Research Exam

  1. Stem and Leaf Plot
    • Shows the frequency of happenings in stats
    • x
    • xxx
    • xx
    • xxxxx
    • xx
    • x
    • xxxxxxxx
  2. Histogram
    Frequency chart shown as bars
  3. Bell
  4. Bell Curve
    A chart showing the standard deviation from the normed center of the plotting
  5. Nominal Scale
    • Least Precise
    • Puts Variables into Categories
    • Like race, socio-economic
  6. Chi Squared
    Designed to measure the significance between 2 variables
  7. Ordinal
    • 2nd Least Precise measurement
    • Categorizes by magnitude highest to lowest
    • Class rank
    • No real detail about why the grades are the way they are, just that they are
  8. Interval
    • The Most Used for of Measurement in Education
    • Catergories by magnitude and understanding the why of the intervals in between
    • No Absoulte Zero...there will always be something
    • Uses Pearson r for correlation
    • Be wary of using this to make predictions...not always cause & effect
  9. Ratio
    • Most Precise
    • Categories of data showing magnitude
    • Intervals are the same size and there is true zero
    • Data can be manipulated
  10. Descriptive Statistics
    • Looking at the numbers and doing things like:
    • Averages
    • Median
    • Mode

    Correlation is not Causation

    • Cummulative Percentage Chart
    • Spearman is all about the rank order of things
  11. Central Tendency
    Single Value that is considered the most typical...a norm at which to determin standard deviation
  12. Validity
    Using the right tool to measure correctly

    Content Validity- the items on the instrument refelct the content

    Construct Validity- the extent to which a higher order construct is represented in a study (Help Seeking, stress, dyslexia...etc.)
  13. Reliability
    Having reliable scores

    Having temporal stability- stability over time

    Angoff Method- standard setting
  14. Internal Validity
    The ability to infer that a causal relationship exists between 2 variables
  15. External Validity
    The extent to which the study can be generalized and applied across populations
  16. Errors of Measurement
    Standard Deviations from the norm

    Always going to have errors in samples, mathematical

    The small the standard deviation the more accurate the measure
  17. Standard Error of Measurement
    The standard deviation of a sample population
  18. Null Hypothesis
    A statement about a population parameter some condition concerning the pop. parameter is true.

    prediction of no difference in a study when a new treatment is given
  19. Rejecting the Null Hypothesis
    When there is a relationship between populations due to the given treatment you reject the null hypothesis
  20. Accepting the Null Hypothesis
    Means you are admitting that there is no relationship between populations given the treatment
  21. Inferential Statistics
    Inferring information from a sample to a larger population
  22. Types 0f Samples
    • Random Sample
    • Sampling Interval
    • Stratified Sample
    • Cluster Sample
    • Convenience Sampling
    • Quota Sampling
    • Purposive Sampling (Judgemental)
    • Snowball Sampling
  23. Type One Error
    To say it had an effect, but it really didn't have one
  24. Type 2 Error
    To say the treatment did not have an effect when it really did
  25. Power
    The larger the sample size the more power a test is more sensitive. The Bigger the sample size the better...less likely to have a Null hypothesis
  26. Effect Size Indicator
    A statistical measure to show strength of a relationship
  27. Practical Significance
    A conclusion made when a relationship is strong enough to be of practical importance
  28. t Test
    Used to compare 2 group means

    Used to determine if the difference between sample populations was created merely by chance errors or really because of the treatment
  29. ANOVA
    ANOVA is used to compare one or more group means

    Can compare more than 2 groups

    Gives a p value
  30. Scientific Method
    • Question
    • Research
    • Hypothesis
    • Test
    • Analyze
    • Test Again
    • Report
  31. Alternate Hypothesis-Example
    Male and Female population's means on SAT tests are different
  32. Null Hypothesis- Example
    Male and Female population means on not different on the SAT
  33. Pearson Product Moment Correlation
    Shows a correlation between 2 ideas

    Higher tuition correlates to lower enrollment

    Tested with the Pearson r
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Research Exam
Research Final Exam