Experiment Basics: Variables

Experiment Basics: Variables Psych 231: Research Methods in Psychology

Mean: 81.7% • Range: 60-96% • Results • If you want to go over your exam set up a time to see me Exam 1

From the creators of American Idol and So You Think You can Dance Mon & Wed, 1c on FOX So you want to do an experiment?

What behavior you want to examine • Identified what things (variables) you think affects that behavior • You’ve got your theory. So you want to do an experiment?

You’ve got your theory. • Next you need to derive predictions from the theory. • These should be stated as hypotheses. • In terms of conceptual variables or constructs • Conceptual variables are abstract theoretical entities • Consider our class experiment • Hypotheses: • What you try to memorize & how you try to memorize it will impact memory performance. So you want to do an experiment?

You’ve got your theory. • Next you need to derive predictions from the theory. • Now you need to design the experiment. • You need to operationalize your variables in terms of how they will be: • Manipulated • Measured • Controlled • Be aware of the underlying assumptions connecting your constructs to your operational variables • Be prepared to justify all of your choices So you want to do an experiment?

Characteristics of the psychological situations • Constants: have the same value for all individuals in the situation • Variables: have potentially different values for each individual in the situation • Variables in our experiment: • Levels of processing • Type of words • Memory performance • time for recall • kind of filler task given • pacing of reading the words on the list • … Constants vs. Variables

Conceptual vs. Operational • Conceptual variables (constructs) are abstract theoretical entities • Operational variables are defined in terms within the experiment. They are concrete so that they can be measured or manipulated Conceptual How we memorize (Levels of processing) Kinds of things Memory Operational Has an ‘a’ ‘Related to ISU’ Words rated as abstract or concrete Memory test Variables

Independent variables (explanatory) • Dependent variables (response) • Extraneous variables • Control variables • Random variables • Confound variables • Correlational designs • have similar functions Many kinds of Variables

Independent variables (explanatory) • Dependent variables (response) • Extraneous variables • Control variables • Random variables • Confound variables Many kinds of Variables

The variables that are manipulated by the experimenter (sometimes called factors) • Each IV must have at least two levels • Remember the point of an experiment is comparison • Combination of all the levels of all of the IVs results in the different conditions in an experiment Independent Variables

Factor A Condition 1 Condition 2 Factor A Cond 1 Cond 2 Cond 3 Factor B Cond 1 Cond 2 Cond 3 Factor A Cond 4 Cond 5 Cond 6 1 factor, 2 levels 1 factor, 3 levels 2 factors, 2 x 3 levels Independent 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” • Pay attention to the range of the levels • Pick a large enough range to show the effect • Aim for 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 (explanatory) • Dependent variables (response) • 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). • Consider our class experiment • Conceptual level:Memory • Operational level: Recall test • Present list of words, participants make a judgment for each word • 15 sec. of filler (counting backwards by 3’s) • Measure the accuracy of recall Dependent Variables

How to measure your your construct: • Can the participant provide self-report? • Introspection – specially trained observers of their own thought processes, method fell out of favor in early 1900’s • Rating scales – strongly agree-agree-undecided-disagree-strongly disagree • 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 • Reliability & Validity 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

Experiment Basics: Variables