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Definition
 A way of knowing characterized by the attempt to apply objective, empirical methods when
 searching for causes of natural events.

Probabilistic
Statistical determinism
 Using probability to
 determine if the events whether causal, predictive or simple relational are
 greater than chance.

Objectivity
 without bias
 from experimenter and participants

Datadriven
 conclusions are
 based on the data objective information.

Empirical
 All information
 is based on observation

Objectivity
 Observations
 must be free from bias.

Systematic
 Observations
 made in stepbystep fashion.

Controlled
 Potentially
 confusing factors are eliminated

ØLawful:
Every event can beunderstood as a sequence of natural causes and effects.

ØDetermined:
The event or behavior issolely influence by natural causes and does not depend of choice or "freewill." Organisms behave in apredictable, lawful way.

ØUnderstandable:
The behavior can beunderstood and part of the explanation of an event or behavior cannot byaccepted as a mystery or an unresolvable contradiction.

ØDeduction:
reasoning from a set ofgeneral statements towards the prediction of some specific event. Based on a theory, one can deductbehavior given particular conditions.

ØHypothesis:
the prediction aboutspecific events that is derived from the theory.

ØInduction:
The logical process ofreasoning from specific events to the theory

ØProductivity:
generating a lot ofresearch studies.

ØFalsification:
They have to be able to beproven wrong.

ØParsimony:
Include the minimum # ofconstructs (ideas or concepts) and assumptions that are necessary to explainthe phenomenon adequately and precisely. lthe simplest theory is preferred.

GENERAL PRINCIPLES
Principle A: Beneficence and Nonmaleficence
 Principle B: Fidelity and
 Responsibility
Principle C: Integrity
Principle D: Justice
 Principle E: Respect for
 People's Rights and Dignity

look at the chart on nominal ordinal etc

Construct:
hypothetical factor that can’t be observeddirectly; it's existence is inferred from certain behaviors

OperationalDefinition:
A way to attach asystem of measurement in a way that can be replicated and which is a faithfulPROXY of the construct.

ØReliability:
reproducibility of ameasurement; the extent to which measures of the same phenomenon are consistentand repeatable; measures that are high in reliability will contain a minimummeasurement error.lExample: Reaction time to answer with a yes or no buttonif the word on a screen is an animal or not.

ØValidity:
The extent to which ameasure of a construct truly measures that construct and not something else.Example: Does this reaction time truly measure speed ofprocessing in semantic memory?

Classical Reliability Theory assumptions? < X (Observed
score) = True score + Error> 4
1. True score is constant.
2. Error is random.
 3. The correlation between
 the true scores and errors is 0.
 4. The correlation between
 errors of different measurement occasions also equal 0.

Classical Test orReliability Theory ?
 ØClassical Test or
 Reliability Theory is a special case of Generalizability Theory (GT) in which error is made up of only one source.
 ØIn GT, error is separated
 into pieces, each of which can be estimated (if we collect the data right).

FOUR COMPONENTS OF VALIDITY
1. Face
2. Content
 3. Criterion (predictive and
 conncurrent)
4. Construct Validity

Construct Validity Definition
 ØDefinition: When the measure being used
 accurately assesses some hypothetical construct; refers to when the construct
 itself is valid; refers to whether the operational definitions used for
 independent and dependent variables are valid.
 ØMost rigorous type of
 validity
 ØCan be used as part of the hypotheticodeductive scientific method where the data we collect
 is dictated by the conceptual definition of the construct and the data then
 helps refine the nature of the construct.

MULTITRAITMULTIMETHOD
MATRIX
 MTMM is an approach to assessing the construct validity of a set of
 measures in a study.

Two subcategories of construct validity discussed.
ØConvergent validity: the degree to whichconcepts that should be related theoretically are interrelated in reality. ØDiscriminant validity: the degree to which concepts that should not be related theoreticallyare not interrelated in reality.

You can assess
both convergent and discriminant validity using the MTMM
LOOK at slide on the MTMM

External Validity:
 ØDegree to which researchfindings can be generalized beyond specific context of the experiment
 ØIt addresses the issue ofbeing able to generalize the results of your study to other times, places, andpersons.
 lE.g., If you conduct a study looking at heart disease inmen, can these results be generalized to women?

Internal
Validity
 ØThe degree to which the experiment is
 methodologically sound and free of confounding variables.
 ØOnce it has been determined that the two
 variables (A & B) are related, need to determined causality.
ØDoes A cause B?
 ØIf a study lacks internal validity, one can
 not make cause and effect statements based on the research

common threats to internal validity? <6>
1. History Threat
2. Maturation Threat
3. Testing Threat
4. Instrumentation Threat
5. Mortality or Attrition Threat
6. Statistical Regression to the Mean Threat

advantages and
disadvantage (1) of doing a Between subjects (2) design?
Advantages:
 ØEach subject in a condition
 is “fresh” and not “contaminated” by a previous treatment condition
Disadvantages:
 ØTakes a lot more subjects
 than a within subject design.
 ØDifferences btwn groups may be due to
 individual differences btwn groups and NOT because of
 the IV.

4 Ways to have equivalent groups:
1. Random assignment
 2. Block randomization a
 method of randomization in which the numbers of subjects are balanced over all
 conditions and the end of each subject assignment block rather than at the end
 of the testing schedule; each block contain each treatment once.
 3. Matching (1 homogenous or
 block matching)
4. Withinsubject design

advantages of a
within subject design?
4
 1.One reason for doing a
 within subject design is that it requires less subjects to obtain a good level
 of power. You may be interested in
 studying a population that is scarce in subjects: e.g., Psychotic Major Depression
 Pts, PTSD pts.
 2.“Perfect matching”
 occurs.
 3.Error variance is
 reduced.
 4.The reduction in error
 variance produces an increase in power.

Two types of Sequence or order effect: effect due to the order of treatment conditions are given to each subject. Aka practice effects.
 ØProgressive effects: Performance changes
 steadily from treatment condition to the next treatment condition.
 ØCarry over effects: the effects of a
 previously administered condition on a subject’s performance on a subsequent
 condition in a withinsubject design.

Controlling for these sequence effects
 ØCounterbalancing: systematic arrangement of
 treatment conditions designed to neutralize sequence effects.
 lComplete counterbalancing: all
 possible sequence combinations of treatment conditions are included.
 Latin square: a form of
 incomplete counterbalancing

Null
Hypothesis
 Statistical
 hypothesis describing the population parameters that the sample data represent
 if the predicted relationship does NOT
 exist.

Alternative
Hypothesis
 Statistical hypothesis
 describing the population parameters that the sample data represent if the
 predicted relationship does exist.

Type I error
 ØStatistical decisionmaking
 error in which large amount of sampling error caused rejection of H0 when
 H0 is true.
 ØThere is relationship btwn IV & DV when it doesn’t
 exist.

Type
II error:
 ØStatistical decisionmaking
 error in which large amount of sampling error caused acceptance of H0 when
 H0 is false.
 ØNo relationship btwn IV & DV when in fact it
 does exist.

Factors Affecting Power <6>
 1.The alpha level. Reducing the alpha level will reduce
 the power of a statistical test.
 1.Eg., moving alpha from .05 to
 .01.
 2.Onetailed versus 2tailed
 alpha test.
 3.As sample size increases, so
 does power.
 4.Reduce error variance will
 increase power.
 5.Greater subject variability
 will decrease power.
 6.Using match, versus
 withingroup, versus between subject, versus mixed design

Ttest and ANOVA definitions?
 ttest: A hypothesistesting
 procedure used to determine if mean difference exist for only 2 treatment
 conditions or populations. Good
 for only 1 IV.
 Analysis of Variance (ANOVA): A hypothesis –testing procedure used to determine if mean
 differences exist for 2 or more treatments or populations.

Unlike the ttest, the ANOVA can: <4>
ØCompare more than 2 groups(multilevel design).ØFind nonlinear effects ofthe IV. ØEvaluate the effects of morethan one IV.ØEvaluate interactionseffects between 2 or more IVs.

So we found that
our results are significant. How much of an effect does IV have on
DV?
 Effect size: To obtain the ratio,
 estimate the population variances from the data of an experiment.
s2A = the estimated population treatment effects
 s2S/A =
 the estimated population error variance

What if we want to
test 2 IVs at the same time?
We do a Factorial Experiment.

The Advantages of Factorial Designs (3)
ØJoint manipulation of IVs
ØInteraction
 Saves time, money and effort
 by doing one bigger experiment (2 or more Ivs)
 than several smaller experiments (only 1 IV).

Main effects (ME):
the separate effect ofeach independent variable AVERAGED over the levels of the other independentvariable. (AVERAGED EFFECTS)

Simple effects (SE):
The variability among thetreatment means associated with one independent variable at a particular levelof the other independent variable. AKA simple main effects.

INTERACTION:
When the effect of one of the IVs on the DV is not the same atall levels of the second IV.When an interaction ispresent, the ME is NOT representative of thecorresponding SEs.


simple interaction AND AXBXC interaction (3way interaction)
 ØA simple
 interaction is the interaction of 2 of
 the independent variables with the third variable held constant.
 Ø An AXBXC
 interaction (3way interaction) is present when the simple
 interactions btwn 2 of IVs are NOT the same
 at all levels of the third IV.

Advantages of a
Mixed Factorial Design
Minimizing Carryover Effects.
 2. Still need less subjects that complete
 between.
Disadvantages: Carryover effects, more subjects than within

TRUE” EXPERIMENT <4 factors>
 ØRandom
 sampling
 a sampling
 method in which each member of a set has independent chances to be selected.
 ØRandom
 assignment
 randomly assign
 subjects into the control group and the treatment group.
 lThe strength of randomization is that it creates two or
 more groups that are equivalent in the very beginning on the average on all characteristics.
lTrue for long term and reasonable size samples.
 ØExperimenter
 manipulation
 directly
 manipulate variables to test causeandeffect relationships
 ØExperimenter
 control
 control of all
 other extraneous variables that might impact the dependent variables.

Common
characteristics of quasiexperiments: <2>
 ØMatching
 instead of randomization is
 used.
 lE.g., studying effects of a new police strategy in one
 town and finding a similar town.
 •That other town
 would have citizen demographics that are very similar to the experimental town.
 It is a comparison group, and this matching strategy is sometimes called nonequivalent group design.
 ØGroups
 through selfselection.
lIndividuals could enter treatment levels through selfselection, because they are in a particular category
lProblem: selfselection or regression to the mean are alternative explanations for results

Time series
 ØSeveral observations over
 time. While you may have some type of experimental intervention, often
 "nature" does the experimenting for you.
 ØMost common type of
 longitudinal (over time) research found in criminal justice.
ØCan be interrupted or noninterrupted.
 lBoth examine changes in the dependent variable over time,
 with only an interrupted time series involving before and after measurement.
lE.g., Crime rates as a new law is taking effect.

Interrupted
time series design, noninturrupted, and switching
 Group 1: O1, O2, O3, O4, T, O5, O6, O7, O8ØE.g., Number of minoritiesaccepted into a university before and after a new change on admission policiesNoninterrupted timeseries design:there is NOprogram or treatment but performance is measures several times.Group 1: O1, O2, O3, O4, O5, O6, O7, O8ØE.g., Number of minoritiesaccepted into a university over a course of 8 years with no change orintervention
 Interruptedtime series design with switching replications: the program or treatment isreplicated at a different location and at a different time.Group 1: O1, O2, T,O3, O4, O5, O6, O7, O8
 Group 2: O1, O2, O3, O4, O5, T,O6, O7, O8
 ØE.g., 2 similar colleges,examining # of minorities accepted before and after change of admissionpolicies. The change occurs atdifferent time points.

Correlation and what can we study with correlation?
 Ø: a statistical procedure that is used to
 measure and describe a relationship between two CONTINOUS
 variables.
 ØUsually the two variables are naturally
 occurring events in the environment.
 They are usually not manipulated or controlled by the experimenter.
 > 1. Prediction.
2. Validity.
3. Reliability.
4. Theory Verification

What are some of
the issues about r? <5>
 1. The value of a
 correlation can be affected greatly by range of scores in the data.
 ØCeiling effect: when everyone scores very high on a measure
 and there is little variability
 ØFloor effect: when everyone scores very low on a measure
 and there is little variability
ØRestriction of range: little variability
2. Measurement error. The more error, the smaller the r
 3. One or two extreme data
 points (outliers) can greatly effect the value of a correlation
 4. If I square r, I get r2 aka coefficient of
 determination: the
 proportion of variability in one variable that can be determined by the other
 variable
 5. Linear transformations: It does not matter if scores of either variables are added
 or multiplied by a constant, the r btwn the 2 variables will remain
 the same.

Equation for a Linear
Relationship
Y = bX + c
 ØY is the dependent variable.
 criterion
 variable.
Øb is the slope.
 lIt determines how much the Y variable will change when X
 increases
lIt describes the direction of the relationship. (+ or ).
 ØX is the independent
 variable. predictor
 variable.
Øc is the Yintercept.

ØRegression & Regression line:
 the statistical techniquesfor finding the bestfitting straight line.
 the straight line thatbest describes the linear relationship between two variables.

Naturalistic Observation:
Participant Observation:
 Studying the behaviors ofpeople as they act in their everyday environments.
 Researcher will join a groupbeing observed, as one of the participants

Sampling
Behavior ØGeneralization of
observations (external validity) depends on how behavior is sampled.
 1. Time Sampling: choosing time intervals for
 making observations either systematically or randomly.
lUsed when the events of interest happen infrequently
 2. Situation or Event Sampling: studying behavior in
 different locations and under different circumstances.
lEnhances the external validity of findings.
 lWithin situations, subject sampling may be used to observe
 some people in the setting.

What are the risks
of Observational Research? <3>
1. Influence of the Observer
 ØIf they know they are being
 observed, the behavior may not represent normal behavior.
 ØTo control reactivity:
 unobtrusive measurement, adaptation, and indirect observations of behavior.
 ØMust consider ethical issues
 when controlling reactivity.
2. Observer Bias
 ØOccurs when observers’
 biases determine which behaviors they choose to observe and when these
 expectations lead to systematic errors in identifying behavior.
 ØExpectancy when they are
 aware of study’s hypotheses.
 ØMust recognize that it may
 be present.
 ØReduced by keeping observers
 unaware of study’ goals.
3. Absence of Control
 ØMore difficult to make
 conclusions about the observations.

EXTENT OF RESEARCHER CONTROL IN
DIFFERENT METHODS OF OBSERVATION WITH INTERVENTION
 Passive Observation: Researcher does
 not attempt to control or change situation when making observations
 Structured observations: Researcher sets
 up a specific situation making observation
 Field expeeriement: person controls by minipulating the IV

What if I only
want to study one person with very rare and unique characteristics? Instead of
doing an experiment, I will do a case study.
 ØThe case study is an
 intensive description and analysis of a single individual.
 ØCapable of uncovering causal
 paths and mechanisms, and through richness of detail, identifying causal
 influences and interaction effects which might not be treated as operationalized variables in a statistical
 study.
 ØRecommended as part of a multimethod approach
 ("triangulation") in which the same dependent variable is
 investigated using multiple additional procedures.

What if I only want to study one
person with very rare and unique characteristics?
 The Case Study Method: ØCase study: an intensive
 description and analysis of a single person
 ØCapable of uncovering causal
 paths & mechanisms, & through richness of detail, identifying causal
 influences & interaction effects which might not be treated as operationalized variables in a study.
 Singlecase (N
 = 1) Experimental Designs
 ØBehavior following treatment
 is compared to the baseline behavior to determine if the treatment is
 effective.
 ØSuppose a treatment was
 designed to decrease the frequency of a behavior
 ØAlthough the data suggests
 the treatment was effective, some other factor could have caused change of
 behavior.

THREE SURVEY
RESEARCH DESIGNS
LOOK over

Advantages and disadvantages
 Advantages
 of the Case Study Method
 ØRich source of ideas for
 developing hypotheses
 ØOpportunity for clinical
 innovation
 ØMethod for studying rare
 events
 ØPossible challenge to
 theoretical assumptions
 ØTentative support for a
 psychological theory
 Disadvantages
 of the Case Study Method
 ØDifficulty drawing
 cause&effect conclusions (limited internal validity)
 ØPossible biases when
 interpreting outcomes due to observer bias
 ØProb of generalizing findings
 for one person (limited external validity)

What if I want to
predict group membership based on results of a particular measure?
Use ROC CURVES: Sensitivity & Specificty

ROC CURVES: Sensitivity & Specificty
 Sensitivity: a ratio or proportion of the truepositive
 test results divided by all patients with the DOS.
 sensitivity = (a / (a + c))
 a + c = true positives+ false
 negatives = everyone with disorder
 The better the sensitivity
 of the test, the fewer the false negatives.
Specificity: a ratio or proportion of the truenegative test results divided by all patients without the disorder.
specificity =(d / (b + d))
(b + d) = sum of (false +s, true s) = those w/o disease
 The better the specificity
 of the test, the fewer false positives.

Finally, what
should I have learned in
Research Methods?
 1.Know which study design to use for different
 research questions.
 2.Start to develop the research and statistic
 language/ vocabulary.
 3.Keep these Research Methods concepts in mind
 to help you conceptualize what you will learn in Statistics 1 and 2.
 4.How to develop a research study design in
 psychology that is well thought out and ethical
 5.Know that all studies are not perfect. Using
 the knowledge from this course, you should be able to critically analyze any
 study, know its strengths and weaknesses, and NEVER take what they say at face
 value.

