Research Methods For Psych

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Response bias:
demand characteristics

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aspects of the study that people consider to determine the purpose
- behavior may be affected by knowledge whether accurate or not
- changes participant reactivity

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Response bias:
social desirability bias

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behave in ways they think is socially acceptable

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Response bias:
leniency bias

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unrealistically favorable rating to known person
(ex) professor/class ratings

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Response bias:
central tendency bias

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clusters responses around middle of scale

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Response bias:
acquiescence response set

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"yea sayers"-- tendency to agree

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ceiling effect

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when scale does not have enough higher ratings to capture variability

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floor effect

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when scale does not have enough lower ratings to capture variability

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volunteerism bias

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volunteers differ from non-volunteers:
-more highly educated, higher social class, higher intelligence, higher need for approval, more social, more likely to be females

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ways to enhance response rate:

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-include clear instructions about goal of project
- guarantee anonymity
- small gift/lottery
- follow up at 2-3 week intervals
- response rates > 50% are good

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QXR reliability:
Test-retest

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administer, let time pass, readminister to the same group of people
-used to test the consistency of measure

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QXR reliability:
split-half reliability

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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

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Increasing QXR reliability

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-increase the number of items
-standardize admission process
-items that are clearly and accurately written

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QXR validity:
content validity

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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)

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QXR validity:
construct validity

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does it measure what it claims to measure

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correlational research

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tells us about the degree & direction of relationship
-no attempt to manipulate variables
-no causal relationships

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experimental research

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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

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correlation

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measure of association
-describing length of a linear relationship
-gives direction & magnitude of relationship
-DOESN'T tell: causation

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direction of correlational relationships

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positive: as x increase, y increases

negative: as x increases, y decreases

none: x and y are unrelated

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magnitude of correlational relationships

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strength of association - Pearson's r (-1 to +1)
- sign tells: direction
- number tells: magnitude
Rule: <.3 = weak, .3-.6 = moderate, .6 = strong

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third-variable problem (Spurious correlations)

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X and Y caused by some other variable

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Directionality problem

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does X cause Y or Y cause X

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independent variable

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manipulate independent variable to test influence on dependent variable
- you manipulate this!

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dependent variable

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value depends on the independent variable
- DV usually compared using experimental or control groups

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random sampling

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Generalizability
-each member has an equal chance of being chosen to be in study

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random assignment

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Internal validity
- equal chance of Ps to be assigned to control or experimental condition (or other conditions)

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internal validity

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when results can be confidently attributed to the effect of an independent variable

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confound

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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

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external validity

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generalizability
-how applicable are results to other populations

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selection bias (external validity)

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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

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remedies for volunteer bias

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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

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reliability

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ability to produce similar results on repeated administrations
-precision and error: true score and measurement error

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interrater reliability

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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)

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internal consistency

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degree of relatedness of individual scale items
-split half reliability (correlation b/w two halves)

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validity (general)

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testing: extent to which scale measures what is intended
methodology: methodological soundness/appropriateness
external: generalizable results

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face validity

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how well it "appears to" measure something

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criterion validity (concurrent and predictive)

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relation to other tests measuring similar phenomena administered to same people

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naturalistic observation

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3rd party observations

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participant observer research

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observer becomes member of group to be studied
-useful for secretive or isolated groups
Problems:objectivity, reactivity, privacy & consent

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observational research

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-careful record keeping (distinguishes naturalistic observation from casual (non-systematic) observation)
*Coding scheme is important
- uses variety of measures, privacy of participants safe-guarded

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observer bias

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may know goals of study (use blind observer)
-interpretation vs. recording

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Data recording methods (Observational Research)
*Field Notes

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used for direct observation
-systematic, selective, recording devices

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Data recording methods (Observational Research)
*Content analysis

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for textual and photographic materials

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Data recording methods (Observational Research)
*Narrative record

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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

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Data recording methods (Observational Research)
*Time Sampling (Interval recording)

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observing target behavior during selected times:
- useful for frequent behaviors
- (ex) observe for 15 s, record for 15 s

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Data recording methods (Observational Research)
*Event Sampling

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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)

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Archival research

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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)

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theory

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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

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general APA principles

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-beneficence/nonmalficence
- fidelty/responsibility
- integrity
- justice
- respect for people's rights/dignity (confidentiality)

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ways of acquiring knowledge

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tenacity (persistence)
authority (consult expert sources)
intuition (gut feeling)

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science

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A system of knowledge and procedures for gaining knowledge through careful and unbiased observation and analysis

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rationalism

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application of reasoning

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deductive

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general to specific:
deriving specific hypothesis from general idea

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inductive reasoning

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specific to general: specific facts to general conclusion
-data leads to theory

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empiricism

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gaining knowledge by observation:
-causal vs. systematic

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characteristics of Science

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lawfulness of events: nature is predictable
asking empirical ?s: solvable
applying control: objectivity
self-correcting: replication, public verification, peer review

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falsifiability

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must be able to be proven wrong for it to be testable hypothesis

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pseudoscience

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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

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heuristics

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judgements made under uncertain conditions;
short cuts that lead to correct answer, but lead to biases in certain specifiable conditions

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heuristics:
availability

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likelihood estimated on basis of how easily it can be brought to mind

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heuristics:
representativeness

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category memberships based on similarity (Linda)

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heuristics:
anchoring and adjustment

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numerical estimates

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Bar graphs

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IV is categorical and DV is continuous
*use to make categorical comparisons

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Line graph

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IV continuous displayed on x axis, (DV on Y) conveys change from point to point
*use to convey trends

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scatter plot

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conveys overall impression of relationship between two continuous variables
-meaningful clusters of dots imply correlations

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pie chart

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conveys proportions or percentages

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error bars

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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)

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frequency distributions

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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)

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between-subjects design

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different groups of subjects are randomly assigned to the levels of independent variable, data averaged for analysis

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within-subjects design

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single group of subjects is exposed to all levels of the independent variable, data are averaged for analysis

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error variance

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the variability among scores NOT caused by the independent variable
-common to all experimental designs, is handled differently in each design

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sources of error variance

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-individual differences among subjects
-environmental conditions aren't constant across levels of IV
-fluctuations in physical/mental state of an individual subject

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handling error variance

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-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

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between subject designs:
*Single-Factor Randomized Groups Design

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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

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placebo effect

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a measurable, observable, or felt improvement in health or behavior not attributable to a medication or treatment that has been administered

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matched-group designs

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used when you know that some variable correlates with your DV
-P's matched on that variable before being studied

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matched-pairs design

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the matched-groups equivalent to the randomized 2 group design to randomized two-group design

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matched-multi group design

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equivalent to the randomized multigroup design (with matching)-- difficult when more than 3-4 groups

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parametric design

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when you manipulate IV quantitatively

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nonparametric design

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when you manipulate IV qualitatively

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factorial designs

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adding a second independent variable to a single-factor design
-two components to assess: main effect of each IV & interaction b/w IVs

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main effect

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-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)

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interaction

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-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)

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factorial nomenclature

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number of numbers- number of factors
value of number- number of levels

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higher-order factorial design

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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

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mixed design

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-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

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covariate

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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

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quasi-independent variable

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correlational variable that looks like an experimental variable (gender);

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Quasi-experimental design:
*time series design

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make several observations of behavior before and after introducing your IV
O1 O2 O3 treatment O4 O5 O6

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Quasi-experimental design

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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

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Quasi-experimental design:
*Interrupted Time Series Design

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make several observations before & after some naturally occurring event
(ex) car accidents before and after new 55 mph speed limit

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Quasi-experimental design:
*Equivalent Time Samples Design

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repeatedly introduce the treatment condition, alternated with periods of observation w/out the treatment
-treatment, O1, no treatment, O2, treatment, O3, no treatment, O4

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Quasi-experimental design:
*Nonequivalent Control Group Design

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uses existing groups, includes a time series component and a control group that isn't exposed to IV
-O1O2treatmentO3O4
-O1 O2 O3 O4

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Pretest-Posttest design

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pretest administered before exposure to experimental treatment, true experimental design
- used to assess impact of some change on performance
- pretest sensitization?

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Developmental Designs:
*The cross-sectional design

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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

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Drawbacks to Cross-sectional designs

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-Cohort effect
-may not be appropriate studies using widely ranging age groups

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cohort effect

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subjects of a given age are affected by factors unique to their generation

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Pros and Cons of cross-sectional design

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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

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Developmental Designs:
*The Longitudinal Design

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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

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Pros and Cons of longitudinal design

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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

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cross-generational (cohort) problem

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results from a longitudinal study on one generation may not generalize to anoter
-subject mortality
-multiple observation effects (repeated IQ test)
-practical difficulties

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Developmental Designs:
*Time lag designs

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(ex) SAT
-same ages studied at different times
-cannot tell about age related changes, but can tell about cohort effects

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Developmental Designs:
*The Cohort-Sequential Design

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combines a cross-sectional and longitudinal component in the same design
*allows you to test for, but not eliminate, generation effects

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Single subject design (why it is used)

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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

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single subject designs

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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)

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limitations of single subject design

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-ethical: withholding an effective treatment
-practical issues
-averaging: obscuring results
-generalizability
-inter-subject variability

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single-subject baseline design

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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

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Pros of single-subject approach

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-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

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Cons of single-subject approach

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-time consuming/tedious
-limited generalizability
-observer bias

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Classes/Phases for single-subject research

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1. no intervention (baseline, withdrawal)
2. intervention
3. reversal

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baseline & withdrawal

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baseline: if it occurs prior to any intervention
withdrawal: occurs after an intervention

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intervention

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systematic behaviors aimed at assisting a client in dealing with concerns

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reversal

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rarely used phase type; occurs when an intervention technique is applied to increase a behavior it had previously ben used to decrease

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No intervention phases
intervention phases

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"A"
"B" or "C" for additional factors

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two questions in single subject evaluation

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1. has there been a change in area of concern?
2. is there a functional relationship b/w intervention and observed change?

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change in level

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change between phases

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change in slope

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change between phases (either no accompanying change in level, or with one)

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carryover

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a delayed change; occurs later in the phase

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overlap

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scores fluctuate; some due to measurement, some to activity of extraneous variables

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B (intervention only) design

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type of single-subject design that allows for measurement of change over the course of an intervention
-no evidence of causation
-no baseline
-DV

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AB (baseline and intervention) design

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consists of no intervention baseline phase (A) and an intervention phase (B)

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ABA (Basic withdrawal) Design

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allows for more reliable establishment of a relationship b/w intervention and outcome than in the AB design

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multiple baseline designs

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allows for evaluation across clients, situations, or problems (client systems)

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issues to consider w/ single-subject design

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choosing a stability criterion
dealing with uncontrolled variability
determining generality of findings (inter-subject replication)
generality requires replication- subjects & settings

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drifting baselines

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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

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unrecoverable baselines

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behavior cannot be returned to original baseline after at treatment (carryover effects) or partially recovered

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unequal baselines between subjects

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baselines for different subjects level off at different values

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inappropriate baseline levels

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baseline levels that are too high or low may mask effects of a treatment

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smoothing

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average set of 2 or more observations (when data is unstable)

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detecting change (single-subject)

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-change in mean level
-immediate change in level
-change in trend
-latency of change