Reliability and Validity

Criteria of Measurement Quality How do we judge the relative success (or failure) in measuring various concepts? Reliability – consistency of measurement Validity – confidence in measures and design

Reliability and Validity Reliability focuses on measurement Validity also extends to: Precision in the design of the study – ability to isolate causal agents while controlling other factors (Internal Validity) Ability to generalized from the unique and idiosyncratic settings, procedures and participants to other populations and conditions (External Validity)

Reliability Consistency of Measurement Estimates of Reliability Reproducibility over time Consistency between different coders/observers Consistency among multiple indicators Estimates of Reliability Statistical coefficients that tell use how consistently we measured something

Measurement Validity Are we really measuring concept we defined? Is it a valid way to measure the concept? Many different approaches to validation Judgmental as well as empirical aspects

Key to Reliability and Validity Concept explication Thorough meaning analysis Conceptual definition: Defining what a concept means Operational definition: Spelling out how we are going to measure concept

Four Aspects of Reliability: 1. Stability 2. Reproducibility 3. Homogeneity 4. Accuracy

1. Stability Consistency across time repeating a measure at a later time to examine the consistency Compare time 1 and time 2

2. Reproducibility Consistency between observers Equivalent application of measuring device Do observers reach the same conclusion? If we don’t get the same results, what are we measuring? Lack of reliability can compromise validity

3. Homogeneity Consistency between different measures of the same concept Different items used to tap a given concept show similar results – ex. open-ended and closed-ended questions

4. Accuracy Lack of mistakes in measurement Increased by clear, defined procedures Reduce complications that lead to errors Observers must have sufficient: Training Motivation Concentration

Increasing Reliability General: Training coders/interviewers/lab personnel More careful concept explication (definitions) Specification of procedures/rules Reduce subjectivity (room for interpretation) Survey measurement: Increase the number of items in scale Weeding out bad items from “item pool” Content analysis coding: Improve definition of content categories Eliminate bad coders

Indicators of Reliability Test-retest Make measurements more than once and see if they yield the same result Split-half If you have multiple measures of a concept, split items into two scales, which should then be correlated Cronbach’s Alpha or Mean Item-total Correlation

Reliability and Validity Reliability is a necessary condition for validity If it is not reliable it cannot be valid Reliability is NOT a sufficient condition for validity If it is reliable it may not necessarily be valid Example: Bathroom scale, old springs

Not Reliable or Valid

Reliable but not Valid

Reliable and Valid

Types of Validity 1. Face validity 2. Content validity 3. Pragmatic (criterion) validity A. Concurrent validity B. Predictive validity 4. Construct validity A. Testing of hypotheses B. Convergent validity C. Discriminant validity

Face Validity Subjective judgment of experts about: “what’s there” Do the measures make sense? Compare each item to conceptual definition Do it represent the concept in question? If not, it should be dropped Is the measure valid “on its face”

Content Validity Subjective judgment of experts about: “what is not there” Start with conceptual definition of each dimension: Is it represented by indicators at the operational level? Are some over or underrepresented? If current indicators are insufficient: develop and add more indicators Example--Civic Participation questions: Did you vote in the last election? Do you belong to any civic groups? Have you ever attended a city council meeting? What about “protest participation” or “online organizing”?

Pragmatic Validity Empirical evidence used to test validity Compare measure to other indicators 1. Concurrent validity Does a measure predict simultaneous criterion? Validating new measure by comparing to existing measure E.g., Does new intelligence test correlate with established test 2. Predictive validity Does a measure predict future criterion? E.g., SAT scores: Do they predict college GPA?

Construct Validity Encompasses other elements of validity Do measurements: A. Represent all dimensions of the concept B. Distinguish concept from other similar concepts Tied to meaning analysis of the concept Specifies the dimensions and indicators to be tested Assessing construct validity A. Testing hypotheses B. Convergent validity C. Discriminant validity

A. Testing Hypotheses When measurements are put into practice: Are hypotheses that are theoretically derived, supported by observations? If not, there is a problem with: A. Theory B. Research design (internal validity) C. Measurement (construct validity?) In seeking to examine construct validity: Examine theoretical linkages of the concept to others Must identify antecedent and consequences What leads to the concept? What are the effects of the concept?

B. Convergent Validity Measuring a concept with different methods If different methods yield the same results: than convergent validity is supported E.g., Survey items measuring Participation: Voting Donating to money to candidates Signing petitions Writing letters to the editor Civic group memberships Volunteer activities

C. Discriminant (Divergent) Validity Measuring a concept to discriminate that concept from other closely related concepts E.g., Measuring Maternalism and Paternalism as distinct concepts

Dimensions of Validity for Research Design Internal Validity of research design Validity of sampling, measurement, procedures External Given the research design, how valid are Inferences made from the conclusions Implications for real world

Internal and External Validity in Experimental Design Internal validity: Did the experimental treatment make a difference? Or is there an internal design flaw that invalidates the results? External validity: Are the results generalizable? Generalizable to: What populations? What situations? Without internal validity, there is no external validity 1