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Levels of measurement and corresponding mathematical operations |
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NOIR
Nominal - John passed the test & Jean did not Ordinal - John has more knowledge of math than Jean Interval - John has twice the math knowledge of Jean
Ratio - John has no knowledge of math |
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- categorical - Numbers are used to classify and identify person • Numbers are substituted for names and verbal labels (e.g. uniform numbers identify football players) • Actual numbers not important |
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• ordering/ranking • Assigns #s to individual so that the rank order of the number corresponds to the rank order of the attribute being measured • The order does not indicate how far about the subjects are in an absolute sense • Ordinal information can help with decisionmaking |
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• The size of the difference between the numbers assigned to two persons or objects corresponds to the degree to which these persons or objects differ on the attribute being measured. |
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• Ratios between the numbers assigned to persons or objects correspond to ratios between the attributes measured in these persons or objects. |
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- yields only categorical data (data grouped according to common property) - number has no quantitative value - reported in the terms of the number of occurrences in each category - reported as 1 (average), 2 (gifted)
ex - number on players' uniforms
appropriate statistics - frequency, mode |
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- Numbers rank people based on the attribute being measured - distance between numbers vary; numbers only have meaning within the group
ex - rankings of bball players before the draft
appro stats - frequency, mode, median, percentile |
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- points on scale are equal distance apart - scale has no absolute zero
ex - measure of player's "desire to win"
appro stats - frequency, mean, mode, median, standard deviation, correlation, f test, analysis of variance |
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- points on scale are equal distance - there is an absolute zero
ex - players heights & game score
appro stats - frequency, mean, mode, median, standard deviation, correlation, f test, analysis of variance, proportions |
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Procedures for interpreting test scores |
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Raw scores Frequency distributions Normal curve Descriptive statistics |
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- most basic scores calculated from test - not useful without interpretation information |
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Techniques to make sense of raw scores
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Frequency Distributions The Normal Curve Descriptive Statistics |
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- Provide visual image of group data - Orderly arrangement of group of scores - Show number or percentage of observations that fall into a category or range |
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- distributions become more symmetrical because it is a better representation of the large population |
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- Summarize a distribution of test scores numerically - Provide us with main points of a group of scores - Typical descriptive statistics include: Measures of central tendency Measures of variability Measures of relationship |
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Measures of central tendency |
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• Measures of central tendency provide information about the typical score in a set of numbers. – Median – Mean – Mode |
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• “Average” • Takes into account every observation • Appropriate for interval or ratio data • Obtained by adding all the scores and dividing by the total # of scores |
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• Middle score of a distribution • To find: – 1 st Order all the scores. – If odd number of scores it is the middle score. – If even number of scores, average the two middle scores. • Appropriate for ordinal, interval, & ratio data |
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• Most frequently occurring score • Easy to find • A data set can have more than one mode (e.g. bimodal distribution) • Can be used with all measurement scales |
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- numerical tools to help understand the distribution - range, variance, standard deviation |
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Range = highest score – lowest score |
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• Problems with variance: – Squared units not on the same scale as the original measurements |
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Use total deviation? – Deviation score (d)– measures the extent to which each individual differs from the mean • Interpret as approximately the average distance of an observation from the mean |
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- how two sets of scores are related - correlation coefficient |
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• Standard Scores –Universally understood units in testing –Allow us to evaluate a person’s test score in reference to others’ test scores |
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change unit of measurement but do not change the characteristics of raw data
• Linear transformations – Percentages – Standard deviation units – Z scores – T scores |
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• Useful for interpreting your deviation score in comparison with other people’s deviation scores • Allows you to compare to test scores that are on different scales • Also, allows you to compare two scores on the same exam, but from groups that have different sample means and standard deviations (e.g. 8 o’clock class and 9 o’clock class) • Tells how far a score is from the mean in standard deviation units. • Z score mean = 0, sd=1.0 |
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• Way to describe a relationship between 2 variables • Tells: – Magnitude of relationship – Direction of relationship |
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- average scores of an identified group of individuals scores • Norms provide standards for interpreting |
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Norm-based interpretation |
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- process of comparing an individual’s test score to a norm group |
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• Types of norms -Percentile ranks -Age and grade norms |
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• Test publishers often publish more than one norm group • Must use correct norm group • Must use up-to-date norms • Should look at size of norm group |
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1. administering a test to a group of individuals, 2. re-administering that same test to the same group at some later time, and 3. correlating the first set of scores with the second. |
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Alternate forms reliability |
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Internal consistency 1. split-half 2. coefficient alpha |
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- how related the test items are to eachother - how the answer on one question predicts the answer on another test item
1. dividing the test in half equally/randomly, comparing the two sides 2. |
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Scorer reliability and agreement |
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Classical test theory view of reliability |
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Spearman-Brown formula – when to use, why? |
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Scorer reliability and agreement |
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Standard error of measurement – interpretation and uses |
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Factors that influence reliability (book) |
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