Download presentation

Presentation is loading. Please wait.

2
Variables cont. Psych 231: Research Methods in Psychology

3
Announcements Download the class experiment results from the web page and bring to labs this week Class experiment due dates: First draft: in labs Oct 23 & 24 Final draft: in class Nov. 19th (no labs that week)

4
Scales of measurement Categorical variables Nominal scale Consists of a set of categories that have different names. Ordinal scale Consists of a set of categories that are organized in an ordered sequence. Quantitative variables Small, Med, Lrg, blue,green,brown,

5
Scales of measurement Categorical variables Nominal scale Consists of a set of categories that have different names. Ordinal scale Consists of a set of categories that are organized in an ordered sequence. Quantitative variables Interval scale Ratio scale Small, Med, Lrg, blue,green,brown,

6
Scales of measurement Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. Example: Fahrenheit temperature scale 20º40º“Not Twice as hot” With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. However, Ratios of magnitudes are not meaningful. 20º40º The amount of temperature increase is the same 60º80º 20º increase

7
Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order Ordered Categories of same size

8
Scales of measurement Ratios of numbers DO reflect ratios of magnitude. It is easy to get ratio and interval scales confused Example: Measuring your height with playing cards Ratio scale: An interval scale with the additional feature of an absolute zero point.

9
Scales of measurement Ratio scale 8 cards high

10
Scales of measurement Interval scale 5 cards high

11
Scales of measurement Interval scaleRatio scale 8 cards high5 cards high 0 cards high means ‘no height’ 0 cards high means ‘as tall as the table’

12
Scales of measurement Categorical variables Nominal scale Ordinal scale Quantitative variables Interval scale Ratio scale Categories Categories with order Ordered Categories of same size Ordered Categories of same size with zero point Given a choice, usually prefer highest level of measurement possible “Best” Scale?

13
Variables Independent variables Dependent variables Measurement Scales of measurement Errors in measurement Extraneous variables Control variables Random variables Confound variables

14
Example: Measuring intelligence? Measuring the true score How do we measure the construct? How good is our measure? How does it compare to other measures of the construct? Is it a self-consistent measure?

15
Errors in measurement In search of the “true score” Reliability Do you get the same value with multiple measurements? Validity Does your measure really measure the construct? Is there bias in our measurement? (systematic error)

16
Dartboard analogy Bull’s eye = the “true score”

17
Dartboard analogy Bull’s eye = the “true score” Validity = measuring what is intended Reliability = consistency of measurement reliable valid reliable invalid unreliable invalid

18
Reliability True score + measurement error A reliable measure will have a small amount of error Many “kinds” of reliability

19
Reliability Test-restest reliability Test the same participants more than once Measurement from the same person at two different times Should be consistent across different administrations ReliableUnreliable

20
Reliability Internal consistency reliability Multiple items testing the same construct Extent to which scores on the items of a measure correlate with each other Cronbach’s alpha (α) Split-half reliability Correlation of score on one half of the measure with the other half (randomly determined)

21
Reliability Inter-rater reliability At least 2 raters observe behavior Extent to which raters agree in their observations Are the raters consistent? Requires some training in judgment 5:00 4:56

22
VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Validity Does your measure really measure what it is supposed to measure? : many varieties

23
VALIDITY CONSTRUCT CRITERION- ORIENTED DISCRIMINANT CONVERGENTPREDICTIVE CONCURRENT FACE INTERNALEXTERNAL Validity: many varieties Does your measure really measure what it is supposed to measure?

24
Face Validity At the surface level, does it look as if the measure is testing the construct? “This guy seems smart to me, and he got a high score on my IQ measure.”

25
Construct Validity Usually requires multiple studies, a large body of evidence that supports the claim that the measure really tests the construct

26
Internal Validity Did the change in the DV result from the changes in the IV or does it come from something else? The precision of the results

27
Threats to internal validity History – an event happens the experiment Maturation – participants get older (and other changes) Selection – nonrandom selection may lead to biases Mortality – participants drop out or can’t continue Testing – being in the study actually influences how the participants respond

28
External Validity Are experiments “real life” behavioral situations, or does the process of control put too much limitation on the “way things really work?”

29
External Validity Variable representativeness Relevant variables for the behavior studied along which the sample may vary Subject representativeness Characteristics of sample and target population along these relevant variables Setting representativeness Ecological validity - are the properties of the research setting similar to those outside the lab

30
Extraneous Variables Control variables Holding things constant - Controls for excessive random variability Random variables – may freely vary, to spread variability equally across all experimental conditions Randomization A procedure that assures that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. Confound variables Variables that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) Co-varys with both the dependent AND an independent variable

31
“Debugging your study” Pilot studies A trial run through Don’t plan to publish these results, just try out the methods Manipulation checks An attempt to directly measure whether the IV variable really affects the DV. Look for correlations with other measures of the desired effects.

Similar presentations

© 2021 SlidePlayer.com Inc.

All rights reserved.

To make this website work, we log user data and share it with processors. To use this website, you must agree to our Privacy Policy, including cookie policy.

Ads by Google