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Assessment Concepts

Assessment Concepts. Dr. Julie Esparza Brown Sped 512: Diagnostic Assessment Week 4 Portland State University. Normal Distribution. Symmetrical Unimodal. No Skew. No Kurtosis. You always know the percent of the distribution in any part of the normal curve. Raw Scores.

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Assessment Concepts

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  1. Assessment Concepts Dr. Julie Esparza Brown Sped 512: Diagnostic Assessment Week 4 Portland State University

  2. Normal Distribution • Symmetrical • Unimodal. • No Skew. • No Kurtosis. • You always know the percent of the distribution in any part of the normal curve.

  3. Raw Scores • Raw scores convey very little meaning unless referenced to some standard,

  4. Percentiles (Relative Standing) • The percent of people in the comparison group who scored at or below the score of interest. • Example: • Billy obtained a percentile rank of 42. • This means that Billy performed as well or better than 42% of children his age on the test. • Or, 42% of children Billy’s age scored at or below Billy’s score. • Or, Billy is number 42 in a line of 100 people.

  5. Advantages of Percentiles Ranks • Percentile ranks are one of the best types of score to report to consumers of a child’s relative standing compared to other children. • Scores indicate how well a student performed compared to the performance of some reference group, • Percentile ranks are Ordinal Scale (values ordered from worst to best but differences between adjacent values are unknown) data, • It is not meaningful to calculate the mean or standard deviation of percentiles.

  6. Standard Scores (Relative Standing) Standard scores are scores of relative standing with a set, fixed, predetermined mean and standard deviation.

  7. WJ-III Scores • WJ-III uses standard scores with a mean of 100 and a standard deviation of 15. • A person earning a score of 85 would be one standard deviation below the mean. • A person earning a score of 115 would be one standard deviation above the mean. • Standard scores are equal-interval scores so they can be combined (e.g., added or averaged).

  8. Age & Grade Equivalents (Developmental Scale) • There are problems with using these scores • Identical age equivalents can mean different task performance.

  9. Problems with Grade and Age Equivalent Scores • Systematic misinterpretation: students who earn an AE of 12.0 has answered as many questions as the average for children of 12. They have not necessarily performed as a 12 year old could. • Implication of a false standard of performance: equivalent scores are constructed so that 50% of any age or grade group will perform below or above age or grade level. • Tendency for scales to be ordinal, not equal interval: age and grade equivalent scores are ordinal, not equal interval: they should not be added or multiplied. Source: Salvia, Ysseldyke & Bolt (2009)

  10. Maria got an age equivalent of 2-0 on a test means: Maria obtained the same number correct as the estimated mean of children 2 years and 0 months of age, It does NOT mean: Maria performed like an average 2 year old on the test. Age & Grade Equivalents (Developmental Scale)

  11. John got a grade equivalent of 3.5 on a test means: John obtained the same number correct as the estimated mean of children 5th month of 3rd grade. It does NOT mean: John is able to do 3.5 grade level work. Age & Grade Equivalents (Developmental Scale) Bottom Line – Do not use grade or grade level scores.

  12. Scales of Measurement

  13. Nominal Scale (Name) • A scale of measurement in which there is no inherent relationship among adjacent values. • Each number reflects an arbitrary category label rather than an amount of a variable. • Nominal Scales are used to indicate classification, category, or group. • Examples • Football jersey numbers • Group 1 • Group2 • Diagnostic categories

  14. Ordinal Scale (Order) • A scale on which values of measurement are ordered from best to worst or from worst to best; on ordinal scales, the differences between adjacent values are unknown. • Ordinal Scales provide order and ranking information (1st, 2nd, 3rd, etc.) • Are used to indicate when one value has more or less of something than another. • The central tendency of an ordinal attribute can be represented by its mode or its median, but the mean cannot be defined. • Examples: • Rank in high school class • Percentile rank • Age and grade equivalent • Results of a horse race

  15. Interval Scale (Interval/Distance) • Interval Scales provide distance (interval) information. • Differences have meaning. Equal differences in the numbers correspond to equal differences in the attributes. • Most data in education will be interval scale data. • Examples: • IQ scores • Test scores • Rating scales

  16. Ratio Scale (Ratio/Absolute 0) • A scale of measurement in which the difference between adjacent values is equal and in which there is a logical and absolute zero. • Ratio Scales provide absolute amount information. • Examples: • Counts of behavior • income

  17. Measures of Central Tendency

  18. Mean (Most useful) • Mean – the average of all the scores in the distribution. • Appropriate for Equal Interval and Ratio Scales. • Not appropriate for skewed distributions.

  19. Median (Next most useful) • Median – the middle score of a distribution. • Appropriate for Ordinal, Equal Interval, and Ratio Scales. • Most appropriate when distribution is skewed, • 50% of scores are above the median, 50% of scores are below the median. • Example • Arrange scores in order from largest to smallest (or vice versa) • If N is odd, the middle score is the median. • If N is even, the average of the two middle scores is the median.

  20. Mode (Least useful) • The Mode is the most frequently occurring score. • Appropriate for Nominal, Ordinal, Equal Interval, and Ratio Scales. • Generally used in a very rough sense to get a feel for “the peak of the mountain.”

  21. Measures of Spread or Variability

  22. Standard Deviation • Standard Deviation (S) indicates the spread or variability of a distribution; the square root of the variance. • Appropriate only for equal interval and ratio scales. • Also used as a unit of measurement.

  23. Technical Adequacy of Instruments

  24. The Reliability Coefficient • An index of the extent to which observations can be generalized; the square of the correlation between obtained scores and true scores on a measure. • The proportion of variability in a set of scores that reflects true differences among individuals. • If there is relatively little error, the ratio of true-score variance to obtained-score variance approaches a reliability index of 1.0 (perfect reliability) • If there is a relatively large amount of error, the ratio of true-score variance to obtained-score variance approaches .00 (total unreliability). • We want to use the most reliable tests available.

  25. Standards for Reliability • If test scores are to be used for administrative purposes and are reported for groups of individuals, a reliability of .60 should be the minimum. The relatively low standard is acceptable because group means are not affected by a test’s lack of reliability. • If weekly (or more frequent) testing is used to monitor pupil progress, a reliability of .70 should be the minimum. This relatively low standard is acceptable because random fluctuations can be taken into account when a behavior or skill is measured often.

  26. Standards for Reliability • If the decision being made is a screening decision, there is still a need for higher reliability. For screening devices, a standard of .80 is recommended. • If a test score is to be used to make an important decision concerning an individual student (such as special education placement), the minimum standard should be .90.

  27. Standard Error of Measurement • SEM is another index of test error. • It is the average standard deviation of error distributed around a person’s true score. • The difference between a student’s actual score and their highest or lowest hypothetical score. • We generally assess a student once on a norm-referenced test so we do not know the test taker’s true score or the variance of the measurement error that forms the distribution around that person’s true score. • We estimate the error distribution by calculating the SEM. • The general formula SEM equals the standard deviation of the obtained scores, multiplied by the square root of 1 minus the reliability coefficient. • When the SEM is relatively large, the uncertainty that the student’s true score will fall within the range is large; when the SEM is relatively small, the uncertainty is small.

  28. Confidence Interval • The range of scores within which a person’s true score will fall with a given probability. • Since we can never know a person’s true score, we can estimate the likelihood that a person’s true score will be found within a specified range of scores called the confidence interval. • Confidence intervals have two components: • Score range • Level of confidence

  29. Confidence Interval • Score range: the range within which a true score is likely to be found • A range of 80 – 90 tells us that a person’s true score is likely to be within that range • Level of confidence: tells us how certain we can be that the true score will be contained within the interval • If a 90% confidence interval for an IQ is 106 – 112, we can be 90% sure that the true score will be contained within that interval. • It also means that there is a 5% chance the true score is higher than 112 and a 5% chance the true score is lower than 106. • To have greater confidence would require a wider confidence interval. • You will have a choice of confidence intervals on Compuscore. You can choose the 90 percent option but the default is set at 68%.

  30. Validity • “The degree to which evidence and theory support the interpretation of test scores entailed by proposeed uses of tests” (APA Standards, 1999, p. 9) • Validity is the most fundamental consideration in evaluating and using tests.

  31. Validity • “A test that leads to valid inferences in general or about most students may not yield valid inferences about a specific student…First, unless a student has been systematically acculturated in the values, behavior, and knowledge found in the public culture of the United States, a test that assumes such cultural information is unlikely to lead to appropriate inferences about that student…

  32. Validity • Second, unless a student has been systematically instructed in the content of an achievement test, a test assuming such academic instruction is unlikely to lead to appropriate inferences about the student’s ability to profit from instruction. It would be inappropriate to administer a standardized test of written language (which counts misspelled words as errors) to a student who has been encouraged to use inventive spelling and reinforced for doing so. It is unlikely that the test results would lead to correct inferences about that student’s ability to provide from systematic instruction in spelling” (Salvia, Ysseldyke, & Bolt, 2009, p. 63.)

  33. Types of Validity • Content validity • Criterion-related validity • Construct validity

  34. Content Validity • A measure of the extent to which a test is an adequate measure of the content it is designed to cover; content validity is established by examining three factors: • Appropriate of type of items included • Comprehensiveness of item sample • The way in which the ietms assess the content • It is assessed by an overview of the items by trained individuals who make judgments about the relevancy of the items and the unambiguity of their formulation. • This is especially important in achievement testing and one under debate. • There is an emerging consensus that the methods used to assess student knowledge should closely parallel those used in instruction.

  35. Criterion-related Validity • The extent to which performance on a test predicts performance in a real-life situation. • Usually expressed as a correlation coefficient called a validity coefficient. • Two types of criterion-related validity: • Concurrent validity • Predictive validity

  36. Concurrent Validity • A measure of how accurately a person’s current test score can be used to estimate a score on a criterion measure. • We look to see if the test presents evidence of content validity and elicits test scores corresponding closely (correlating significantly) to judgments and scores from other achievement tests that are presumed to be valid, we can conclude that there is evidence for a test’s criterion-related validity.

  37. Predictive Criterion-related Validity • A measure of the extent to which a person’s current test scores can be used to estimate accurately what that person’s criterion scores will be at a later time. • Concurrent and predictive validity differ in the time at which scores on the criterion measure are obtained. • If we are developing a test to assess reading readiness, we can ask: Does knowledge of a student’s score on the reading test allow an accurate estimation of the student’s actual readiness for instruction? How do we know that our test really assesses reading readiness? • The first step is to find a valid criterion measure and if an assessment has content validity and corresponds to another measure, we can conclude the test is valid.

  38. Construct Validity • The extent to which a procedure or test measures a theoretical trait or characteristics. • Especially important for measures of process such as intelligence/cognition. • To provide evidence of construct validity, an author must rely on indirect evidence and inference. • To gauge construct validity a test develop accumulates evidence that the test acts in the way it would if it were a valid measure of a construct. • As the research evidence accumulates, the developer can make a stronger claim to construct validity.

  39. The Bottom Line… “Test users are expected to ensure that the test is appropriate for the specific students being assessed.” Salvia, Ysseldyke & Bolt, 2009, p. 71

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