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What are the 4 SCALES OF MEASUREMENTS?
- Interval Scale
- Ratio Scale
- Ordinal Scale
- Nominal Scale
HINT: “IRON”
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Define Interval Scale:
- - Always Numerical!
- - Zero does not indicate absence of variable Example: 0 degrees does not mean there is no temp.
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Define Ratio Scale:
- -
- Similar to Interval Scale but this can/has a true
- zero.
- - Doubling Principle: 100 degrees is not twice as hot as 50 degrees
- but 100$ is twice as much as $50
-
- Example: 0 seconds, 0 Dollars, height, weight,
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Define Ordinal Scale:
- -
- Mutually Exclusive you can only fall into one
- category!
- -
- Ordered / Ranked (1st 2nd
- or 3rd place or Small/Med/Lg)
Can be Numerical or Nonnumerical
-
-
Mutually Exclusive you can only fall into one
- category!
-
- Not Ordered/Ranked (1 a = Married 2b = Divorced 3c = Never been Married)
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Define Variable:
measurable characteristic
-
IV
- Antecedent
- variable/manipulated (a.k.a. predictor variable)
-
DV
- Variable measured but not manipulated directly; it detects
- influence of IV (a.k.a. outcome variable)
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What are the 2 types of data?
Qualitative:
- Measured with nominal or ordinal scales
-Vary in categories/kind
Quantitative:
-Measured with interval or ratio scales
-Vary in Quantity, thus Quantitative
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Which of the
following variables is measured on a nominal scale?
-1. The number of cigarettes a person smokes in a day
-2. Level in school: freshman = 1, sophomore = 2, etc.
-3. Age in years
- 4. How happy are you? 0 (Not at all) -------- 10 (Very)
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Which of the following quantitative variables is measured on a ratio
scale?
Blood pressure level
Heart beat rate
Age in years
Number of dates one had last week
The number of hours of TV watched in a month
All of the above
None of the above
I haven’t the faintest!
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What are the 3 methods for checking on the manipulation of the IV? Explain them
- 1. Interview participants to ensure IV
- had desired effect
- - 2. Behavioral (or physiological) indicator,
- such as blood pressure if manipulating stress (IV)
- - 3. Pre-testing, or pilot testing
- before running actual study
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What is the effect of a single IV called?
Main Effect
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What is more than 1 IV called? Explain advantage of using more than 1 IV.
- Called “interaction effect”
- ADV of using more than one IV modifies and shows interaction/effects of other IV’s.
-
What is a “construct”?
- - Abstract idea or consistent set of behaviors
- with a label on it. (Ex: Athletic,Friendly, Cute)
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What is Construct Validity? And What is it established by?
-Extent to which an abstract construct can be inferred from the operational definition of that construct.
- - Construct Validity is established by
- -Clear operational definition.
- -Face Validity: the face of it, looks valid.
- -Convergent Validity: corresponds to other measures of same construct
- Divergent Validity: does not correspond to measures of different constructs.
-
RELIABILITY refers to …..
the extent to which the IV or DV is consistent or stable over time.
-
What can be established by replication?
- - Reliabilty can be established by replication.
- - Meaning if someone else was to conduct study,experimenter should get same measures!
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What is External validity?
- -
- The extent to which study findings can be
- generalized to “the real world”
-Results should play to be true in real world as well.
-
What is Internal validity?
- Extent to which one can accurately state that
- the IV produced any observed effects on DV.
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What is an extraneous variable that varies with IV called?
Cofounding Variables
-
Extraneous variables are controlled by (2):
- Eliminating it! (or build it into study, like
- another IV)
- Keep constant/balance across all levels of IV
- (via random assignment or matching)
-
List of Extraneous Variables :
History: Some influential event occurs between a pre & post test (CONTROL GROUP)
Maturation: Biological/psychological changes to participant (CONTROL GROUP)
Instrumentation: Assessment tool/person changes during study (IOA, TRAINING, “BLIND” PROCEDURES)
- Selection: Bias in selection method means characteristics of participants/groups—not under study—may affect DV (RANDOMIZATION; PRETEST TO
- ENSURE EQUIVALENT)
- Statistical regression: Groups are chosen due to high or low measurements which will regress (trend back) toward the mean over
- repeated measures—this is also a selection bias (PRETEST; RANDOMIZATION)
- Mortality/participant attrition: Loss of participants
- from study, especially when unequal across groups (CAREFUL PLANNING AND
- INCENTIVES)
Sequencing/order effect: Prior study condition (not IV) affects subsequent study condition (COUNTERBALANCING)
- Participant sophistication: Participants become familiar with subject matter or with materials/procedure (more of a problem with
- repeated measures designs) (CONTROL GROUP)
Testing effects: Participants may change their answers based on thinking further about questions and may learn from repeated testing (CONTROL GROUP)
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Be able to list and define experimenter effects.
Experimenter Attributes: Characteristics or skills of experimenter may influence results (e.g., gender, method of delivery, etc.)
- Experimenter Expectancies:
- -Recording bias
- -Bias when interpreting data
- -Can also
- actually affect participants’ responses
-
Your research assistant has a
crush on one of your research participants in a study designed to help folks to
stop smoking. They change that participant’s criteria for what is considered
smoking (the DV) from simply putting the cigarette in one’s mouth to actually
breathing in smoke. This made it look like the DV decreased dramatically. This
is called:
Statistical regression
Mortality problem
Maturation problem
History problem
Selection problem
Instrumentation problem
- Statistical regression
- Mortality problem
- Maturation problem
- History problem
- Selection problem
- Instrumentation problem
-
You are measuring the effects
of caffeine on people who experience panic attacks. One of your caffeine
condition participants is hospitalized mid-study. This could be an example of….
Statistical regression
Mortality problem
Maturation problem
History problem
Selection problem
Instrumentation problem
- Statistical regression
- Mortality problem
- Maturation problem
- History problem
- Selection problem
- Instrumentation problem
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How does one CONTROL RECORDING
ERRORS? (4)
HINT: MAMB
1. Maintain observer awareness: provide thorough and continuous training
- 2. Automation: computer/videos,etc. (take out human error and minimize bias/expectancies)
- 3. Multiple observers: measure
- inter-observer agreement (IOA)
- 4. “Blind” data recorders:
- unaware of conditions in effect (minimize bias/expectancies)
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Methods of controlling experimenter expectancy errors.
- Blind technique:
- Experimenter unaware of conditions
- Partial blind technique: Keep experimenter “blind” as long as possible
- Automation: Use
- computer, video tapes, audio recordings
-
A micro-switch in the base of little Johnny's chair allows us to detect if he gets off his seat.The experiment is investigating if caffeine increases these out of seat
episodes. This is an example of…
Blind technique
Multiple observers
Number 1 & 2
Nada!
Blind technique
Multiple observers
Number 1 & 2
Nada!
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What are the 2 Participant effects:
Demand characteristics: Cues that give participants clues as to the nature of the experiment and they change their behavior (consciously or not; positively or negatively—”screw-you effect”)
Positive self-presentation: Participants try to appear positive (and often use demand chars. in this quest!)
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How to control Participant Effects?
1) Double-Blind Placebo: Experimenter and participant unaware of the experimental conditions in effect
2) Deception: Misdirect participants as to the nature of the experiment.
3) Disguised Experiment: Participant is not aware they are participating in an experiment.
4) Independent Measures of the DV: Measure the DV away from the experimental condition.
5) Procedural control/control of participant interpretation:
Gaininsight into participants perceptions
^^^^ex's:
A. Retrospective verbal report (exit interview)
B. Concurrent Verbal Reports (think aloud)
C. Sacrifice groups (terminate at various times)
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What is the difference between random selection and random assignment and the goals of each.
Random selection: is how you draw the sample of people for your study from a population
- Every member of the population as an equal chance of being selected for
- the sample
The goal is a representative sample.
Random assignment: is how you assign the sample that you draw to different groups or treatments in your study
- Every member of the sample has an equal chance of being assigned to
- each condition.
- The goal is to distribute potential extraneous variables equally to
- various groups.
By chance may get bias The larger the sample, the less likely bias will occur
-
Define Matching: What are the two benefits?
- Matching: equating groups on one or more variables by measuring participants on those variables and assigning them in equal amounts
- to the various groups.
- Advantages:
- increases sensitivity of experiment
- morebalanced groups
- Disadvantages: time-consuming;
- pre-measuring
- induces demand characteristics
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