13. Statistics, Tests and Measurements (Ch2)

13.1 Descriptive and Inferential Statistics Samples, populations, norms

  • Statistics
    • collection, analysis, interpretation, and presentation of numeric data
  • Samples
    • representative subset of larger population
    • random sample
  • Populations
    • group of people looking to study
  • Norms
    • identifying normal behavior of group to compare to
    • standardizing
  • Descriptive Statistics
    • used for correlational and experimental designs
    • measurements of behavior from sample
  • Mean
    • average
  • Mode
    • most commonly occurring score
  • Median
    • middle score, separates lower and upper halves of scores
  • Standard Deviation
    • statistical measure of how much scores in a sample vary around the mean
    • higher SD = more variability (more spread)
    • lower SD = less variability (less spread)
  • Normal Distribution
    • bell curve showing symmetrical alignment of two variables (e.g Intelligence)
  • Inferential Statistics
    • inferences about population based on characteristics of sample
  • statistical significance
    • not likely to have happened by chance
    • significant equals 5% of the time or less

13.2 Reliability and Validity

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  • Reliability

    • stability and consistency of scores
    • does not need to be valid to be reliable
  • Types of Reliability

    • test-retest reliability

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    • internal consistency
      • How well does a test correlate with itself
    • split-half reliability
      • Cronbach’s alpha: avg correlation for every way a test can be split in half

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  • Validity

    • how well a test measures what it is supposed to measure
    • must be reliable to be valid
  • Types of validity

    • face/content validity
      • whether a test looks as though it is measuring what it is supposed to measure
    • predictive validity
      • how well scores on the test predict the actual behavior of the type that the test is supposed to measure
    • construct validity
      • whether the scores on a questionnaire are related in expected ways, either positively or negatively, to scores on other questionnaires that are proposing to measure the same thing. Image result for types of validity
  • standardizing measures

13.3 Types of Tests

  • Tests used to rule out chance
  • t-test: computed for two means to see if they come from same population (e.g., of two groups or variables)
  • ANOVA: analysis of variance

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  • Pearson correlation coefficient (-1.0 to +1.0)

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13.4 Measurement of Intelligence

  • Stanford-Binet Intelligence Scale

    • first IQ test
    • still widely used today
    • norming and standardization
  • Wechsler Intelligence Tests

    • WAIS- IV: Adult
    • WISC-V: Children
    • WPPSI-IV: Pre-school and primary school

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  • Flynn effect

    • each generation, higher IQ

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  • Which of the following is a measure of central tendency that can be easily distorted by unusually high or low scores?
    • (A) Mean
    • (B) Mode
    • (C) Median
    • (D) Range
    • (E) Standard deviation
  • Which of the following statistics indicates the distribution with the greatest variability?
    • A variance of 30.6
    • (B) A standard deviation of 11.2
    • (C) A range of 6
    • (D) A mean of 61.5
    • (E) A median of 38
  • Which of the following is a true statement about the relationship between test validity and test reliability?
    • (A) A test can be reliable without being valid.
    • (B) A test that has high content validity will have high reliability.
    • (C) A test that has low content validity will have low reliability.
    • (D) The higher the test’s validity, the lower its reliability will be.
    • (E) The validity of a test always exceeds its reliability.
  • If the null hypothesis is rejected, a researcher can conclude that the
    • (A) treatment effect was significant
    • (B) theory must be modified, a new hypothesis formed, and the experimental procedure revised
    • (C) theory does not need modification, but the hypothesis and the experimental procedure need revision
    • (D) theory and hypothesis do not need modification, but the experimental procedure needs revision
    • (E) hypothesis is false
  • In order to illustrate how often a particular score occurs in a given data set, researchers use
    • (A) inferential techniques
    • (B) cognitive mapping
    • (C) cluster analysis
    • (D) the median
    • (E) a frequency distribution

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