Experiment Basics: Variables

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Experiment Basics Variables

• Psych 231 Research Methods in Psychology

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Exam 1

• Results

• Mean 81.7
• Range 60-96

• If you want to go over your exam set up a time to
  see me

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So you want to do an experiment?
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So you want to do an experiment?

• Youve got your theory.

• What behavior you want to examine
• Identified what things (variables) you think
  affects that behavior

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So you want to do an experiment?

• Youve 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.

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So you want to do an experiment?

• Youve 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

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Constants vs. Variables

• 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

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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
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Many kinds of Variables

• Independent variables (explanatory)
• Dependent variables (response)
• Extraneous variables
• Control variables
• Random variables
• Confound variables

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Many kinds of Variables

• Independent variables (explanatory)
• Dependent variables (response)
• Extraneous variables
• Control variables
• Random variables
• Confound variables

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Independent 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

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Independent Variables

• 1 factor, 2 levels

1 factor, 3 levels
2 factors, 2 x 3 levels
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Manipulating your independent variable

• 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
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Choosing 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

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Identifying potential problems

• 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

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Demand characteristics

• 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 Dont you think its
  bad to murder unborn children?

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Experimenter Bias

• 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

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• Knowing that you are being measured
• Just being in an experimental setting, people
  dont always respond the way that they normally
  would.
• Cooperative
• Defensive
• Non-cooperative

Reactivity
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Floor effects

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

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Ceiling effects

• When the dependent variable reaches a level that
  cannot be exceeded
• So while there may be an effect of the IV, that
  effect cant 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

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Variables

• Independent variables (explanatory)
• Dependent variables (response)
• Extraneous variables
• Control variables
• Random variables
• Confound variables

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Dependent 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 3s)
• Measure the accuracy of recall

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Choosing your dependent variable

• 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 1900s
• Rating scales strongly agree-agree-undecided-di
  sagree-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)

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Measuring your dependent variables

• Scales of measurement
• Errors in measurement

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Measuring your dependent variables

• Scales of measurement
• Errors in measurement

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Measuring your dependent variables

• Scales of measurement - the correspondence
  between the numbers representing the properties
  that were measuring
• The scale that you use will (partially) determine
  what kinds of statistical analyses you can perform

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Scales of measurement

• Categorical variables
• Quantitative variables

• Nominal scale

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Scales of measurement

• Nominal Scale Consists of a set of categories
  that have different names.

• Label and categorize observations,
• Do not make any quantitative distinctions between
  observations.
• Example
• Eye color

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Scales of measurement

• Categorical variables
• Nominal scale
• Ordinal scale
• Quantitative variables

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Scales of measurement

• Ordinal Scale Consists of a set of categories
  that are organized in an ordered sequence.

• Rank observations in terms of size or magnitude.
• Example
• T-shirt size

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Scales of measurement

• Categorical variables
• Nominal scale
• Ordinal scale
• Quantitative variables
• Interval scale

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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

20º
40º
Not Twice as hot
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Scales of measurement

• Categorical variables
• Nominal scale
• Ordinal scale
• Quantitative variables
• Interval scale
• Ratio scale

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Scales of measurement

• Ratio scale An interval scale with the
  additional feature of an absolute zero point.

• 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

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

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Measuring your dependent variables

• Scales of measurement
• Errors in measurement

• Reliability Validity

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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
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Errors in measurement

• 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?

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

• True score measurement error
• A reliable measure will have a small amount of
  error
• Multiple kinds of reliability

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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
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Reliability

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

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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

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Validity

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

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

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

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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.
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External Validity

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

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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

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Internal Validity

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

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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 cant
  continue
• Testing being in the study actually influences
  how the participants respond

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Variables

• Independent variables (explanatory)
• Dependent variables (response)
• Extraneous variables
• Control variables
• Random variables
• Confound variables

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Control your extraneous variable(s)

• Can you keep them constant?
• Should you make them random variables?

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Extraneous Variables

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

• 90 seconds for recall
• 15 seconds of counting backwards by 3s

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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.

• random order of word presentation
• time of day administered
• what they ate that day
• when they woke up

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Confound Variables

• Confound variables
• Other variables, that havent been accounted for
  (manipulated, measured, randomized, controlled)
  that can impact changes in the dependent
  variable(s)

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Confound Variables

• Confound variables
• Other variables, that havent been accounted for
  (manipulated, measured, randomized, controlled)
  that can impact changes in the dependent
  variable(s)

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Debugging your study

• Pilot studies
• A trial run through
• Dont 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.