2. Introduction to Measurement

3. Goals of Workshop Reviewing assessment concepts Reviewing instruments used in norming process Getting an overview of the secondary and elementary normative samples Learning how to use the manuals in interpreting students scores.

4. ASSESSMENT The process of collecting data for the purpose of making decisions about students Its a process and typically involves multiple sources and methods. Assessment is in service of a goal or purpose. The data we collect will be used to support some type of decision (e.g., monitoring, intervention, placement)

5. Major Types of Assessment in Schools More frequently used: –Achievement: how well is child doing in curriculum? –Aptitude: what is this childs intellectual and other capabilities? –Behavior: Is the childs behavior affecting learning? Less frequently used: –Teacher competence: Is teacher actually imparting knowledge? –Classroom environment: Are classroom conditions conducive to learning? –Other concerns: home, community,...

6. Types of Tests Norm-referenced –Comparison of performance to a specified population/set of individuals Individually-referenced –Comparisons to self Criterion-referenced –Comparison of performance to mastery of a content area; what does the student know? The data in the manual will allow you to do look at norms and at individual growth.

7. MAJOR CONCEPTS Nomothetic and Idiographic Samples Norms Standardized Administration Reliability Validity

8. Nomothethic Relating to the abstract, the universal, the general. Nomothetic assessment focuses on the group as a unit. Refers to finding principles that are applicable on a broad level. For example, boys report higher math self- concepts than girls; girls report more depressive symptoms than boys..

9. Idiographic Relating to the concrete, the individual, the unique Idiographic assessment focuses on the individual student What type of phonemic awareness skills does Joe possess?

10. Populations and Samples I A population consists of all the representatives of a particular domain that you are interested in The domain could be people, behavior, curriculum (e.g. reading, math, spelling,...

11. Populations and Samples II A sample is a subgroup that you actually draw from the population of interest Ideally, you want your sample to represent your population –people polled or examined, test content, manifestations of behavior

12. Random Samples A sample in which each member of the population had an equal and independent chance of being selected. Random samples are important because the idea is to have a sample that represents the population fairly; an unbiased sample. A sample can be used to represent the population.

13. Probability Samples I Sampling in which elements are drawn according to some known probability structure. Random samples are subcases of probability samples. Probability samples are typically used in conjunction with subgroups (e.g., ethnicity, socioeconomic status, gender).

14. Probability Samples II Probability samples using subgroups are also referred to as stratified samples. Standardization samples are typically probability or stratified samples. Standardization samples need to represent population because the samples results will be used to create norms against which all members of population will be compared.

15. Norms I Norms are examples of how the average individual performs. Many of the tests and rating scales that are used to compare children in the US are norm-referenced. –An individual childs performance is compared to the norms established using a representative sample.

16. Norms II For the score on a normed instrument to be valid, the person being assessed must belong to the population for which the test was normed If we wish to apply the test to another group of people, we need to establish norms for the new group

17. Norms III To create new norms, we need to do a number of things: –Get a representative sample of new population –Administer the instrument to the sample in a standardized fashion. –Examine the reliability and validity of the instrument with that new sample –Determine how we are going to report on scores and create the appropriate tables

18. Standardized Administration All measurement has error. Standardized administration is one way to reduce error due to examiner/clinician effects. For example, consider these questions with different facial expressions and tone: Please define a noun for me :-) DEFINE a noun if you can ? :- (

19. Distributions Any group of scores can arranged in a distribution from highest to lowest 10, 3, 31, 100, 17, 4 3, 4, 10, 17, 31, 100

20. Normal Curve Many distributions of human traits form a normal curve Most cases cluster near middle, with fewer individuals at extremes; symmetrical We know how the population is distributed based on the normal curve

21. Ways of reporting scores Mean, standard deviation Distribution of scores –68.26% ± 1; 95.44 ± 2; 99.72 ±3 Stanines (1, 2, 3, 4, 5, 6, 7, 8, 9) Standard scores - linear transformations of scores, but easier to interpret Percentile ranks* Box and Whisker Plots*

22. Percentiles A way of reporting where a person falls on a distribution. The percentile rank of a score tells you how many people obtained a score equal to or lower than that score. So if we have a score at the 23rd %tile and another at the 69th %tile, which score is higher?

23. Percentiles 2 Is a high percentile always better than a low percentile? It depends on what you are measuring. For example…. Box and whisker plots are visual displays r graphic representation of the shape of a distribution using percentiles.

25. Correlation We need to understand the correlation coefficient to understand the manual The correlation coefficient, r, quantifies the relationship between two sets of scores. A correlation coefficient can have a range from -1 to + 1. –Zero means the two sets of scores are not related. –One means the two sets of scores are identical (a perfect correlation)

26. Correlation 2 Correlations can be positive or negative. A + correlation tells us that as one set of scores increases, the second set of scores also increases. they can be negative. Examples? A negative correlation tells us that as one set of scores increases, the other set decreases. Think of some examples of variables with negative rs. The absolute value of a correlation indicates the strength of the relationship. Thus.55 is equal in strength to -.55.

27. How would you describe the correlations shown by these charts?

28. Correlation 4.25,.70, -.40,.55, -.87,.58,.05 Order these from strongest to weakest -.87,.70,.58,.57, -.40,.25,.05 We will meet 3 different types of correlation coefficients today: Reliability coefficients - Definitions? Validity coefficients Pattern coefficients

29. Reliability Reliability addresses the stability, consistency, or reproducibility of scores. –Internal consistency –Split half, Cronbachs alpha –Test-retest –Parallel forms –Inter-rater

30. Reliability 2 Internal Consistency –How do the items on a scale relate to one another? Are respondents relating to them in the same way? Test-retest –How do respondents scores at Time 1 relate to their scores at Time 2?

31. Reliability 3 Parallel forms –Begin by creating at least two versions of the exam. How do respondents performance on one version compare to their performance on another version Inter-rater –Connected to ratings of behavior. How does one raters scores compare to anothers?

32. Validity Validity addresses the accuracy or truthfulness of scores. Are they measuring what we want them to? –Content –Criterion - Concurrent –Criterion - Predictive –Construct –Face

33. Content Validity Is the assessment tool representative of the domain (behavior, curriculum) being measured? An assessment tool is scrutinized for its (a) completeness or representativeness, (b) appropriateness, (c) format, and (d) bias –E.g., MSPAS

34. Criterion-related Validity What is the correlation between our instrument, scale, or test and another variable that measures the same thing, or measures something that is very close to ours? In concurrent validity, we compare scores on the instrument we are validating to scores on another variable that are obtained at the same time. In predictive validity, we compare scores on the instrument we are validating to scores on another variable that are obtained at some future time.

35. Structural Validity Used when an instrument has multiple scales. Asks the question, Which items go together best? For example, how would you group these items from the Self-Description Questionnaire? 3. I am hopeless in English classes. 5. Overall, I am no good. 7. I look forward to mathematics class. 15. I feel that my life is not very useful. 24. I get good marks in English. 28. I hate mathematics.

36. Structural Validity 2 We expect the English items (3, 24), Math items (7, 28) and global items (5, 15) to group together. The items that group together make up a new composite variable we call a factor. We want each item to correlate highly with the factor it clusters on, and less well with other factors. Typically, we accept item-factor coefficients from about.30 and higher.

37. What can we say about the structural validity of the SDQ given these scores? Item #VerbalMathGlobal 3.587-.044.624 5-.016.024.561 7.086.630-.059 23.019-.015.625 24.754-.006-.024 28-.020.750.042

38. Construct Validity Overarching construct: Is the instrument measuring what it is supposed to? –Dependent on reliability, content and criterion-related validity. We also look at some other types of validity evidence some times –Convergent validity: r with similar construct –Discriminant validity: r with unrelated construct –Structural validity: What is the structure of the scores on this instrument?

39. Statistical Significance When we examine group differences in science, we want to make objective rather than subjective decisions. We use statistics to let us know if the difference we are observing occurs by chance. In psychology, we typically set our alpha or error rate at 5% (i.e.,.05), and we conclude that if a difference was likely less than 5% of the time, that difference is statistically significant.

40. Statistical Significance 2 When our statistical test tells us that our difference is statistically significant (i.e., <.05). Statistical significance is affected by a number of variables, including sample size. The larger the sample, the easier it is to achieve statistical significance. We also look at the magnitude of the difference (or effect size). A difference may be statistically significant, but have a small effect size..10 to. 30 = small effect;.40 to.60 = medium effect; >.60 = large effect.