1. Research Methods in Psychology
Independent Groups Designs

2. Why Psychologists Conduct Experiments
To test
hypotheses derived from theories
effectiveness of treatments and programs
Third goal of psychological research
explanation
examine the causes of behavior

3. Experimental Research
An experiment must include
independent variable (IV)‏
dependent variable (DV)‏
An independent variable
manipulated (controlled) by experimenter
at least two conditions (levels)‏
“treatment” and “control”

4. Experimental Research
dependent variables
measured by experimenter
used to determine effect of IV
In most experiments, researchers measure several dependent variables to assess effect of IV

5. Example: Body Image Among Young Girls
Dittmar, Halliwell, and Ives (2006)
Research question
Does exposure to very thin body images cause young girls to experience negative feelings about their own body?
Independent Variable
version of picture book with three levels
Barbie (very thin body image)‏
Emme (realistic body image)‏
Neutral (no body images)‏

6. Body Image Among Young Girls, continued
Dependent Variables
Several measured body image and body dissatisfaction, including:
Child Figure Rating Scale
rate perceived actual body shape
rate ideal body shape
obtain difference score:
score of zero: no body shape dissatisfaction
positive score: a desire to be bigger
negative score: desire to be thinner (body dissatisfaction)‏

7. Body Image Among Young Girls, continued
Dittmar et al.’s hypothesis
Young girls who are exposed to the very thin body image (Barbie) will experience greater body dissatisfaction than young girls who are exposed to realistic body images (Emme) or neutral images.

8. Experimental Control and Internal Validity
An experiment has internal validity when we can state confidently that the independent variable caused differences between groups on the dependent variable
a causal inference
alternative explanations for a study’s findings are ruled out

9. Control and Internal Validity, continued
Example:
Suppose young girls who view the Barbie images are more overweight or own more Barbie dolls than girls in the other conditions
How do we know viewing the Barbie images in the experiment caused them to experience greater body dissatisfaction?
What are some alternative explanations?

10. Three conditions for causal inference
Causal Inferences
Three conditions for causal inference
Covariation
relationship between IV and DV
example: young girls’ body dissatisfaction covaried with experimental condition
correlation does not imply causation

11. Causal Inferences, continued
Time-order relationship
presumed cause precedes the effect
example: version of images (cause) was manipulated prior to measuring body dissatisfaction (effect)‏
How can we be sure girls in Barbie condition didn’t have greater body dissatisfaction than the other girls before the manipulation (effect precedes cause)?

12. Causal Inferences, continued
Elimination of plausible alternative causes
use control techniques to eliminate other explanations
example: if the three groups differ in ways other than the type of images they viewed, these differences are alternative explanations for the study’s findings

13. Causal Inferences, continued
Confoundings
when the IV is allowed to covary with a different, potential independent variable
confoundings represent alternative explanations for a study’s findings
an experiment that is free of confoundings has internal validity

14. Causal Inferences, continued
Example of confounding
suppose that after viewing the Barbie images, young girls in this condition are interviewed by a counselor to make sure they’re okay after exposure to the very thin images; as part of this interview, they’re asked specifically about feelings toward their body
suppose, too, that young girls in the Emme and Neutral conditions are not interviewed

15. Causal Inferences, continued
What is the confounding?
version of images (IV of interest) covaries with interview (present, absent)‏
viewing Barbie images is always paired with interview
viewing Emme or neutral images is always paired with no interview
alternative explanation for findings cannot be ruled out
greater body dissatisfaction in Barbie condition could be explained by interview, not viewing Barbie images
Note: This confounding was not present in the Dittmar et al. study

16. Two control techniques to eliminate alternative explanations
holding conditions constant
balancing
With proper use of control techniques, an experiment has internal validity

17. Control Techniques, continued
Holding conditions constant
Independent variable: groups in the different conditions have different experiences
example: view Barbie, or Emme, or neutral images
Experiences should differ only in terms of the independent variable
The only thing we allow to vary across groups are the IV conditions—everything else should be the same for the groups of the experiment

18. Control Techniques, continued
Example of holding conditions constant
Dittmar et al. (2006) held constant
all the young girls listened to the same story
all were given the same instructions
all completed the same questions after the story
What if only girls in the Barbie condition listened to the story and girls in the other two conditions sat quietly?
alternative explanation: listening to a story caused the different outcomes

19. Control Techniques, continued
Balancing
some alternative explanations for a study’s findings concern characteristics of participants
example
What if girls in Barbie condition were more overweight, owned more Barbie dolls, or greater body dissatisfaction even before they viewed the picture books?

20. Control Techniques, continued
Some variables cannot be held constant
subjects’ characteristics cannot be held constant
participants all have the same body weight
same number of Barbie dolls
same preexisting levels of body dissatisfaction
same everything
Balancing controls for alternative explanations due to subject characteristics
Goal: make sure that on average, participants (as a group) in each condition are essentially equivalent

21. Control Techniques, continued
How to balance subject characteristics across the levels of the experiment:
Participants are assigned to conditions using some random procedure (e.g., two conditions: flip a coin)‏
Random assignment creates, on average, equivalent groups of participants in the experimental conditions
Rule out alternative explanations due to subject characteristics

22. Independent Groups Designs
different individuals participate in each condition of the experiment (i.e., no overlap of participants across conditions)‏
three types
random groups design
matched groups design
natural groups design

23. Individuals are randomly assigned to conditions of the IV
Random Groups Designs
Individuals are randomly assigned to conditions of the IV
Groups of participants are equivalent, on average, before the IV manipulation
Any differences between groups on dependent variable are caused by independent variable (if conditions are held constant)‏
Dittmar et al. (2006) study used a random groups design

24. Random Groups Designs, continued
Block randomization
A “block” is a random order of all conditions in the experiment
Example: a random order of conditions A, B, C could be B C A
1st participant assigned to condition B
2nd participant—condition C
3rd participant—condition A
Generate random orders until goal for number of participants in each condition is met (e.g., 10 in each condition)‏

25. Random Groups Designs, continued
Advantages of block randomization
creates groups of equal size for each condition
controls for time-related events that occur during course of experiment
natural changes in experimental conditions, experimenters, participants that occur over time are balanced across the experimental conditions
as with all random assignment, block randomization balances subject characteristics across conditions of the experiment

26. Threats to Internal Validity
Ability to make causal inferences is jeopardized when
intact groups are used
extraneous variables are not controlled
selective subject loss occurs
demand characteristics and experimenter effects are not controlled

27. Threats to Internal Validity, continued
Intact groups
these groups exist before experiment
examples
children in different classrooms, departments within an organization, sections of Introductory Psychology course
individuals are not randomly assigned to intact groups
when intact groups (not individuals) are randomly assigned to conditions, subject characteristics are not balanced
do not use intact groups

28. Threats to Internal Validity, continued
Extraneous variables
practical considerations when conducting an experiment may create confoundings
examples of extraneous variables
number of participants in each session
different experimenters
different rooms where experiment is conducted

29. Threats to Internal Validity, continued
Example
Suppose two experimenters help to conduct an experiment. One experimenter tests all of the participants in the treatment condition and the second experimenter tests all of the participants in the control condition.
This experiment is confounded because any differences on the DV may be due to the IV (treatment, control) or to the two experimenters.

30. Threats to Internal Validity, continued
How to control extraneous variables
Balancing
randomly assign extraneous variables across the conditions of the experiment
example: Each experimenter conducts both treatment and control sessions, and are randomly assigned to administer a condition at any particular time
Holding conditions constant
hold extraneous variables constant across the conditions of the experiment
example: one experimenter conducts both treatment and control sessions

31. Threats to Internal Validity, continued
Subject loss (attrition)‏
occurs when participants fail to complete an experiment
equivalent groups formed at beginning of an experiment through random assignment may no longer be equivalent
two types of attrition
mechanical subject loss
selective subject loss

32. Threats to Internal Validity, continued
Mechanical subject loss
when equipment failure or experimenter error results in participant’s inability to complete experiment
often due to chance factors
likely to occur equally across conditions of experiment
because mechanical subject loss is due to chance events, it does not threaten internal validity of experiment

33. Threats to Internal Validity, continued
Selective subject loss occurs when
participants are lost differentially across conditions
some characteristic of participant is responsible for the loss
the subject characteristic is related to the dependent variable
example:
suppose a treatment for depression is compared to a no- treatment control condition
selective subject loss might occur if people drop out of the control condition more than the treatment condition

34. Threats to Internal Validity, continued
Placebo control and double-blind experiments
demand characteristics are cues participants use to guide their behavior in a study
example:
in drug treatment research, demand characteristics suggest to participants they will improve as a result of the drug
participants may expect to improve
expectations may cause improvement, not the drug

35. Threats to Internal Validity, continued
Placebo control group
used to assess whether participants’ expectancies contribute to outcome of experiment
participants in placebo control group receive a placebo (inert substance), but believe they may be receiving an effective treatment
if participants who receive the actual drug improve more than participants who receive the placebo, we gain confidence that the drug produced the beneficial outcome, rather than expectancies

36. Threats to Internal Validity, continued
Experimenter effects
potential biases that occur when experimenter’s expectancies regarding the outcome of the experiment influence their behavior toward participants
control by keeping experimenters and observers “blind” or unaware of the expected results

37. Threats to Internal Validity, continued
Double-blind experiment
procedures in which both participants and experimenters/observers are unaware of the condition being administered
controls both
demand characteristics
experimenter effects
allows researchers to rule out participants’ and experimenters’ expectancies as alternative explanations for a study’s outcome

38. Analysis and Interpretation of Experimental Findings
We rely on statistical analysis to
claim an independent variable produced an effect on a dependent variable
rule out the alternative explanation that chance produced differences among the groups in an experiment
Replication
best way to determine whether findings are reliable
repeat experiment and see if same results are obtained

39. Analysis of Experimental Designs
Three steps
Check the data
errors? outliers?
Describe the results
descriptive statistics such as means, standard deviations
Confirm what the data reveal
inferential statistics

40. Analysis of Experiments, continued
Descriptive Statistics
Mean (central tendency)‏
average score on a DV, computed for each group
not interested in each individual score, but how people responded on average in a condition
Standard deviation (variability)‏
average distance of each score from the mean of a group
not everyone responds the same way to an experimental condition

41. Analysis of Experiments, continued
Effect size
measure of the strength of the relationship between the IV and DV
Cohen’s d
difference between treatment and control means
average variability for all participants’ scores
Guidelines for interpreting Cohen’s d:
small effect of IV: d = .20
medium effect of IV: d = .50
large effect of IV: d = .80

42. Analysis of Experiments, continued
Meta-analysis
summarize the effect sizes across many experiments that investigate the same IV or DV
select experiments to include based on their internal validity and other criteria
allows researchers to gain confidence in general psychological principles

43. Analysis of Experiments, continued
Confirm what the data reveal
use inferential statistics to determine whether the IV had a reliable effect on the DV
rule out whether findings are due to chance (error variation)‏
two types of inferential statistics
Null Hypothesis Significance Testing
Confidence intervals

44. Analysis of Experiments, continued
Null Hypothesis Significance Testing
statistical procedure to determine whether mean difference between conditions is greater than what might be expected due to chance or error variation
the effect of an IV on the DV is statistically significant when the probability of the results being due to chance is low

45. Analysis of Experiments, continued
Steps for Null Hypothesis Testing
(1) Assume the null hypothesis is true
The null hypothesis assumes the population means for groups in the experiment are equal.
example:
the population mean for body dissatisfaction following Barbie images is equal to the population mean for Emme images or neutral images

46. Analysis of Experiments, continued
(2) Use sample means to estimate population means.
example:
mean body dissatisfaction for Barbie = -.76
mean body dissatisfaction for Emme = 0.00
mean body dissatisfaction for neutral = 0.00
difference between Barbie and Emme/neutral = -.76
Is the observed mean difference (-.76) greater than what is expected when we assume the null hypothesis is true (zero)?

47. Analysis of Experiments, continued
(3) Compute the appropriate inferential statistic.
t-test: test the difference between two sample means
F-test (ANOVA): test the difference among three or more sample means
(4) Identify the probability associated with the inferential statistic
p value is printed in computer output or can be found in statistical tables

48. Analysis of Experiments, continued
(5) Compare the observed probability with the predetermine level of significance (alpha), which is usually p < .05
If the observed p value is greater than .05, do not reject the null hypothesis of no difference
conclude IV did not produce a reliable effect
If the observed p value is less than .05, reject the null hypothesis of no difference.
conclude IV did produce a reliable effect
version of picture books (Barbie, Emme, neutral) caused differences in young girls’ body dissatisfaction

49. Analysis of Experiments, continued
Confidence intervals
sample means estimate population means
Confidence intervals provide the range of values that contains the true population mean
with some probability, usually .95

50. Analysis of Experiments, continued
we typically want to conclude that performance in one experimental condition differs from performance in a second condition
compute the confidence interval around the sample mean in each condition
if the confidence intervals do not overlap, we gain confidence that the population means for the conditions are different
—that is, there is a difference among conditions

51. Analysis of Experiments, continued
Example of confidence intervals
suppose the confidence interval for mean body dissatisfaction in the Barbie condition is
– –.36
This interval contains the true population mean for body dissatisfaction following Barbie images (remember the sample mean is –.76).
suppose the confidence interval for mean body dissatisfaction in the neutral image condition is –
this interval contains the true population mean for body dissatisfaction following neutral images (the sample mean is 0.00)‏

52. Analysis of Experiments, continued
Barbie: – –.36 Neutral: –
because the confidence intervals do not overlap, we can be confidence that the population means for the two groups differ
viewing Barbie images, compared to neutral images, produces greater body dissatisfaction in the population of young girls

53. Analysis of Experiments, continued
suppose instead that the confidence intervals overlap:
Barbie Neutral
– –
even though the sample means differ (–.76 and 0.00), we cannot conclude that the population means differ because the confidence intervals overlap
the difference between the sample means could be attributed to chance

54. External Validity External validity
the extent to which findings from an experiment can be generalized to describe individuals, settings, and conditions beyond the scope of a specific experiment
any single experiment has limited external validity
external validity of findings increase when findings are replicated in a new experiment

55. External Validity, continued
Questions of external validity
would the same findings occur
in different settings?
in different conditions?
for different participants?
example:
research with college students is often criticized because of low external validity
sample often doesn’t matter when testing a theory
on what dimensions do college students differ?

56. External Validity, continued
Increasing external validity
include characteristics of situations, settings, and population to which researchers wish to generalize
partial replications
field experiments
conceptual replications

57. Additional Independent Groups Designs
Matched Groups Design
random assignment requires large samples to balance subject characteristics
sometimes only small samples are available
in matched groups design,
researchers select one or two individual differences variables for matching

58. Matched Groups Design Procedure select matching variable
individual differences variables are characteristics of people that differ, or vary
choose matching variable related to outcome or dependent variable
measure variable and order individuals’ scores
match pairs (or triples, quadruples, etc. depending on number of conditions) of identical or similar scores
randomly assign participants within each match to the different conditions

59. Matched Groups Design, continued
Important points about matching
participants are matched only on the matching variable
participants across conditions may differ on other important variables
these differences may be alternative explanations for study’s results (confounding)‏
the more characteristics a researcher tries to match, the harder if will be to match

60. Natural Groups Designs
psychologists’ questions often ask about how individuals differ, and how these individual differences are related to important outcomes.
examples:
Do men and women differ in what they seek in intimate relationships?
Are extraverted individuals, compared to introverted individuals, more likely to succeed in business?

61. Natural Groups Designs, continued
Individual differences (subject) variables
characteristics or traits that vary across individuals
physical characteristics
sex, race
social (demographic) characteristics
ethnicity, religious affiliation, marital status
personality characteristics
extraversion, emotional stability, intelligence
mental health characteristics
depression, anxiety, substance abuse

62. Natural Groups Designs, continued
Researchers can’t randomly assign participants to these groups
random assignment to male/female groups?
When a researcher investigates an independent variable in which the groups (conditions) are formed naturally, we say a “natural groups design” is used

63. Natural Groups Designs, continued
Example:
Suppose we want to compare occupational functioning of schizophrenics and normal (nonschizophrenic) controls?
Independent variable
natural groups variable: schizophrenic vs. normal
Dependent variable
measure of occupational functioning
Result
suppose schizophrenics have poorer occupational functioning than normal participants

64. Natural Groups Designs, continued
Causal inferences and natural groups design
Researchers can’t make a causal inference when a natural groups design is used
example: can we say that schizophrenia causes poorer occupational functioning?
No. The two groups likely differ in other ways that may cause poorer occupational functioning among schizophrenics (confoundings)‏
education level, drugs, nutritional status, tardive dyskinesia, etc.

65. Natural Groups Designs, continued
correlational research
allow researchers to describe and predict relationships among
individual differences variables and
outcomes
do not allow researchers to make causal inferences about individual differences variables