
matched pairs design
 a betweensubjects design
 subjects randomly assigned as pairs after being matched on potentially confounding variables (the two groups have same set of characteristics)
 to control for subject variability

advantages/disadvantages of matched pairs
 advantages  no practice/fatigue effects across conditions  no carryover effects
 disadvantages  larger sample needed, need to know what variables need to be matched

within subjects design
 ultimate matchedpair design (each subject matched with themselves)
 Also called repeated measures design
 controls for subjectrelated variability
 use paired ttests
 BUT carryover effects are possible

advantages/disadvantages of withinsubjects
 advantages  controls for subject variability, more power, fewer subjects required (in principle)
 disadvantages  practice/fatigue effects, awareness of changes across conditions, order effects (can be subject to task demands  know what is being manipulated)

how to deal with order effects
 vary sequences of conditions
 randomization
 counterbalancing
 to vary the order systematically so that the order effects was out

counterbalancing
 create sequences so that
 1. every conditions appears in every possible position in the order the same # of times
 2. every condition precedes every other condition as many times as it follows that condition
 then randomly assign participants to sequences

advantages/disadvantages of counterbalancing
 advantages  balance order, any differences due to fatigue/practice or subject variability should was out
 disadvantages  requires more subjects, analysis is more difficult

types of counterbalancing
 1  each subject gets all possible orders (reverse counterbalancing, block randomization)
 2  each subject gets only one possible order, but across subjects all orders accounted for (complete counterbalancing)

reverse counterbalancing
 AB, BA
 particular individual does each order
 present different orders  one is just reverse order

block randomization
 order randomized within blocks  subjects receive all blocks
 ABC, BAC, CBA, ACB
 would be a really long experiment!

complete counterbalancing
 = N! orders
 each subject gets one order  but across all subjects all orders are accounted for

Simple Latin Square
 partial counterbalancing
 each condition appears in each position in the sequence
 DOES NOT fulfill "every condition precedes every other condition as many times as it follows that condition"

balanced latin square
 full counterbalancing
 only works for even # of conditions (2x2, 4x2)
 fulfills both counterbalancing conditions

mixed designs
 at least one between subject IV and one withinsubject IV
 Still do ANOVA  same general test for factorial!

Descriptive Statistics
 summarizing a set of empirical data
 central tendency (mean, median, mode)
 variability (the spread, SD)

inferential statistics
 drawing conclusions about the broader population
  infer what your sample means about the larger population
  if you aren't interested in the broader population  inferetial not necessary!

variability and 3 sources
 represents background "noise"through which we have to detect the experimental "signal"
 NEED to reduce extraneous variability
 sources: within groups (different individuals contribute to the data), between groups (because of IV's), between groups (because of extraneous variables  DO NOT WANT)
do we have enough confidence that between groups variability is larger than variability within groups

statistical inference
how likely is it that the IVs had an effect over and above the variability due to random sampling?
 based on probabilities!
 "inferences under uncertainty"  we make statements about how probable it is that the population means are different

hypothesis testing
 start with a hypotehsis that is opposite of what we are looking for (null) and see if data is improbable given that hypothesis
 null vs. alternative hypothesis  any differences due to treatments

sampling distribution
 any given sample is just one of many
 b/c of the many different samples that could be made with the population  their actual distributions will be different because of noise variability
 if you average all possible samples  end up on average with distribution that looks like population

alpha = .05
 the probability that teh difference you are observing in your data is from a different population
 reasonable doubt = 5%!

Type I error
 null is true, but we reject null and assume that there is an effect
 nothing there but we think there is!!
 equal to alpha

Type II error
 we failed to reject the null  but there truly is an effect!
 something there but we don't think there is!
 equal to Beta

Power
 1Beta (1probability of type II error)
 ability of independent manipulation to detect an effect!  to reject null when should reject it!

causal inference
how your independent variable caused some change in the dependent variable

ANOVA
 analysis of variance
 when there are more than two groups!

Betweensubjects design
rely on random assignment/random sampling to make sure the groups don't differ in ability

external validity
 can we generalize from results
 results then must be internally valid and replicable!  a

generalizations
 1. to different samples  are your conclusions not limited to your specific sample?
 2. to different procedures/operationalizations?  how does your research mesh with others?
 3. to the world at large

how would you increase external validity?
 aggregations  multiple subjects, trials, vary the stimuli, measure it in different ways
 multivariate measurement (multiple dependent variables and see hwo they pattern together)
 nonreactive studies  nonexperimental
 naturalistic and field studies  nonexperimental  people more likely to act normally
more you increase external though, you may decrease internal!

Power  depends on...?
 N (sample size)
 small N = higher probability of type 2 error
 which would be a higher power!??

Qualitative vs. Quantitative Research
 Both involve observations of behavior!
 Quantitative  more control, less openended, reactivity, manipulation, inferential, numbers
 Qualitative  less control, more openended, naturalistic, spontaneous, descriptive, categories

Why do survey research?
 to assess how people feel abotu an issue
 to examine relationships across responses
 to dispel myths
 to gather scientific information

what kinds of problems do you want to avoid in survey research?
 want one issue per item
 want to avoid bias/leading questions

how to construct surveys
 make alternatives clear (mutually exclusive and exhaustive)
 beware of social desirability  use neutral statements!
 beware of acquiescence/response bias  when respondents tend to agree to any statement
 think about item format/sequencing (but beware of context effects!)

sampling bias
 representativeness  does sample exhibit same distribution of characteristics as the intended population to be studied?
 randomness  does each individual in population have equal likelihood to survey

Different kinds of sampling
 probability sampling (simple random, stratified, cluster)
 nonprobability sampling (non equal probability)  convenience/haphazard, quota sampling (consider population characteristics)

survey administration
 mail surveys
 oneonone interviews
 telephone interviews
 internet surveys

correlational research
 examines degree of relationship between 2 behaviors/events
 changes in one are associated with changes in other (multivariate)
 measures the extent of covariation between two (or more) dependent variables
 typically reported in terms of "r"

types of coreelational studies
 observational research
 survey research
 archival research

correlation can be misleading
 Restrictive range (by restrict range of the data = different interpretation)
 outliers (can make it look stronger or weaker)
 nonlinear  a Ushaped relation or a hump (correlations assume simple lines!)

correlation vs causation
 correlation is necessary for determining causation but is not sufficient
 X > Y
 Y > X
 or a third variable problem (Z could cause X and Y)

