CONCEPTUAL DRAFT OF CHAPTER 1 INSTRUCTIONS

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Chapter 5:

Concepts, Operationalization and Measurement

Learning Objectives

• Understand the role of concepts as summary devices for bringing together observations and experiences that have something in common

• Explain how concepts are mental images that do not exist in the real world

• Describe how operationalization specifies concrete empirical procedures for measuring variables

• Recognize that operationalization begins with study design but continues through the duration of research

• Explain why measurement categories must be mutually exclusive and exhaustive

Learning Objectives, cont.

• Distinguish different levels of measurement and the properties of different levels

• Understand precision, reliability, and validity as dimensions of measurement quality

• Summarize how creating specific, reliable measures may not reflect the complexity of the concepts we seek to study

• Understand how multiple measures of a concept can improve reliability and validity

• Describe composite measures and explain their advantages

Introduction

• Because measurement is difficult and imprecise, researchers try to describe the measurement process explicitly

• 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

• Clarifying abstract mental images is an essential first step in measurement

• “Crime” • 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., gender, punishment, chivalry, delinquency, poverty,

intelligence, racism, sexism, assault, deviance, income

Three Classes

• Direct observables: Those things or qualities we can observe directly (color, shape)

• Indirect observables: Require relatively more subtle, complex, or indirect observations for things that cannot be observed directly (reports, court transcripts, criminal history records)

• Constructs: Theoretical creations; cannot be observed directly or indirectly; similar to Concepts

Conceptualization

• Specifying precisely what we mean when we use particular terms

• Results in a set of indicators of what we have in mind

• Indicates a presence or absence of the concept we are studying

• Violent 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

Discussion Question 1

Why does the phrase “crime seriousness” require further conceptualization?

Confusion Over Definitions and Reality

• Concepts are abstract and only mental creations

• The terms we use to describe them do not have real and concrete meanings – What is poverty? delinquency? strain?

• Reification: Process of regarding as real things that are not

Creating Conceptual Order

• Conceptual definition (what is SES?) – 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

• Operational definition (how will we measure SES?) – Spells out precisely how the concept will be measured

Discussion Question 2

What are some social science concepts that you believe might be hard to operationalize?

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

• Difficult in CJ research because basic concepts are not perfectly definable

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: Employment status

Discussion Question 3

Can you identify a variable used in crime studies that is both exhaustive and mutually exclusive?

Levels of Measurement

• Nominal: Offer names or labels for characteristics (race, gender, state of residence)

• Ordinal: Attributes can be logically rank-ordered (education, opinions, occupational status)

• Interval: Meaningful distance between attributes (temperature, IQ)

• Ratio: Has a true zero point (age, # of priors, sentence length, income)

Implications of Levels of Measurement

• Certain analytic techniques have Levels of Measurement requirements

• Ratio level can also be treated as Nominal, Ordinal, or Interval

• You cannot convert a lower Level of Measurement to a higher one

• Therefore, seek the highest Level of Measurement possible

Criteria for Measurement Quality • The key standards for measurement quality

are reliability and validity • Measurements can be made with varying

degrees of precision • Common sense dictates that the more

precise, the better • However, you do not necessarily need

complete precision

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

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

• Split-half method: Make more than one measure of any concept; see if each measures the concept differently

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

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 variable relates to another in the logically expected direction

• Content validity: Does the measure cover the range of meanings included in the concept?

• Multiple Measures: Alternative measures

Composite Measures • Allows us to combine individual measures to

produce more valid and reliable indicators • Reasons for using Composite Measures:

– The researcher is often unable to develop single indicators of complex concepts

– We may wish to use a rather refined ordinal measure of a variable, arranging cases in several ordinal categories from very low to very high on a variable such as degree of parental supervision

– Indexes and scales are efficient devices for data analysis

Typologies • “Taxonomy” • 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 $5, vandalism,

fare evasion), Moderate (theft over $5, 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 between physical presence and

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

Benefits of Indexes

• A composite index is a more valid measure than a single question

• Computing and averaging across all items in a category create more variation than we could obtain in any single item

• Two indexes are more parsimonious than nine individual variables

• Data analysis and interpretation can be more efficient