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PSYC512: Research Methods Lecture 7

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... through a process of converging operations (Garner, Hake, & Eriksen, 1956) ... Example: The phenomenon of 'Perceptual' Defense (Garner, Hake, & Eriksen, 1956) ... – PowerPoint PPT presentation

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Title: PSYC512: Research Methods Lecture 7


1
PSYC512 Research Methods Lecture 7
  • Brian P. Dyre
  • University of Idaho

2
Lecture 7 Outline
  • Questions about material covered in Lecture 6
  • Measures scales and sensitivity
  • More on Measurement
  • Reliability, Precision, and Validity
  • Hypothesis testing and Variables
  • Variables and Research Design
  • Defining Variables

3
Features of Measures Reliability
  • The ability of a measure to produce consistent
    results when repeated measurements are taken
    under identical conditions
  • Types
  • precision physical measurement (1/noise)
  • margin of error sampling in surveys
  • interrater reliability observers viewing the
    same behavior
  • Test-retest, parallel forms and split-half
    reliabilities psychological tests

4
Other Features of Measures
  • Accuracy
  • does a measure produce results that agree with a
    known standard?
  • Accuracy vs. Precision
  • Validity
  • Measurement validity the extent to which your
    measure indeed measures what it is intended to
    measure
  • Types Face validity, Content validity,
    Criterion-related validity (concurrent vs.
    predictive), Construct validity
  • Relationship between reliability and validity

5
Hypothesis Testing Variables
  • Hypothesis testing is the process by which
    hypothetical relationships between variables
    (something that varies in quantity or quality)
    are assessed (the relationships are deduced from
    one or more theories)
  • Types of variables
  • Dependent variable ? measure
  • Independent variable ? manipulation
  • Extraneous variable ? not pertinent to hypotheses
  • Confounding variable ? extraneous variable that
    covaries with your manipulated variable
    (typically we try to control these to eliminate
    the covariance)
  • Intervening variable ? theoretical construct of
    interest that is not directly observable (e.g.,
    group cohesiveness, mental workload)

6
Variables and Research Designs
  • Relationships can be hypothesized between
  • Multiple dependent measures ? correlational
    research design presence or absence of a
    relation between the variables can be tested, but
    not causality
  • Manipulated (independent) variables and some
    measure ? experimental design, with proper
    control of confounding variables (e.g., random
    assignment to experimental treatment groups)
    causality may be inferred

7
Defining Variables Operationism
  • Operationism psychological concepts are
    equivalent to the operations (manipulations or
    measures) used to define those concepts
  • Hunger the state produced by food deprivation
  • Only observable operations are included in
    theoretical or hypothetical statements
  • You cannot separate the concept from its
    operationscannot generalize, concept has no
    external validity

8
Defining Variables Converging Operations or
Network Specification
  • Multiple operations or a set of operations can be
    used to define a concept, not just one
  • Operations can converge to scientifically isolate
    intervening variables through a process of
    converging operations (Garner, Hake, Eriksen,
    1956)
  • selective influence experimental manipulations
    affect particular intervening variables but not
    others
  • convergence different operations can be used to
    manipulate or measure a common intervening
    variable or psychological construct

9
Converging Operations
  • Example The phenomenon of Perceptual Defense
    (Garner, Hake, Eriksen, 1956)
  • Two Possibilities
  • perceptual discrimination of vulgar words takes
    longer
  • responding with a vulgar word takes longer
  • Operationist perception is the discrimination
    response, therefore, we cant tell which
  • Converging operations add a second, orthogonal
    operationexchange the vulgar and neutral
    response mappings

10
Network Specification of Meaning
  • Psychological Concepts are defined by their
    relations with other concepts rather than a
    unitary operational definition
  • Introduction and Discussion sections of papers
    describe the relationships of our variables to
    all other relevant variables and conceptswhat G,
    H, E call assumed operations
  • Method and results sections describe the specific
    converging operations we use

11
Construct Validity
  • The soundness of our operations, do they
    manipulate or measure the intervening variable
    that they are intended to manipulate or measure?
  • Types (Campbell Fiske, 1959)
  • Discriminant validation operation should not
    affect or correlate with operations on other
    intervening variables
  • Convergent validation operation should affect or
    correlate with other operations on the same
    intervening variable

12
Testing Hypotheses
  • Hypothesis testing is the process by which
    hypothetical relationships between intervening
    variables are assessed
  • Hypotheses are always tested relative to
    one-another or to a null hypothesis
  • Examples
  • Comparing Groups
  • Assessing Performance Interventions
  • Assessing Relationships between variables
  • Problem Measurement Noise

13
Hypothesis Testing Probability and Statistics
  • Why are probability and statistics important?
  • Used to assess variability in a measure
  • Effect (treatment) Variance
  • Variability due to relationship between variables
    or effect of different levels of independent
    variable (treatments)
  • Good variance that we want to maximize
  • Error Variance
  • Variability in measure due to factors other than
    the treatment
  • Bad variance that we want to minimize
  • Probability and Statistics are simply tools used
    to assess (descriptive statistics) and compare
    (inferential statistics) these sources of
    variability

14
Visualizing Variability Distributions of
Frequency and the Histogram
  • Histograms used to represent frequencies of data
    in different classes or categories

15
Displaying Histograms Stem and Leaf Plots
  • Stem and Leaf plots are used to display
    histograms graphically (on their side) using only
    typed characters
  • Stem Leaf (hypothetical histogram for IQ)
  • 6 78
  • 7 35668
  • 8 012234445555667777889
  • 9 00011233333334445566667889999
  • 10 01112233334444445566677777888899
  • 11 0001122233444566777899
  • 12 0012569
  • 13 02

16
Distributions of Probability Density
  • Similar to frequency histogram except y-axis now
    represents probability density (mass) rather than
    frequency
  • Probability density Frequency/N

17
Some Types of Distributions
  • Normal Gamma

18
Measures of the Center of a Distribution
  • Measures of center represent the general
    magnitude of scores in a distribution
  • Mode most frequent score
  • Median the middle score of an ordered
    distribution
  • Mean (average) where X is the data and
  • N is the total number of observations

19
Measures of the Spread of a Distribution
  • Measures of spread are used to assess the
    consistency of scores in a distribution
  • Range max score min score
  • Interquartile range score(Q3) score(Q1)
  • Variance (s2) and standard deviation (s)
  • where X is the data,
  • m is the mean of the data, and N is the
    total number of observations

20
More on Variance
  • Standard Deviation (s) sqrt(variance)
  • where X is the data,
  • m is the mean of the data, and N is the
    total number of observations
  • Why N instead of N-1? Populations vs. Samples
  • Remembering how to compute variance
  • the mean of the squares square of the means

21
Describing Distributions Parametrically
Statistical Moments
  • Any distribution based on interval or ratio data
    can be summarized by its statistical moments
  • First Moment Meanlocation of distribution on
    x-axis
  • Second Moment Variancedispersion of
    distribution
  • Third Moment Skewnesssymmetry of distribution
  • Fourth Moment Kurtosisdegree of peakedness

22
Estimators
  • Sample statistics estimate population parameters
  • Mean M or vs. M
  • Variance s2 vs. s2
  • Properties of Estimators
  • Sufficiency uses all information in sample (mean
    and variance are sufficient, mode and range are
    not)
  • Unbiasedness expected value approaches real
    value with increased sampling
  • Efficiency tightness of cluster of sample
    statistics relative to the population parameter
  • Resistance influence of outliers on sample
    statistic

23
Next Time…
  • Topic Research Designs and Inferential
    Statistics
  • Be sure to
  • Read the assigned readings (Howell chapters 6-7)
  • Continue searching and reading the scientific
    literature for your proposal
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