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FSU PSY 3213C - Chapter 5 – Identifying Good Measurement

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Chapter 5 – Identifying Good MeasurementTypes of Measurement:Assessing Reliability (getting consistent results)Validity:Scales:More Types of Validity:Chapter 6 – Describing What People DoTypes of Questions:Problems with Questions:Enemies of Accurate Responding:Sampling:Sample Size Issue:Chapter 7 – Bivariate CorrelationsCorrelational Design vs. Correlational Statistical MethodsNotes on StatisticsStatistical Significance may not be Practically SignificantSide note issues for bivariate correlation:Dealing with Outliers:Curvilinear Relationships:Moderator Variables:Question of Causality:Chapter 8 – Multivariate CorrelationEstablishing Temporal Precedence:Ruling out Third Variables:Standardized Beta and Unstandardized Coefficient bMediation:Mediator vs. ModeratorChapter 5 – Identifying Good MeasurementTypes of Measurement:1. Self-report: Anything that the subjects give the answer to (GPA, etc.)- Issues: people are not always accurate, they may overestimate their abilities, people might randomly answer “Christmas tree”, they might not be truthful or try to give answers they deem as being correct or desired, questions can be biased or loaded (confirmatory hypothesis testing)2. Behavioral: how a person acts (response time- as in chess playing: easy to measure, good rating system, can be interested in how chess players can be so good with memory)- Issues: misinterpret the behavior, may not be able to observe all of the factors that are contributing to performance, limitations to what you can study, some people act differently when they are observed (faking good or bad, Hawthorn Effect)3. Physiological: (heart rate, sweat, brain imaging)- Issues: be very careful to get a baseline level, everybody’s body works differently, (lie detectors are a physiological measure) you can get false positives and false negatives because people might be more anxious or have different body responses simply becausethey are being measured, or they might be able to control certain responses and cheat the systemAssessing Reliability (getting consistent results)1. Test-Retest Reliability: is the answer the first time able to predict your answer the second time?- Correlation Coefficients can quantify reliability: High correlation means it is reliable, because one measurement can predict a future measurement (Test-Retest Reliability)- Does performance at Time A predict performance at Time B?2. Interrater Reliability: Ex: two people measure same person’s aggression on different days. This measures how much the two observers agree. It should be high, if it is not then it is not reliable.- You do not want negative correlations on this (or any) reliability. On this it would mean the researchers are strongly disagreeing.- (see slide #10: A is good interrater reliability, B is bad interrater reliability)- If you get bad reliability here you can- more specifically define what they are supposed to be measuring, send out new rater(s)3. Internal Reliability: do different items on a test correlate with each other- Cronbach’s Alpha: measure of internal reliability, measures how well every item on a test correlates with every other item on a test- Average correlation is the Cronbach’s Alpha. Typically 0-1 realistically, different questions could correlate with each other negatively but (people who get one right would get another wrong) but two things that are negatively correlated to the same item, would themselves be positively correlated(Example of Cronbach’s alpha: point by serial correlation- how every item on a test correlates with all other items on a test)- Why ask so many questions if they are all correlated? So you can establish an average! You take more measurements so you can get closer to the average (creating a distribution around the true value) this will get you the best estimate!Validity: Is a reliable measure always a good measure? NO! You need something else- VALIDITY. The measure hasto be VALID and RELIABLE! Reliability is consistency over time, validity is how your measure actually measures what it’s supposed to measure.1. Face Validity: the extent to which your test APPEARS to measure what it says it’s measuring. - For example, answering math questions as an intelligence test might have face validity (superficial validity). - A test does NOT NEED to have face validity to be valid, and having facing validity does NOT MEAN it is valid overall. 2. Content Validity: the extent to which your measure envelopes everything about a certain construct (big picture about a construct)- If you are measuring depression and you only measure their feelings, not also their behaviors, then you do not have content validity because you are measuring one aspectof depression but not all of them.Scales: Categorical Scales: If data is in categories. Also called Nominal Scales.- Example: Gender, Driving experiment with Cell Phone or No Cell Phone, Ethnicity, level of education (high school vs. college vs. post-grad)Quantitative Scales: Different subtypes of this:1. Ordinal: a rank order, 1st 2nd 3rd etc. – distance between ordinal values can vary- Problem is difference between the ordinal values is not consistent, maybe 1st and 2nd are miles apart (1st is way better than 2nd), but 2nd is just barely better than 3rd (rankings in athletics)2. Interval: equal distances between their values, but they have a non-meaningful 0 point, and the ratio between those distances is not the same. - Most common example is: temperature (Fahrenheit and Celsius) 0 degrees Celsius and 0 degrees Fahrenheit are not No Temperature (not meaningful 0), the distance between0 and 1 degrees is the same as 1 to 2 degrees. Is 6 degrees three times the temperatureof 2 degrees? NO. 6 is not 3 times the temperature of 2 degrees. - IQ tests: 100 is the average. Standard deviation is 15 in either direction (normally distributed). If you have a 0 it does not mean you have NO intelligence (there is no 0 point- so no meaningful 0), If someone scores a 60 they are not ACTUALLY half as smart as someone with a 120. - Therefore : A) No meaningful true zeros B) cannot be thought of as ratios to each other even though C) the distance is the same3. Ratio: has a true and meaningful 0. Same distance between values, and there is a ratio between distances- Kelvin scale of temperature- 0 = no temperature ABSOLUTE ZERO. Weight- 0 grams means NO WEIGHT. Relative terms can be used- distance between 1 pound and 2 pound is the same as 2 to 3


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