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CRIM 430 Lecture 7 Creating Measures for Data Collection

CRIM 430 Lecture 7 Creating Measures for Data Collection

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Page 1: CRIM 430 Lecture 7 Creating Measures for Data Collection

CRIM 430

Lecture 7Creating Measures for Data Collection

Page 2: CRIM 430 Lecture 7 Creating Measures for Data Collection

The Basis of Creating Measures

Conceptual definition: Result of conceptualization—a working definition specifically assigned to a termOperational definition: Definition that clarifies exactly how the concept will be measured—most be specific and unambiguousUse the operational definition to conduct measurements in the real world

These are decisions that you must make based on the literature and your expertise

Creating measures or variables=Process of assigning numbers or labels to units of analysis in order to represent conceptual propertiesOnce your variables are defined, use them to capture observations. Those observations, in turn, are scored to allow for analysis

Page 3: CRIM 430 Lecture 7 Creating Measures for Data Collection

Levels of Measurement

Nominal Measures Variables only used to capture exhaustiveness and exclusiveness

(no order to responses) Examples: Gender, race, city of residence

Ordinal Measures Each attribute represents more or less of the variable Examples: Sentence type, crime seriousness, fear of crime, opinion

of policeInterval Measures

Rank ordered attributes and the distance between attributes has meaning and is measurable

Examples: Temperature, age, years sentenced to prisonRatio Measures

Rank ordered attributes; distance is measurable; attributes are based on a zero starting point

Example: Amount of fine imposed

Page 4: CRIM 430 Lecture 7 Creating Measures for Data Collection

Types of Measures

Page 5: CRIM 430 Lecture 7 Creating Measures for Data Collection

Single Item Measures

Single items involve one question to capture the data you needSingle items are best used to capture demographic information, such as gender, or information that is straightforward in natureSingle items are limited in their ability to represent more complex concepts—for example, single items are not appropriate to capture an attitude toward “x”

Page 6: CRIM 430 Lecture 7 Creating Measures for Data Collection

Composite Measures

Single measures do not necessarily have high reliability and validity when the concept is more complex in nature Happiness, fear of crime, child abuse,

attitudes and opinionsComposite measures improve upon single item measures by using multiple items to measure one variableTypes of composite measures include: Typologies Indexes

Page 7: CRIM 430 Lecture 7 Creating Measures for Data Collection

Typologies

Typologies=Intersection of two or more aspects of the concept(s) you are trying to measureExample 1: Court experience

Two issues: Did you serve on a jury? Did you testify as a witness?

Which of the following best describes your past court experience? No experience with court Experience as juror only Experience as a witness only Experience as juror and witness

Page 8: CRIM 430 Lecture 7 Creating Measures for Data Collection

Typologies, Continued

Example 2: The research question requires that we measure whether a respondent was a victim of sexual assault and/or a victim of domestic violenceYou can ask two different items:

Have you ever been a victim of sexual assault? Have you ever been a victim of domestic violence?

Or you can combine responses into one item: Have you ever been a victim of sexual assault and/or

domestic violence? Not a victim of either sexual assault or domestic

violenceVictim of sexual assault Victim of domestic violenceVictim of sexual assault and domestic violence

Page 9: CRIM 430 Lecture 7 Creating Measures for Data Collection

Index MeasuresIndex: Multiple measures are created based on various aspects of the desired variable/conceptIn this case, the variable you are trying to measure has many different characteristics or aspects. These types of measures are used to increase the accuracy of measuring an attitude, perception, opinion by asking a variety of items related to the desired concept. Together, the responses are considered a reflection of the concept, attitude, perception, or belief.

Page 10: CRIM 430 Lecture 7 Creating Measures for Data Collection

Index Example #1

Example: Perception of disorder Two dimensions required to adequately measure the

concept: Extent and frequency of the problem Two sets of questions:

1.To what extent do you think graffiti is a problem in your neighborhood?

Loitering is a serious problem Loitering is a somewhat serious problem Loitering is a little bit of a problem Loitering is not a problem at all

2.How often do you see graffiti in your neighborhood? All of the time Some of the time Rarely None of the time

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Index Example #2

For example: Delinquency Instead of using one item such as, “Have you ever committed delinquency?” you would provide a list of characteristics and use them collectively (a sum) to measure delinquencyHave you done any of the following in the past __?Gotten into a serious fight? N Y

Taken something worth over $50? N Y

Taken a car without permission N Y

Damaged property on purpose? N Y

And so on…

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Index Example #3

Example: Self-Control Please read each of the following items and indicate the extent to which you agree with each item.

I will often say whatever comes into my head without thinking first.

Strongly agree Somewhat agree Neither agree or disagree Somewhat disagree Strongly disagree

I enjoy working problems slowly and carefully. Often, I don’t spend enough time thinking over a

situation before I act. Responses are summed to create a score that relates to high or low levels of self-control.

Page 13: CRIM 430 Lecture 7 Creating Measures for Data Collection

Assessing the Quality of Your Measures

Reliability—Will your measure, if applied repeatedly to the same object, yield the same result each time?

Test-retest—same person takes test at two different times

Interrater—two people the code same information

Validity—Are you measuring what you say you are measuring?

Face validity—common agreement Content validity—degree to which it covers the range of

meanings Criterion-related validity—extent to which it matches

outcomes of a similar, but different measure