R.M.

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Author:
faulkebr
ID:
106350
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R.M.
Updated:
2011-10-04 10:29:43
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Relational Research
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  1. Are changes in X causes of changes in Y?
    • 1. Show that "delta" in Y did not occur until after "delta" in X
    • 2. Show that X and Y are related. The cause and effect must be correlated with each other
    • 3. Show that other explanations for the relationship between X and Y can be ruled out.
    • 4. Plausible causal link between X and Y
  2. Descriptive Research: Find out how things are
    Example facial expressions- eyebrow flash

    • 1. Important research method
    • 2. Sometimes first step in research program
    • 3. Often called case studies or 1-shot case studies
  3. Relational Research: Find out how things are in RELATION to other things
    Example Word usage and length of word; Social context and eyebrow flash

    Can show that X and Y are related (covariation rule)
  4. Contingency Table-
    Tabular presentation of all combinations of categories of two variables, which allows the relationships between the two to be examined
  5. Experimental Research-
    Find out how things are in relation to other things and how they got that way
  6. Experimental research:

    Independent variable and dependent variable-
    • 1. Show that delta in Y did not occur until after delta in X
    • 2. Show that X and Y are related
    • 3. Show that other explanations for the relationship between X and Y can be ruled out
  7. Relational Research-
    Find out how things are in relation to other things
  8. Relational research: find out how things are in relation to other things:
    • Measure the degree of relationship between 2 or more naturally occuring characteristics, behaviors,or variables.
    • Researcher doesnt change the value of the variables-looks at natural occurances
    • Thus no I.V.-no manipulation
    • Predictor Variable(first possible cause) & outcome variable (second: possible effect)
    • Ex. Gender and helping behavior, Growth in hippocampus & depression, height and shoe size
  9. Scatter Plots-
    • One way of doing relational research: look at linear correlation between predictor and outcome variable.
    • Typical statistic used in Pearson
  10. Pearson Correlation Coefficient: Sometimes transform data by performing some standard mathematical operation on each score. Transform data for a number of reasons:
    • 1. to obtain more convenient numbers
    • 2. to meet the assumptions of certain stats
    • 3. toobtain pretty graphs
    • 4. to meet the assumptions of some theory
    • 5. to minimize the influence of extreme scores
    • 6. to allow for comparison across different data sets such as diff. psychological tests or college examinations
  11. Two transformations:
    • 1. Percent (per hundred)
    • 2. Z scores
  12. Z-scores
    How far the score is from the mean in standard deviations units

    • 1. measure of relative position
    • 2. Can compare pple on different measures even if scales are different
    • 3. Convert set of scores to z scores: mean = 0.0 and standard deviation = 1.0
  13. The Standard Normal Curve has a mean of ____ and a standard Deviation of___
    • 0.0
    • 1.0
  14. Strongest Correlation:
    Both X and Y of each pair exactly same absolute distance from mean in Z score units
  15. Strongest Positive Correlation:
    Zx and Zy have same sign and same absolute distance from mean in Zs
  16. Main idea-Pearson Product moment correlation:
    Strong r if each member of pair-same relative position on its respective measure
  17. Scaling-
    assigning numbers to the magnitude of psychological events
  18. Fechner's Law:
    S = K log R
  19. Weber's Law
    • Sensitivity of sensory system depends on absolute strength of a stimulus.
    • Difference threshold = constant x intensity of the stimulus
  20. Steven's Power Law
    S = k RN

    Equal physical ratios are psychologically equal
  21. Scaling: Psychometrics-
    Assign numbers to experiences caused by stimuli that vary along a single psychological dimension
  22. Regression is
    symbolized by R rather than r
  23. Correlations have suspect internal validity because of:
    • The third variable problem
    • truncated range
    • Unknown direction of causation
  24. The _______ ______ procedure enhances the internal validity
    cross-lagged panel
  25. Experiments are internally valid because:
    the causal variables and the direction of causation are unknown

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