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Research Approaches. Internal Validity External Validity “A study that is fetchingly realistic might bring us no closer to the truth than one that seems painfully contrived” (Myers & Hansen, 2006, p. 63). Dimensions of Research. Antecedent Manipulation Treatments Independent variable (IV)

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research approaches
Research Approaches
  • Internal Validity
  • External Validity

“A study that is fetchingly realistic might bring us no closer to the truth than one that seems painfully contrived” (Myers & Hansen, 2006, p. 63).

dimensions of research
Dimensions of Research
  • Antecedent Manipulation
    • Treatments
    • Independent variable (IV)
  • Imposition of Units
    • Behavioral measures
    • Dependent variable (DV)
dimensions of research1
Dimensions of Research

High/

Low

High/

Medium

High/

High

High

Imposition of Units

Medium/

Low

Medium/

Medium

Medium/

High

Medium

Low/

Low

Low/

Medium

Low/

High

Low

Low

Medium

High

Antecedent Manipulation

true experiments
True Experiments

High/

High

High

Imposition of Units

Medium

Low

Low

Medium

High

Antecedent Manipulation

nonexperimental approaches
Nonexperimental Approaches
  • Phenomenology – attending to and describing one’s own experience
  • Case studies – outside observer records an individual’s experiences & behaviors
  • Field studies – research method conducted in the field using a variety of techniques
  • Archival studies – reexamine existing data for a new reason
  • Qualitative studies – data are verbal descriptions rather than numbers
nonexperimental approaches1
Nonexperimental Approaches
  • Phenomenology -

Description of one’s own immediate experience

Examples: pain in my C5 vertebrae

the Purkinje effect

phenomenology
Phenomenology

High

Imposition of Units

Medium

Low/

Low

Low

Low

Medium

High

Antecedent Manipulation

nonexperimental approaches2
Nonexperimental Approaches
  • Case studies -

Descriptive records of another individual’s experiences or behavior.

Evaluative case studies– case compared to hypothetical “normal” psychological diagnosis – DSM-IV

Deviant case analysis– deviant case compared to “normal” for significant differences.

e.g. Mednick, 1969 – ANS of schizophrenic children functions different compared to normal controls.

case studies
Case studies

High/

Low

High

Imposition of Units

Medium/

Low

Medium

Low/

Low

Low

Low

Medium

High

Antecedent Manipulation

nonexperimental approaches3
Nonexperimental Approaches
  • Field studies -

Studies done in situ, in real-life settings as opposed to the laboratory.

e.g. A field Experiment in Chicago (p. 86).

field studies
Field studies

High/

Low

High

Imposition of Units

Medium/

Low

Medium

Low/

Low

Low

Low

Medium

High

Antecedent Manipulation

nonexperimental approaches4
Nonexperimental Approaches
  • Naturalistic observation -

a technique of observing behaviors as they occur spontaneously in the natural setting.

e.g. dominance hierarchies in social groups.

naturalistic observation
Naturalistic Observation

High

Imposition of Units

Medium

Low/

Low

Low

Low

Medium

High

Antecedent Manipulation

nonexperimental approaches5
Nonexperimental Approaches
  • Systematic observation -

a technique of using specific rules in a pre-arranged way to objectively record observations.

Female sexual receptivity (rodents only)

Lordosis- 1. darting, 2. ear wiggling 3. inverted back and 4. tail diversion

nonexperimental approaches6
Nonexperimental Approaches
  • Participant-observer studies -

the researcher becomes part of the group being studied.

Undercover roid guy… just what baseball needed!

nonexperimental approaches7
Nonexperimental Approaches
  • Archival study -

already existing records are reexamined for a new purpose. E.g. data on crime, death rates, education levels, salaries housing patterns and disease rates are accessible to researchers.

Bioinformatics

Gene database

nonexperimental approaches8
Nonexperimental Approaches

Self-reports personal narratives expression of ideas, memories, feelings and thoughts

  • Qualitative research

relies on words rather than numbers

Is there a paradigm shift occurring?

slide18

Phenomenology is used as part of qualitative research

Contemporary or Empirical Phenomenology

  • Researcher self-reflects on experiences related to the phenomenon
  • Others provide verbal or written descriptions of experiences
  • Accounts of the phenomenon are gathered from literature, art, television, the internet and other sources
correlational designs
Correlational Designs

Determine the degree of relationship between two traits, behaviors or events; predict one set from another.

  • Antecedents are preexisting
  • Degree of imposition of units - high
  • Tend to be higher in external validity
correlational designs1
Correlational Designs

Low/

High

High

Imposition of Units

Medium

Low

Low

Medium

High

Antecedent Manipulation

quasi experimental designs
Quasi-experimental Designs

Can seem like an experiment, but subjects are not randomly assigned to treatment conditions.

  • Antecedent control varies
  • Degree of imposition of units - high
  • Tend to be higher in external validity
quasiexperimental designs
Quasiexperimental Designs

Low/

High

meduim/

High

High

Imposition of Units

Medium

Low

Low

Medium

High

Antecedent Manipulation

example of a quasiexperiment
Example of a Quasiexperiment

Lighting condition – fluorescent vs incandescent.

Subjects – from company A (fluorescent lights) or B (incandescent).

Performance measure – productivity.

Can cause-effect be established with confidence?

pearson product moment correlation coefficient r
Pearson Product-Moment Correlation Coefficient (r )

Most common procedure for calculating simple correlations – relationship between pairs of scores for each subject. Three outcomes are possible:

  • Positive relationship
  • Negative relationship
  • No relationship
scatterplots
Scatterplots

Visual representations of the scores belonging to each subject in a study. Each dot = two scores (x,y) from one subject.

  • One score places the dot along the horizontal axis (x) and the other score places it along the vertical (y) axis.
  • Regression lines (of best fit) represent the mathematical equation that best represents the relationship between the two measured scores.
hypothetical relationships
Hypothetical Relationships

A.

B.

Positive r = +.69

Negative r = -.72

Variable Y

Variable Y

Variable X

Variable X

C.

No correlation r = -.02

Variable Y

Variable X

four possible causal directions of a correlation
Four possible causal directions of a correlation
  • Given a strong positive relationship between childhood aggressiveness and watching violent TV (r = +.70).
  • Watching violent TV  aggressiveness
  • Aggressiveness  watching violent TV
  • Aggressiveness  watching violent TV
  • Both are caused by a third variable (unknown or not measured, e.g., parental supervision)
coefficient of determination
Coefficient of determination
  • Estimates the amount of variability in scores on one variable that can be explained by the other variable.
  • E.g., if r = .56, then r 2 = .31.
  • 31% of the variability in scores on variable X can be accounted for by variable Y.
  • An r 2 ≥ .25 can be considered a strong association.
regression equation
Regression equation

Positive r = +.56

Y

slope

Variable Y: calculate mean and S

Y intercept

X

Variable X: calculate mean and S

regression equation1

Y = Y + r [Sy / Sx] (X – X)

Regression Equation
  • Given the score on one variable you can predict the score on the other if you know:
    • The value of r
    • Average scores of X and Y (the means)
    • Standard deviation (S) of X and Y
multiple regression
Multiple Regression
  • Used to predict the score on one behavior from the scores on others included in the analysis.
  • The regression equation provides beta weights for each predictor (indicating their importance)
  • Beta weights can simply be reported or used in an advanced correlational analysis to construct causal sequences for the behaviors.
multiple correlation
Multiple Correlation
  • Intercorrelations among 3 or more behaviors (R)
  • Can not explain why the 3 measures are related but it may suggest that a “third variable” is important.
  • Influence of one variable is held constant while measuring the correlation between the other two – partial correlation
causal modeling
Causal Modeling
  • Advanced correlational techniques
  • provide information about the direction of the cause and effect sequences among variables. Two techniques:
    • Path analysis
    • Cross-lagged panel designs
path analysis
Path Analysis
  • Creates models of possible causal sequences when several related behaviors are measured
  • Beta weights from multiple regression analysis are used to evaluate the direction of cause and effect from correlated variables.
  • Internal validity is low (correlational data), consequently causal statements can not be made.
path analysis1
Path Analysis

Perceived

Risk

.25**

.20*

.30**

Monitoring

Intrusive

Thoughts

.37**

Psychological

Distress

Internal validity?

Third variables?

* p < .05, ** p < .01

From Schwartz, Lerman, Miller, Daly, and Masny (1995)

cross lagged panel design
Cross-Lagged Panel Design
  • Uses relationships measured over time to suggest causal models.
  • The same pair of related behaviors or characteristics are measured at two separate time points for each subject.
  • Can only suggest the direction of causal relationships (not conclusive).
  • Bidirectional causation and the third variable problem cannot be ruled out.
cross lagged panel design1

Age 3

Age 8

r = .14

Time watching

TV

Time watching

TV

r = .05

r = .20

r = .07

r = -.59

Size of

Vocabulary

Size of

Vocabulary

r = .41

Cross-Lagged Panel Design

Hypothetical Cross-Lagged Panel design

quasiexperimental designs1
Quasiexperimental Designs
  • Subjects cannot be randomly assigned to different treatments
  • Quasi-treatments are formed based on a particular event, characteristic or behavior of interest.
  • E.g., gender differences in sleep patterns.
  • Low internal validity.
quasiexperimental designs2
Quasiexperimental Designs
  • Subjects may be exposed to different treatments, but without random assignment (e.g. the lighting-productivity study)
  • There is a lack of control over other potential confounds (i.e., an inability to hold all else constant except for the treatment condition).
ex post facto studies
Ex Post Facto Studies
  • Ex Post Facto – systematic examination of the effects of subject variables (characteristics) without manipulation.
  • Low Antecedent Manipulation
  • High Imposition of Units
  • Greater external validity
nonequivalent groups
Nonequivalent Groups
  • A manipulation is carried out but subjects are not randomly assigned to groups
  • E.g. the lighting experiment yet again
  • Internal validity can be increased by controlling extraneous variables after careful consideration of potential confounds.
longitudinal designs
Longitudinal Designs
  • Measure the behavior of the same group of subjects across time.
  • A form of within-subject design
  • Important for studying growth and development and aging
  • Retaining subjects may be difficult
cross sectional studies
Cross-sectional Studies
  • Investigates changes across time by comparing groups of subjects already at different stages at a single point in time.
  • Typically requires more subjects than the longitudinal study.
  • Subjects may differ in ways other than those being studied (similar to Ex post facto).
pretest posttest design
Pretest/Posttest Design
  • Investigates the effects of a treatment by comparing behavior before and after the treatment.
  • Practice effects (pretest sensitization)
  • Outside influences cannot be ruled out
  • Low internal validity

e.g., exposure to cocoa on cognitive performance

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