Reliability AND Validity. Fact checking your instrument

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Reliability AND Validity Fact checking your instrument

General Principles Clearly Identify the Construct of Interest Use Multiple Items Use One or More Reverse Scored Items Use a Consistent Response Format That Matches the Items

Not Anything Goes Not Necessarily Easy Requires Careful Creation and Empirical Evaluation

Evaluation The empirical focus of science must apply to the measures used as well as the study topics. Verifying the accuracy and consistency of tools that measure variables is important.

Evaluation Most commonly used measurements have gone through rigorous verification Beck Depression Inventory Recovery Assessment Scale New measures also need verification, this means that new scales have to be assessed when used Measures are assessed on two primary attributes

Dimensions of Evaluation Reliability Are observations answering consistently? Validity Is the instrument measuring what it says it is measuring?

True Score Theory True score theory - statistical concept: there is a true value for a measurable characteristics, but error occurs True score theory of intelligence One true ring value for intelligence Extraneous variables cause errors Anything beyond the main characteristic of interest could be thought of as error Testing multiple times can give support to the reliability of a measure and account for error (though it may introduce other problems)

Reliability Are measurements consistent There are several measures of reliability but here are some common ones Test-retest Parallel forms Internal consistency Inter-rater

Test-Retest Reliability Test 1 vs Test 2 Consistency Across Time Assessed by Test-Retest Correlation

Interpreting Test-Retest Correlations In General, r =.8 is Good However, One Must Take Into Account Expected Consistency of Construct Time Between Tests

Parallel Forms Reliability I create two forms with items measuring the same construct and see if they correlate with each other Testing the reliability of a measure of depression I create one ten item form (form A) I create a second ten item form with similar (but still distinct) questions (form B) I then administer both forms to a group of people and see how responses on the two forms correlate One drawback is that you need to create a lot of items

Internal Consistency Consistency Across Items Assessed by Split-Half Correlation

Internal Consistency A measure of internal consistency that is far superior to split-half because it essentially measures the mean of all possible split-halves There are 252 ways to split a set of 10 items into two sets of five. Cronbach s α would be the mean of the 252 split-half correlations Created by a Fresno State Alumus Cronbach's alpha Internal consistency 0.9 α Excellent 0.8 α < 0.9 Good 0.7 α < 0.8 Acceptable 0.6 α < 0.7 Questionable 0.5 α < 0.6 Poor α < 0.5 Unacceptable

Inter-Rater Reliability A behavioral measure Scores are correlated between different raters on a behavioral measure

Validity Extent to Which Scores Represent the Construct They Are Meant To Dimensions Face Measure Appears Valid Content Measure Covers Construct Criterion Scores Correlate with Related Variables Discriminant Scores Do Not Correlate with Non-Related Variables

Face Validity Does a measure look like it is measuring what it says it is measuring? Sadism measurement on a scale from 1 (never) to 5 (always) 1. I often get angry 2. I get pleasure in hurting small animals 3. I don t have pets for long 4. etc Face validity?? Good? Bad?

Content Validity Based on the conceptual definition of the construct Experts are often the ones who determine content validity Based on current theories of the construct Conceptual definition of trauma: When a person experiences an event or events that drastically affect the person s ability to function on a day to day basis. Items in a trauma instrument should reflect this definition of trauma

Altruism Criterion Validity Related variables should be related Pearson s r value should be close to 1 r = 0.745 10 9 8 7 6 5 4 3 2 1 0 0 2 4 6 8 10 12 Greed

Altruism Discriminant validity Unrelated variables should be unrelated Pearson s r value should be close to 0 r = 0.018 10 9 8 7 6 5 4 3 2 1 0 0 2 4 6 8 10 12 Skill at creating dank memes

A strong measure needs to have both reliability and validity If a measure does not measure what it claims (valid), then it is not very useful Likewise, it is not useful if it not consistent (reliable) Body weight scale is accurate if it produces weight measurements that are representing the true weight of a person standing on it It is reliable if it produces consistent results under similar circumstances This means that a measure can be reliable, but not valid. But a score that is valid must be reliable.