Research approaches
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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?


Research approaches

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 and quasi experimental designs

Correlational and Quasi-Experimental Designs

Chapter 5


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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