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Experiment Basics: Variables

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Title: Experiment Basics: Variables Last modified by: Admin Created Date: 10/6/2010 1:33:25 PM Document presentation format: On-screen Show (4:3) Company – PowerPoint PPT presentation

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Title: Experiment Basics: Variables


1
Experiment Basics Variables
  • Psych 231 Research Methods in Psychology

2
Reminders
  • Journal Summary 1 due in labs this week

3
Many kinds of Variables
  • Independent variables (explanatory)
  • Dependent variables (response)
  • Extraneous variables
  • Control variables
  • Random variables
  • Confound variables

4
Many kinds of Variables
  • Independent variables (explanatory)
  • Dependent variables (response)
  • Extraneous variables
  • Control variables
  • Random variables
  • Confound variables

5
Measuring your dependent variables
  • Scales of measurement
  • Errors in measurement
  • Reliability Validity
  • Sampling error

6
Sampling
Everybody that the research is targeted to be
about
µ 71
The subset of the population that actually
participates in the research
Sample
7
Sampling
  • Allow us to quantify the Sampling error

8
Sampling
  • Goals of good sampling
  • Maximize Representativeness
  • To what extent do the characteristics of those in
    the sample reflect those in the population
  • Reduce Bias
  • A systematic difference between those in the
    sample and those in the population
  • Key tool Random selection

9
Sampling Methods
  • Probability sampling
  • Simple random sampling
  • Cluster sampling
  • Stratified sampling
  • Non-probability sampling
  • Quota sampling
  • Convenience sampling
  • There are advantages and disadvantages to each of
    these methods
  • I recommend that you check out table 6.1 in the
    textbook pp 127-128
  • Here is a nice video (5 mins.) reviewing some of
    the sampling techniques (Statistics Learning
    Centre)

10
Simple random sampling
  • Every individual has a equal and independent
    chance of being selected from the population

11
Cluster sampling
  • Step 1 Identify clusters
  • Step 2 randomly select some clusters
  • Step 3 randomly select from each selected
    cluster

12
Stratified sampling
  • Step 1 Identify distribution of subgroups
    (strata) in population
  • Step 2 randomly select from each group so that
    your sample distribution matches the population
    distribution

13
Quota sampling
  • Step 1 identify the specific subgroups (strata)
  • Step 2 take from each group until desired number
    of individuals (not using random selection)

14
Convenience sampling
  • Use the participants who are easy to get (e.g.,
    volunteer sign-up sheets, using a group that you
    already have access to, etc.)

15
Convenience sampling
  • Use the participants who are easy to get (e.g.,
    volunteer sign-up sheets, using a group that you
    already have access to, etc.)
  • College student bias (World of Psychology Blog)

Who are the people studied in behavioral science
research? A recent analysis of the top journals
in six sub-disciplines of psychology from 2003 to
2007 revealed that 68 of subjects came from the
United States, and a full 96 of subjects were
from Western industrialized countries,
specifically those in North America and Europe,
as well as Australia and Israel (Arnett 2008).
The make-up of these samples appears to largely
reflect the country of residence of the authors,
as 73 of first authors were at American
universities, and 99 were at universities in
Western countries. This means that 96 of
psychological samples come from countries with
only 12 of the world's population. Henrich, J.
Heine, S.J., Norenzayan, A. (2010). The
weirdest people in the world? (free access).
Behavioral and Brain Sciences, 33(2-3), 61-83.
16
Variables
  • Independent variables
  • Dependent variables
  • Measurement
  • Scales of measurement
  • Errors in measurement
  • Extraneous variables
  • Control variables
  • Random variables
  • Confound variables

17
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 havent 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

18
Colors and words
  • Divide into two groups
  • men
  • women
  • Instructions Read aloud the COLOR that the
    words are presented in. When done raise your
    hand.
  • Women first. Men please close your eyes.
  • Okay ready?

19
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
List 1
20
  • Okay, now it is the mens turn.
  • Remember the instructions Read aloud the COLOR
    that the words are presented in. When done raise
    your hand.
  • Okay ready?

21
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
List 2
22
Our results
  • So why the difference between the results for men
    versus women?
  • Is this support for a theory that proposes
  • Women are good color identifiers, men are not
  • Why or why not? Lets look at the two lists.

23
List 2 Men
List 1 Women
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
Matched
Mis-Matched
24
  • What resulted in the performance difference?
  • Our manipulated independent variable (men vs.
    women)
  • The other variable match/mis-match?
  • Because the two variables are perfectly
    correlated we cant tell
  • This is the problem with confounds

Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
25
  • What DIDNT result in the performance difference?
  • Extraneous variables
  • Control
  • of words on the list
  • The actual words that were printed
  • Random
  • Age of the men and women in the groups
  • Majors, class level, seating in classroom,
  • These are not confounds, because they dont
    co-vary with the IV

Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
Blue Green Red Purple Yellow Green Purple Blue Red
Yellow Blue Red Green
26
Experimental Control
  • Our goal
  • To test the possibility of a systematic
    relationship between the variability in our IV
    and how that affects the variability of our DV.
  • Control is used to
  • Minimize excessive variability
  • To reduce the potential of confounds (systematic
    variability not part of the research design)

27
Experimental Control
  • Our goal
  • To test the possibility of a systematic
    relationship between the variability in our IV
    and how that affects the variability of our DV.

NRexp Manipulated independent variables (IV)
  • Our hypothesis the IV will result in changes
    in the DV

NRother extraneous variables (EV) which covary
with IV
  • Condfounds

Random (R) Variability
  • Imprecision in measurement (DV)
  • Randomly varying extraneous variables (EV)

28
Experimental Control Weight analogy
  • Variability in a simple experiment

T NRexp NRother R
29
Experimental Control Weight analogy
  • Variability in a simple experiment

T NRexp NRother R
Control group
Treatment group
30
Experimental Control Weight analogy
  • If there is an effect of the treatment then NRexp
    will ? 0

Control group
Treatment group
Difference Detector
Our experiment can detect the effect of the
treatment
31
Things making detection difficult
  • Potential Problems
  • Confounding
  • Excessive random variability

Difference Detector
32
Potential Problems
  • Confound
  • If an EV co-varies with IV, then NRother
    component of data will be present, and may lead
    to misattribution of effect to IV

IV
DV
EV
33
Confounding
  • Confound
  • Hard to detect the effect of NRexp because the
    effect looks like it could be from NRexp but
    could be due to the NRother

NR
other
NR
exp
Difference Detector
Experiment can detect an effect, but cant tell
where it is from
34
Confounding
  • Confound
  • Hard to detect the effect of NRexp because the
    effect looks like it could be from NRexp but
    could be due to the NRother

These two situations look the same
NR
other
Difference Detector
There is not an effect of the IV
There is an effect of the IV
35
Potential Problems
  • Excessive random variability
  • If experimental control procedures are not
    applied
  • Then R component of data will be excessively
    large, and may make NRexp undetectable

36
Excessive random variability
  • If R is large relative to NRexp then detecting a
    difference may be difficult

Difference Detector
Experiment cant detect the effect of the
treatment
37
Reduced random variability
  • But if we reduce the size of NRother and R
    relative to NRexp then detecting gets easier
  • So try to minimize this by using good measures of
    DV, good manipulations of IV, etc.

Difference Detector
Our experiment can detect the effect of the
treatment
38
Controlling Variability
  • How do we introduce control?
  • Methods of Experimental Control
  • Constancy/Randomization
  • Comparison
  • Production

39
Methods of Controlling Variability
  • Constancy/Randomization
  • If there is a variable that may be related to the
    DV that you cant (or dont want to) manipulate
  • Control variable hold it constant
  • Random variable let it vary randomly across all
    of the experimental conditions

40
Methods of Controlling Variability
  • Comparison
  • An experiment always makes a comparison, so it
    must have at least two groups
  • Sometimes there are control groups
  • This is often the absence of the treatment

Training group
No training (Control) group
  • Without control groups if is harder to see what
    is really happening in the experiment
  • It is easier to be swayed by plausibility or
    inappropriate comparisons
  • Useful for eliminating potential confounds

41
Methods of Controlling Variability
  • Comparison
  • An experiment always makes a comparison, so it
    must have at least two groups
  • Sometimes there are control groups
  • This is often the absence of the treatment
  • Sometimes there are a range of values of the IV

1 week of Training group
2 weeks of Training group
3 weeks of Training group
42
Methods of Controlling Variability
  • Production
  • The experimenter selects the specific values of
    the Independent Variables

1 week of Training group
2 weeks of Training group
3 weeks of Training group
  • Need to do this carefully
  • Suppose that you dont find a difference in the
    DV across your different groups
  • Is this because the IV and DV arent related?
  • Or is it because your levels of IV werent
    different enough

43
Experimental designs
  • So far weve covered a lot of the about details
    experiments generally
  • Now lets consider some specific experimental
    designs.
  • Some bad (but common) designs
  • Some good designs
  • 1 Factor, two levels
  • 1 Factor, multi-levels
  • Between within factors
  • Factorial (more than 1 factor)

44
Poorly designed experiments
  • Bad design example 1 Does standing close to
    somebody cause them to move?
  • hmm thats an empirical question. Lets see
    what happens if
  • So you stand closely to people and see how long
    before they move
  • Problem no control group to establish the
    comparison group (this design is sometimes called
    one-shot case study design)

45
Poorly designed experiments
  • Bad design example 2
  • Testing the effectiveness of a stop smoking
    relaxation program
  • The participants choose which group (relaxation
    or no program) to be in

46
Poorly designed experiments
  • Bad design example 2
  • Non-equivalent control groups

Independent Variable
Dependent Variable
Self Assignment
Training group
Measure
participants
No training (Control) group
Measure
Problem selection bias for the two groups, need
to do random assignment to groups
47
Poorly designed experiments
  • Bad design example 3 Does a relaxation program
    decrease the urge to smoke?
  • Pretest desire level give relaxation program
    posttest desire to smoke

48
Poorly designed experiments
  • Bad design example 3
  • One group pretest-posttest
  • design

Independent Variable
Dependent Variable
Dependent Variable
participants
Pre-test
Training group
Post-test Measure
Add another factor
Problems include history, maturation, testing,
and more
49
1 factor - 2 levels
  • Good design example
  • How does anxiety level affect test performance?
  • Two groups take the same test
  • Grp1 (moderate anxiety group) 5 min lecture on
    the importance of good grades for success
  • Grp2 (low anxiety group) 5 min lecture on how
    good grades dont matter, just trying is good
    enough
  • 1 Factor (Independent variable), two levels
  • Basically you want to compare two treatments
    (conditions)
  • The statistics are pretty easy, a t-test

50
1 factor - 2 levels
  • Good design example
  • How does anxiety level affect test performance?

51
1 factor - 2 levels
  • Good design example
  • How does anxiety level affect test performance?

anxiety
80
60
Observed difference between conditions
T-test
Difference expected by chance
52
1 factor - 2 levels
  • Advantages
  • Simple, relatively easy to interpret the results
  • Is the independent variable worth studying?
  • If no effect, then usually dont bother with a
    more complex design
  • Sometimes two levels is all you need
  • One theory predicts one pattern and another
    predicts a different pattern

53
1 factor - 2 levels
  • Disadvantages
  • True shape of the function is hard to see
  • Interpolation and Extrapolation are not a good
    idea

54
1 factor - 2 levels
  • Disadvantages
  • True shape of the function is hard to see
  • Interpolation and Extrapolation are not a good
    idea

55
1 Factor - multilevel experiments
  • For more complex theories you will typically need
    more complex designs (more than two levels of one
    IV)
  • 1 factor - more than two levels
  • Basically you want to compare more than two
    conditions
  • The statistics are a little more difficult, an
    ANOVA (Analysis of Variance)

56
1 Factor - multilevel experiments
  • Good design example (similar to earlier ex.)
  • How does anxiety level affect test performance?
  • Two groups take the same test
  • Grp1 (moderate anxiety group) 5 min lecture on
    the importance of good grades for success
  • Grp2 (low anxiety group) 5 min lecture on how
    good grades dont matter, just trying is good
    enough
  • Grp3 (high anxiety group) 5 min lecture on how
    the students must pass this test to pass the
    course

57
1 factor - 3 levels
58
1 Factor - multilevel experiments
60
59
1 Factor - multilevel experiments
  • Advantages
  • Gives a better picture of the relationship
    (function)
  • Generally, the more levels you have, the less you
    have to worry about your range of the independent
    variable

60
Relationship between Anxiety and Performance
61
1 Factor - multilevel experiments
  • Disadvantages
  • Needs more resources (participants and/or
    stimuli)
  • Requires more complex statistical analysis
    (analysis of variance and pair-wise comparisons)

62
Pair-wise comparisons
  • The ANOVA just tells you that not all of the
    groups are equal.
  • If this is your conclusion (you get a
    significant ANOVA) then you should do further
    tests to see where the differences are
  • High vs. Low
  • High vs. Moderate
  • Low vs. Moderate
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