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CRJS 4466 PROGRAM & POLICY EVALUATION LECTURE #4 Test #1 results Evaluation projects Questions?

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Presentation on theme: "CRJS 4466 PROGRAM & POLICY EVALUATION LECTURE #4 Test #1 results Evaluation projects Questions?"— Presentation transcript:

1 CRJS 4466 PROGRAM & POLICY EVALUATION LECTURE #4 Test #1 results Evaluation projects Questions?

2 Measurement in Program Evaluation: test – measurement theory: observed score on measure true score error

3 Deductive/Inductive Model Theory ConceptPropositionConcept VariableHypothesisVariable Operationalization Operationalization Indicator(s)Indicator(s) Empirical Empirical

4 Conceptual Framework Measures Intended Inputs Program Components Intended Outputs Intended Outcomes Measures

5 An Example Let’s begin with an example – The photo radar program in BC is intended to reduce the number of speed-related motor vehicle collisions on BC roadways We can model it Photo Radar Program Fewer speed- related motor vehicle collisions

6 An Example If we want to measure the performance of the program, we need to translate the intended outcome into observables Our conceptual framework for measurement outlines the process

7 Table 4-2:Program Logic of the Vancouver Radar Camera Intervention

8 Construct Is the construct clearly stated? Speed-related motor vehicle collisions Measurement procedures (the actual steps we use to gather the data) Criteria/Issues For Measurement MeasurementProcess Our Example Attending police officer’s assessment of whether speed was a contributing factor; recorded in an accident report; entered into a database Are the measurement procedures valid and reliable?

9 Figure 4-2:Measuring Constructs in Evaluations

10 Measuring Mental Constructs -We ask survey questions -We try to control how the questions are asked -Intended survey questions or survey items are stimuli While we are asking the questions, uncontrolled things happen: -Interviewer characteristics -Setting characteristics -Interviewee characteristics -Instrument characteristics STIMULI RESPONSES The Person’s: KNOWLEDGEATTITUDESEXPERIENCE Valid and reliable responses to survey items (useful data) -Responses to uncontrolled stimuli (noise) -These produce invalid or unreliable data The challenge is to separate useful data from noise

11 Validity and Reliability of Measures Validity: does the variable actually measure the corresponding construct? In our example of the photo radar program, do we believe that police officers can actually tell whether speed was a contributing factor in a motor vehicle accident? Reliability: if we repeat the measurement process for a construct in a given situation, do we get the same result? In a given accident situation, would independent observers reach the same conclusions about speed being a contributing factor?

12 Types of Validity There are different ways of assessing validity – several are relevant here Face validity: do we judge the measurement process/variable to validly represent the construct? Content validity: would experts in the field say that the measure captures the meaning of the construct? Concurrent validity: does the measure correlate with another measure that is valid? – Measuring crime levels (police reports and victim surveys)

13 Types of Reliability We can also assess reliability in different ways Having two or more independent observers take measurements in a given situation – Two police officers completing accident report forms Having the same observer repeat the measurement process in a given situation – Police officer repeats the assessment of possible contributing causes of the accident

14 Tests for Checking Reliability Test-retest method - take the same measurement more than once. Split-half method - make more than one measurement of a social concept (prejudice). Use established measures. Check reliability of research-workers.

15 Characteristics of Variables Variables can categorize (nominal variables) – Categories must be mutually exclusive and jointly exhaustive In a job training program, clients could be categorized as being on social assistance or not Variables can rank (ordinal variables) – Categories are ranked from less to more In a job training program, clients could be asked to rate the program: not beneficial, somewhat beneficial, very beneficial Variables can count (interval and ratio variables) – There is a unit of measurement Number of weeks of job training

16 Likert Item and Response Categories Improved pre-harvest planning, quicker reforestation, and better planting maintenance would reduce the need for chemical or mechanical treatments. StronglyStrongly AgreeAgree Neither DisagreeDisagree 1 2 3 4 5 Please circle the appropriate response

17 Example Questions Question 8:Do you think that your police services would improve if your police department and all other police departments (emphasis in the original) in the West Shore area combined into one department? _____ Yes_____ No_____ Undecided Question 9:Have you discussed this question of police consolidation with friends or neighbors? _____ Yes_____ No_____ Undecided Question 10:Are you for or against combining your police department with police departments in surrounding municipalities? _____ Yes_____ No_____ Undecided

18 Examples of Validity and Reliability Issues Applicable to Surveys Validity: BiasSource of the ProblemReliability: Random Error race, gender, appearance, interjections, interviewer reactions to responses interviewerinconsistency in the way questions are worded/spoken old age, handicaps, suspicion respondentwandering attention biased questions, response set, question order instrumentsingle measures to measure client perceptions of the program privacy, confidentiality, anonymity interviewing situation/interviewing method noise, interruptions biased coding, biased categories (particularly for qualitative data) data processingcoding errors, intercoder reliability problems

19 Four Levels of Measurement 1.Nominal - offer names for labels for characteristics (gender, birthplace). 2.Ordinal - variables with attributes we can logically rank and order.

20 Four Levels of Measurement 3.Interval - distances separating variables (temperature scale). 4.Ratio - attributes composing a variable are based on a true zero point (age).

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