research methods in psychology independent groups designs
TRANSCRIPT
Research Methods in Psychology
Independent Groups Designs
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
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”
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
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)
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)
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.
Experimental Control and Internal Validity
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
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?
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
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)?
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
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
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
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
Control Techniques
Two control techniques to eliminate alternative explanations• holding conditions constant• balancing
With proper use of control techniques, an experiment has internal validity
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
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
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?
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
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
Independent Groups Designs
Independent groups design• 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
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
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)
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
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
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
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
Threats to Internal Validity, continued
• ExampleSuppose 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.
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
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
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
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
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
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
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
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
Analysis and Interpretationof 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
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
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
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
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
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
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
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
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)?
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
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
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
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
Analysis of Experiments, continued
• Example of confidence intervals suppose the confidence interval for mean body
dissatisfaction in the Barbie condition is–1.16 -- –.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
–.25 -- +.25• this interval contains the true population mean for body
dissatisfaction following neutral images (the sample mean is 0.00)
Analysis of Experiments, continued
Barbie: –1.16 -- –.36 Neutral: –.25 -- +.25
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
Analysis of Experiments, continued
• suppose instead that the confidence intervals overlap:
Barbie Neutral –1.56 -- +.04 –.82 -- +.82
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
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
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?
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
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
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
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
Natural Groups Designs
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?
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
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
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
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.
Natural Groups Designs, continued
Natural groups designs• 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