilenski, S. M., & Greenberg, M. T. (2009). Complexity of Programs: Understanding the Intervention. Journal of Community Psychology.
7. Gottfredson, M. R., & Hirschi, T. (1990). A General Theory of Crime. Stanford University Press.
8. Hindelang, M. J. (1978). Reconsidering the Dark Figure of Crime. The Journal of Criminal Law and Criminology.
9. National Institute of Justice. (2010). Measuring Crime. Retrieved from https://nij.ojp.gov/.
10. Rudestam, K. E., & Newton, R. R. (2015). Surviving Your Dissertation: A Comprehensive Guide to Content and Process. Sage Publications.
This comprehensive overview serves as a foundation for engaging with concepts, operationalization, and measurement, highlighting their significance in research methodology.