Manipulation and Measurement of Variables

Manipulation and Measurement of Variables Psych 231: Research Methods in Psychology

For labs this week you’ll need todownload(and bring to lab): • Class experiment packet Announcements

Independent variables • How do we manipulate these? • Dependent variables • How do we measure these? • Extraneous variables • Control variables • Random variables • Confound variables Variables

Methods of manipulation • Straightforward • Stimulus manipulation - different conditions use different stimuli • Instructional manipulation – different groups are given different instructions • Staged • Event manipulation – manipulate characteristics of the context, setting, etc. • Subject (Participant)– there are (pre-existing mostly) differences between the subjects in the different conditions • leads to a quasi-experiment Abstract vs. concrete words Has an “a” vs. “ISU related” Manipulating your independent variable

Choosing the right levels of your independent variable • Review the literature • Do a pilot experiment • Consider the costs, your resources, your limitations • Be realistic • Pick levels found in the “real world” • Pick a large enough range to show the effect • And in the middle of the range Choosing your independent variable

These are things that you want to try to avoid by careful selection of the levels of your IV (may be issues for your DV as well). • Demand characteristics • Experimenter bias • Reactivity • Floor and ceiling effects Identifying potential problems

Characteristics of the study that may give away the purpose of the experiment • May influence how the participants behave in the study • Examples: • Experiment title: The effects of horror movies on mood • Obvious manipulation: Ten psychology students looking straight up • Biased or leading questions: Don’t you think it’s bad to murder unborn children? Demand characteristics

Experimenter bias (expectancy effects) • The experimenter may influence the results (intentionally and unintentionally) • E.g., Clever Hans • One solution is to keep the experimenter (as well as the participants) “blind” as to what conditions are being tested Experimenter Bias

Knowing that you are being measured • Just being in an experimental setting, people don’t always respond the way that they “normally” would. • Cooperative • Defensive • Non-cooperative Reactivity

A value below which a response cannot be made • As a result the effects of your IV (if there are indeed any) can’t be seen. • Imagine a task that is so difficult, that none of your participants can do it. Floor effects

When the dependent variable reaches a level that cannot be exceeded • So while there may be an effect of the IV, that effect can’t be seen because everybody has “maxed out” • Imagine a task that is so easy, that everybody scores a 100% • To avoid floor and ceiling effects you want to pick levels of your IV that result in middle level performance in your DV Ceiling effects

Independent variables • How do we manipulate these? • Dependent variables • How do we measure these? • Extraneous variables • Control variables • Random variables • Confound variables Variables

The variables that are measured by the experimenter • They are “dependent” on the independent variables (if there is a relationship between the IV and DV as the hypothesis predicts). Dependent Variables

How to measure your your construct: • Can the participant provide self-report? • Introspection • Rating scales • Is the dependent variable directly observable? • Choice/decision (sometimes timed) • Is the dependent variable indirectly observable? • Physiological measures (e.g. GSR, heart rate) • Behavioral measures (e.g. speed, accuracy) Choosing your dependent variable

Scales of measurement • Errors in measurement Measuring your dependent variables

Scales of measurement - the correspondence between the numbers representing the properties that we’re measuring • The scale that you use will (partially) determine what kinds of statistical analyses you can perform Measuring your dependent variables

Categorical variables • Quantitative variables • Nominal scale Scales of measurement

brown, hazel blue, green, • Label and categorize observations, • Do not make any quantitative distinctions between observations. • Example: • Eye color: • Nominal Scale: Consists of a set of categories that have different names. Scales of measurement

Categorical variables • Nominal scale • Ordinal scale • Quantitative variables Scales of measurement

Small, Med, Lrg, XL, XXL • Rank observations in terms of size or magnitude. • Example: • T-shirt size: • Ordinal Scale: Consists of a set of categories that are organized in an ordered sequence. Scales of measurement

Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale Scales of measurement

Interval Scale: Consists of ordered categories where all of the categories are intervals of exactly the same size. • With an interval scale, equal differences between numbers on the scale reflect equal differences in magnitude. • Ratios of magnitudes are not meaningful. • Example: Fahrenheit temperature scale 40º 20º “Not Twice as hot” Scales of measurement

Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale 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. Scales of measurement

Ratio scale 8 cards high Scales of measurement

Interval scale 5 cards high Scales of measurement

Ratio scale Interval scale 8 cards high 5 cards high 0 cards high means ‘as tall as the table’ 0 cards high means ‘no height’ Scales of measurement

Categorical variables • Nominal scale • Ordinal scale • Quantitative variables • Interval scale • Ratio scale “Best” Scale? • Given a choice, usually prefer highest level of measurement possible Scales of measurement

Scales of measurement • Errors in measurement Measuring your dependent variables

Example: Measuring intelligence? • 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? Reliability & Validity

Reliability • If you measure the same thing twice (or have two measures of the same thing) do you get the same values? • Validity • Does your measure really measure what it is supposed to measure? • Does our measure really measure the construct? • Is there bias in our measurement? Errors in measurement

Reliability = consistency Validity = measuring what is intended reliablevalid unreliable invalid reliable invalid Reliability & Validity

True score + measurement error • A reliable measure will have a small amount of error • Multiple “kinds” of reliability 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 Reliable Unreliable 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) 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 Reliability

Does your measure really measure what it is supposed to measure? • There are many “kinds” of validity Validity

VALIDITY CONSTRUCT INTERNAL EXTERNAL FACE CRITERION- ORIENTED PREDICTIVE CONVERGENT CONCURRENT DISCRIMINANT Many kinds of Validity

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

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

The precision of the results • Did the change result from the changes in the DV or does it come from something else? 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 Threats to internal validity

Control variables • Holding things constant - Controls for excessive random variability Extraneous Variables

Random variables – may freely vary, to spread variability equally across all experimental conditions • Randomization • A procedures that assure that each level of an extraneous variable has an equal chance of occurring in all conditions of observation. • On average, the extraneous variable is not confounded with our manipulated variable. Extraneous Variables

Can you keep them constant? • Should you make them random variables? • Two things to watch out for: • Experimenter bias • Demand characteristics Control your extraneous variable(s)

Confound variables • Other variables, that haven’t been accounted for (manipulated, measured, randomized, controlled) that can impact changes in the dependent variable(s) Confound Variables

Manipulation and Measurement of Variables