Questions and Answers
What are raw scores?
The total scores from tests.
How are raw scores turned into derived scores?
Raw scores must be converted to derived scores such as percentiles, standard scores, T scores, or stanines.
What does a higher percentile indicate?
A higher rank indicates a higher score.
What is the purpose of standard scores?
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What is the mean and standard deviation for T scores in personality testing?
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What does item difficulty refer to?
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What is item discrimination?
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What are norms in psychological testing?
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What does criterion-referenced testing measure?
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Why are scores combined in psychological testing?
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What are raw scores?
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What is the highest percentile score?
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What is standard deviation?
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The formula for converting raw scores to z scores is z = ___
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What do T scores represent in educational testing?
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What is item difficulty?
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What is item discrimination?
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What kind of sampling can norms be based on?
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Which of the following is a method for combining test scores?
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Criterion-referenced testing compares scores against group norms.
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Study Notes
Interpreting Test Scores
- Raw scores represent total points from a test but lack context without comparison to normative data.
- Derived scores like percentiles, standard scores, T scores, and stanines provide meaningful interpretations of raw scores.
- Percentiles rank individual scores within group scores; range from 1 (lowest) to 99 (highest).
- Standard scores (z scores) quantify deviation from the mean, indicating whether a score is above or below average.
- T scores simplify z scores by standardizing the mean to 50 and standard deviation to 10 for personality tests, or mean of 500 and SD of 100 for educational tests.
- Stanine scores categorize performance into nine levels, with a mean of 5 and SD of 2.
Item Characteristics
- Item difficulty indicates the percentage of participants answering an item correctly; an ideal average difficulty is around 50%.
- Item discrimination assesses how well an item differentiates between high and low scorers on the measured variable.
- High item discrimination requires establishing criteria for what constitutes high or low scores, using methods like factor analysis or empirical approaches.
- Item Response Theory considers individual proficiency and the probability of correct responses based on item difficulty and test discrimination.
Norms
- Norms reflect performance benchmarks derived from population data, which can be obtained through various sampling methods including random, stratified, and convenience samples.
- Expectancy tables predict expected performance based on specific scores in terms of percentage.
- Criterion-referenced testing evaluates performance against explicit or implicit criteria instead of group norms, focusing on mastery of particular skills.
Combining Test Scores
- Combining scores enhances result interpretation and aids in decision-making processes.
- Individual item scores can be aggregated into total scores, which can then be combined across multiple tests for composite scores.
- Scoring methods for combining may involve converting raw scores to standardized formats (z or T scores) or using examiner judgment.
Interpreting Test Scores
- Raw scores represent total points from a test but lack context without comparison to normative data.
- Derived scores like percentiles, standard scores, T scores, and stanines provide meaningful interpretations of raw scores.
- Percentiles rank individual scores within group scores; range from 1 (lowest) to 99 (highest).
- Standard scores (z scores) quantify deviation from the mean, indicating whether a score is above or below average.
- T scores simplify z scores by standardizing the mean to 50 and standard deviation to 10 for personality tests, or mean of 500 and SD of 100 for educational tests.
- Stanine scores categorize performance into nine levels, with a mean of 5 and SD of 2.
Item Characteristics
- Item difficulty indicates the percentage of participants answering an item correctly; an ideal average difficulty is around 50%.
- Item discrimination assesses how well an item differentiates between high and low scorers on the measured variable.
- High item discrimination requires establishing criteria for what constitutes high or low scores, using methods like factor analysis or empirical approaches.
- Item Response Theory considers individual proficiency and the probability of correct responses based on item difficulty and test discrimination.
Norms
- Norms reflect performance benchmarks derived from population data, which can be obtained through various sampling methods including random, stratified, and convenience samples.
- Expectancy tables predict expected performance based on specific scores in terms of percentage.
- Criterion-referenced testing evaluates performance against explicit or implicit criteria instead of group norms, focusing on mastery of particular skills.
Combining Test Scores
- Combining scores enhances result interpretation and aids in decision-making processes.
- Individual item scores can be aggregated into total scores, which can then be combined across multiple tests for composite scores.
- Scoring methods for combining may involve converting raw scores to standardized formats (z or T scores) or using examiner judgment.
Interpreting Test Scores
- Raw scores are the initial total scores from tests, lacking meaning until compared with norm scores for context.
- Derived scores such as percentiles, standard scores, T scores, and stanines provide meaningful comparisons for individual performance against groups.
Percentiles
- Percentiles rank individual scores within a group, ranging from 1 (lowest) to 99 (highest).
- Higher percentiles indicate better performance; however, percentiles are ordinal and do not equalize score differences.
Standard Scores
- Standard deviation quantifies how far a score is from the mean, allowing comparisons across different tests.
- Raw scores can be converted into standard scores (z scores) for evaluation of performance deviations.
T Scores
- T scores are derived from z scores, facilitating easier comparisons with a set mean and standard deviation (e.g., personality tests: mean of 50, SD of 10; educational tests: mean of 500, SD of 100).
- The conversion formula is T = z (desired SD) + desired M.
Stanines
- Stanine scores transform raw scores into a normal distribution ranging from 1 (low) to 9 (high), with a mean of 5 and SD of 2.
- Assignment of stanine scores is based on the percentage of scores within the normal distribution.
Item Characteristics
- Item Difficulty: Measured as the percentage of correct responses; ideal difficulty level averages around 50% but may vary by test type.
- Item Discrimination: Indicates how well an item differentiates between high and low performers, defined through score ranges based on internal consistency or external criteria.
Item Discrimination (Continued)
- Based on Item Response Theory, considerations include individual ability, test discriminatory power, item difficulty, and guessing probabilities.
Norms
- Norms represent population performance and can be derived through random or stratified sampling, reflecting demographic characteristics (e.g., age, education).
- Expectancy tables can be used to predict expected scores in percentage terms.
Criterion-Referenced Testing
- In these tests, scores are assessed against a defined criterion rather than group norms.
- Criteria can be explicit (e.g., specific tasks to meet mastery) or implicit (based on examiner judgment).
Combining Test Scores
- Scores are aggregated to yield interpretations and informed decisions.
- Methods for combining scores include converting raw scores to z or T scores and making composite scores via differential or unit weighting, or subjective evaluation by the examiner.
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Description
This quiz covers key concepts in interpreting test scores, including raw and norm scores, as well as item characteristics and combining test scores. It is designed for students in the PSY61204 Psychological Tests and Measurements course. Test your understanding and application of these essential topics.
