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129 Cards in this Set

  • Front
  • Back
Response bias:
demand characteristics
aspects of the study that people consider to determine the purpose
- behavior may be affected by knowledge whether accurate or not
- changes participant reactivity
Response bias:
social desirability bias
behave in ways they think is socially acceptable
Response bias:
leniency bias
unrealistically favorable rating to known person
(ex) professor/class ratings
Response bias:
central tendency bias
clusters responses around middle of scale
Response bias:
acquiescence response set
"yea sayers"-- tendency to agree
ceiling effect
when scale does not have enough higher ratings to capture variability
floor effect
when scale does not have enough lower ratings to capture variability
volunteerism bias
volunteers differ from non-volunteers:
-more highly educated, higher social class, higher intelligence, higher need for approval, more social, more likely to be females
ways to enhance response rate:
-include clear instructions about goal of project
- guarantee anonymity
- small gift/lottery
- follow up at 2-3 week intervals
- response rates > 50% are good
QXR reliability:
administer, let time pass, readminister to the same group of people
-used to test the consistency of measure
QXR reliability:
split-half reliability
split the survey in half and correlate
useful with different versions of questions assessing the same construct
-treats two halves of a measure as alternate forms
Increasing QXR reliability
-increase the number of items
-standardize admission process
-items that are clearly and accurately written
QXR validity:
content validity
do questions cover range of behaviors considered to be part of dimension that you're addressing (measuring what you want to measure)
- measures all facets of a given social construct (how adequately does instrument sample from important behaviors)
QXR validity:
construct validity
does it measure what it claims to measure
correlational research
tells us about the degree & direction of relationship
-no attempt to manipulate variables
-no causal relationships
experimental research
tells us if causal relationship exists between one variable and another
-manipulates IV to test influence of DV
-compares DV under control/experimental
-equates Ps in different conditions
-Controls unwanted effects of extraneous variables on DV
measure of association
-describing length of a linear relationship
-gives direction & magnitude of relationship
-DOESN'T tell: causation
direction of correlational relationships
positive: as x increase, y increases

negative: as x increases, y decreases

none: x and y are unrelated
magnitude of correlational relationships
strength of association - Pearson's r (-1 to +1)
- sign tells: direction
- number tells: magnitude
Rule: <.3 = weak, .3-.6 = moderate, .6 = strong
third-variable problem (Spurious correlations)
X and Y caused by some other variable
Directionality problem
does X cause Y or Y cause X
independent variable
manipulate independent variable to test influence on dependent variable
- you manipulate this!
dependent variable
value depends on the independent variable
- DV usually compared using experimental or control groups
random sampling
-each member has an equal chance of being chosen to be in study
random assignment
Internal validity
- equal chance of Ps to be assigned to control or experimental condition (or other conditions)
internal validity
when results can be confidently attributed to the effect of an independent variable
when IV varies with another variable
-extraneous variable that exerts differential effect
-threat to internal validity
1. age
2. cohort (generational effects)
3. time of assessment
external validity
-how applicable are results to other populations
selection bias (external validity)
error due to systematic differences in characteristics of those who take part in a study and those who don't
-results when non-comparable criteria are used to enroll participants in an investigation
remedies for volunteer bias
make interesting appeal, non-threatening, state importance of research, state why target population, offer small reward, avoid research that is stressful, use public or private commitment to volunteering, have someone known make appeal
ability to produce similar results on repeated administrations
-precision and error: true score and measurement error
interrater reliability
multiple observers: how closely to observers agree when coding same observations
-% agreement, or Cohen's kappa where Pr(a)= observed agreement & Pr(e) = probability of agreement based on chance (takes into account disagreement based on chance)
internal consistency
degree of relatedness of individual scale items
-split half reliability (correlation b/w two halves)
validity (general)
testing: extent to which scale measures what is intended
methodology: methodological soundness/appropriateness
external: generalizable results
face validity
how well it "appears to" measure something
criterion validity (concurrent and predictive)
relation to other tests measuring similar phenomena administered to same people
naturalistic observation
3rd party observations
participant observer research
observer becomes member of group to be studied
-useful for secretive or isolated groups
Problems:objectivity, reactivity, privacy & consent
observational research
-careful record keeping (distinguishes naturalistic observation from casual (non-systematic) observation)
*Coding scheme is important
- uses variety of measures, privacy of participants safe-guarded
observer bias
may know goals of study (use blind observer)
-interpretation vs. recording
Data recording methods (Observational Research)
*Field Notes
used for direct observation
-systematic, selective, recording devices
Data recording methods (Observational Research)
*Content analysis
for textual and photographic materials
Data recording methods (Observational Research)
*Narrative record
chronological description of all behaviors that occur in given setting- qualitative analysis
-Pros: more info, good starting pt
-Cons: time consuming/demanding, not economical if you have hypothesis
Data recording methods (Observational Research)
*Time Sampling (Interval recording)
observing target behavior during selected times:
- useful for frequent behaviors
- (ex) observe for 15 s, record for 15 s
Data recording methods (Observational Research)
*Event Sampling
target behavior serves as unit of analysis
- wait for behavior to begin recording, useful for infrequent behaviors
-duration record: length of time behavior occurs
- frequency-count record: number of times behavior occurs (short duration behaviors)
Archival research
using existing data/records
- records could be biased
- no possibility of reactivity
- good external validity
Cons: records might be hard to get, not generated for research (lack details, accuracy, reliability)
formalized set of concepts that organizes observations and inferences and predicts and explains phenomena
-unites seemingly disparate facts
- generative, lead to empirically testable data, (ex) evolution, operant condtioning
general APA principles
- fidelty/responsibility
- integrity
- justice
- respect for people's rights/dignity (confidentiality)
ways of acquiring knowledge
tenacity (persistence)
authority (consult expert sources)
intuition (gut feeling)
A system of knowledge and procedures for gaining knowledge through careful and unbiased observation and analysis
application of reasoning
general to specific:
deriving specific hypothesis from general idea
inductive reasoning
specific to general: specific facts to general conclusion
-data leads to theory
gaining knowledge by observation:
-causal vs. systematic
characteristics of Science
lawfulness of events: nature is predictable
asking empirical ?s: solvable
applying control: objectivity
self-correcting: replication, public verification, peer review
must be able to be proven wrong for it to be testable hypothesis
doctrine or belief system that pretends to be a science
-non falsifiable hypothesis, unwillingness to look closely at phenomenom, burden of proof lies with person making claim, failure to change/update theory
judgements made under uncertain conditions;
short cuts that lead to correct answer, but lead to biases in certain specifiable conditions
likelihood estimated on basis of how easily it can be brought to mind
category memberships based on similarity (Linda)
anchoring and adjustment
numerical estimates
Bar graphs
IV is categorical and DV is continuous
*use to make categorical comparisons
Line graph
IV continuous displayed on x axis, (DV on Y) conveys change from point to point
*use to convey trends
scatter plot
conveys overall impression of relationship between two continuous variables
-meaningful clusters of dots imply correlations
pie chart
conveys proportions or percentages
error bars
used on bar graphs
- standard error of measure (SEM); measures how far your sample mean is likely to be from the true population mean
SEM= standard deviation/square root(n)
frequency distributions
set of mutually exclusive categories in which actual values are classified and count of values in each category
- "histogram" in graph form (y axis is frequency count #, x axis is categories)
between-subjects design
different groups of subjects are randomly assigned to the levels of independent variable, data averaged for analysis
within-subjects design
single group of subjects is exposed to all levels of the independent variable, data are averaged for analysis
error variance
the variability among scores NOT caused by the independent variable
-common to all experimental designs, is handled differently in each design
sources of error variance
-individual differences among subjects
-environmental conditions aren't constant across levels of IV
-fluctuations in physical/mental state of an individual subject
handling error variance
-hold extraneous variables constant and treat Ps as similarly as possible
-match subjects
-increase effectiveness of IV (strong manipulation)
-randomize error variance across groups (random assignment)
-inferential statistics
between subject designs:
*Single-Factor Randomized Groups Design
1. the randomized two group design-- simplest to conduct, but amount of info yielded may be limited
2. the randomized multiple group design; (multiple control group design)-additional levels of IV can be added
3. parametric design; different levels of IV represent quantitative difference
4. nonparametric design: if diff levels of IV represent qualitative differeces
placebo effect
a measurable, observable, or felt improvement in health or behavior not attributable to a medication or treatment that has been administered
matched-group designs
used when you know that some variable correlates with your DV
-P's matched on that variable before being studied
matched-pairs design
the matched-groups equivalent to the randomized 2 group design to randomized two-group design
matched-multi group design
equivalent to the randomized multigroup design (with matching)-- difficult when more than 3-4 groups
parametric design
when you manipulate IV quantitatively
nonparametric design
when you manipulate IV qualitatively
factorial designs
adding a second independent variable to a single-factor design
-two components to assess: main effect of each IV & interaction b/w IVs
main effect
-manipulation one of the IV's produces a change in the DV
the separate effect of each IV
analogous to separate experiments involving those variables (factorial design)
-the effect of one IV on the DV is dependent on the other IV
when the effect of one IV changes over levels of the 2nd (factorial design)
factorial nomenclature
number of numbers- number of factors
value of number- number of levels
higher-order factorial design
more than two IVs; complexity increases as factors increase
-# of main effects and interactions increase
-# subjects req'd increases
-volume of materials and amt. time needed to complete experiment increases
mixed design
-includes between subjects & within-subjects factor in the same design
-allows you to evaluate effects of variables that can't be manipulated effectively w/in subjects
-complex mixed designs would include >2 factors, w/ any combo of between subjects & w/in subject factors
a correlational value (self esteem, GPA) in an experimental design
-subtract out the influence of the covariate to reduce error variance
*makes design more sensitive to effects of the IV
quasi-independent variable
correlational variable that looks like an experimental variable (gender);
Quasi-experimental design:
*time series design
make several observations of behavior before and after introducing your IV
O1 O2 O3 treatment O4 O5 O6
Quasi-experimental design
including a quasi-independent variable in an experimental design
-resulting design looks like a factorial experimental design
*must NOT be interpreted as causing changes in the DV because it is a pre-existing variable
Quasi-experimental design:
*Interrupted Time Series Design
make several observations before & after some naturally occurring event
(ex) car accidents before and after new 55 mph speed limit
Quasi-experimental design:
*Equivalent Time Samples Design
repeatedly introduce the treatment condition, alternated with periods of observation w/out the treatment
-treatment, O1, no treatment, O2, treatment, O3, no treatment, O4
Quasi-experimental design:
*Nonequivalent Control Group Design
uses existing groups, includes a time series component and a control group that isn't exposed to IV
-O1 O2 O3 O4
Pretest-Posttest design
pretest administered before exposure to experimental treatment, true experimental design
- used to assess impact of some change on performance
- pretest sensitization?
Developmental Designs:
*The cross-sectional design
Different cohorts assessed at the same time--thus they are of different age
Ps from different age groups are run through a study at the same time, create "cohort" groups based on age, allows collection of developmental data quickly
*tells about differences, not changes
Drawbacks to Cross-sectional designs
-Cohort effect
-may not be appropriate studies using widely ranging age groups
cohort effect
subjects of a given age are affected by factors unique to their generation
Pros and Cons of cross-sectional design
Pros: inexpensive
Cons: no direct measure of change; only age differences
- difficulty in establishing the equivalence of measures
- are results limited to the particular time of assessment?
- confounds age differences w/ cohort differences
Developmental Designs:
*The Longitudinal Design
a Single group of ps is measured several times over some period of time (months or years)
-avoids the generation effect that may hurt cross-sectional study
Pros and Cons of longitudinal design
Pros: provides a direct measure of age changes
Cons: costly, subject loss or attrition results in non-representative samples, and non-equivalent samples across time
-measures become obsolescent & questionably equivalent,
-repeated testing effects; and results may be limited to cohort assessed *cross-generational problem
*confounds age with time of testing
cross-generational (cohort) problem
results from a longitudinal study on one generation may not generalize to anoter
-subject mortality
-multiple observation effects (repeated IQ test)
-practical difficulties
Developmental Designs:
*Time lag designs
(ex) SAT
-same ages studied at different times
-cannot tell about age related changes, but can tell about cohort effects
Developmental Designs:
*The Cohort-Sequential Design
combines a cross-sectional and longitudinal component in the same design
*allows you to test for, but not eliminate, generation effects
Single subject design (why it is used)
to evaluate macro-level effects on neighborhoods, communities, and larger systems
-also small systems like families and individuals
-used more in applied vs. basic research
single subject designs
involve repeated, systematic measurement of a DV before, during, and after the manipulation of an IV
(usually DV is characteristic of human being and IV involves application of some intervention)
limitations of single subject design
-ethical: withholding an effective treatment
-practical issues
-averaging: obscuring results
-inter-subject variability
single-subject baseline design
1. repeated observation
2. consistent observational technique: same measurement and criteria used
3. distinction b/w intervention and non-intervention
4. decision rules: how do you determine if intervention was successful?
5. experimental control-use individuals as their own controls
Pros of single-subject approach
-rich set of data
-score not masked by group averages
-makes IDing and controlling sources of error variance easy
-focus on individual behavior may reveal subtle effects of an IV lost w/ group approach
-causal relationships can be established w/ few subjects
Cons of single-subject approach
-time consuming/tedious
-limited generalizability
-observer bias
Classes/Phases for single-subject research
1. no intervention (baseline, withdrawal)
2. intervention
3. reversal
baseline & withdrawal
baseline: if it occurs prior to any intervention
withdrawal: occurs after an intervention
systematic behaviors aimed at assisting a client in dealing with concerns
rarely used phase type; occurs when an intervention technique is applied to increase a behavior it had previously ben used to decrease
No intervention phases
intervention phases
"B" or "C" for additional factors
two questions in single subject evaluation
1. has there been a change in area of concern?
2. is there a functional relationship b/w intervention and observed change?
change in level
change between phases
change in slope
change between phases (either no accompanying change in level, or with one)
a delayed change; occurs later in the phase
scores fluctuate; some due to measurement, some to activity of extraneous variables
B (intervention only) design
type of single-subject design that allows for measurement of change over the course of an intervention
-no evidence of causation
-no baseline
AB (baseline and intervention) design
consists of no intervention baseline phase (A) and an intervention phase (B)
ABA (Basic withdrawal) Design
allows for more reliable establishment of a relationship b/w intervention and outcome than in the AB design
multiple baseline designs
allows for evaluation across clients, situations, or problems (client systems)
issues to consider w/ single-subject design
choosing a stability criterion
dealing with uncontrolled variability
determining generality of findings (inter-subject replication)
generality requires replication- subjects & settings
drifting baselines
baseline that doesn't stabilize but continues to show systematic variations (drift)
-may drift up or downwards
-can be taken into account and effects of variables can be determined
unrecoverable baselines
behavior cannot be returned to original baseline after at treatment (carryover effects) or partially recovered
unequal baselines between subjects
baselines for different subjects level off at different values
inappropriate baseline levels
baseline levels that are too high or low may mask effects of a treatment
average set of 2 or more observations (when data is unstable)
detecting change (single-subject)
-change in mean level
-immediate change in level
-change in trend
-latency of change