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Lecture 5

Lecture 5 - Reliability & Validity.docx

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Western University
Psychology 2800E
Doug Hazlewood

Reliability & Validity Prologue  Example: measuring your height  Value is 165 cm. is this an accurate (true) measure?  Random error (misreading number; slouching)  Systematic error or bias (wearing shoes?)  Observed score = True Score + & - Random Error + or – systematic error (or bias)  Systematic error systematically raises/lowers observed score from true score  Must minimize random and systematic error (so observed score = true score). How?  By maximizing the reliability & validity of measures Part 1: Reliability (Minimizing Random Error) A. The “more is better” rule  Random error will cancel itself out over repeated measurements  Example 1: Beating Vince Carter in basketball  Example 2: Grandfathers who “can’t believe it” B. We can decrease random error by increasing the reliability of our measures.  A measure is reliable if it measures things consistently C. Types of Reliability 1. Internal reliability (or internal consistency):  Relevant when measure consists of multiple item (e.g. exam)  Is there consistency between the items?  Inconsistency can be a sign of random error  Assessing internal reliability:  Item-total correlations (if random error is low, responses to any single item should be positively correlated with total score)  Eliminate items with low item-total correlations (and/or add more items)  Split-half reliability (e.g., odd-even correlation)  Combine all the odd number items together and all the even number items together, then correlate the average of the odd with the average of the even  High positive correlation = low random error  Best to use the average of all slit-halves:  E.g. KR-20 (p.133) for measure with discrete values (T-F; MC)  Cronbach’s Alpha for measures with continuous (or discrete) values  If either is low; internal reliability is low (too much random error in measure) 2. Test-retest reliability: measure produces same results consistently over time (aka “temporal stability”)  Take measure at two times; correlate  High correlation = high stability (rules out random error; which is unstable)  Note: only relevant when measuring stable properties of people/things that aren’t
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