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Research Study at University of Akron. Answer  Yes, we can with approximately equal reliability and accuracy. Question  How then do we combine these ratings?D*Bigger Question*Can we combine Angoff with Normative data?[>ProgramNew Angoff program from Assessment Systems Corporation Allows you to combine Normative estimates with other data Normative estimates are interesting in and of themselves in that they represent value judgments Ignoring normative judgments can get us into troubleZK.NThe following article appeared in the New York Post. The BIG 'F' ON EMT ADVANCEMENT EXAM (GINGER ADAMS OTIS, 4.19.2009) It was a massive medical failure for hundreds of FDNY medics who hoped to get promoted, as a measly eight out of 721 Emergency Medical Service workers passed the most recent lieutenant exam. The 1.1 percent pass rate for the 2008 test is about 38 percentage points lower than the last time the exam was given, in 2004, when 1,044 medics took the test and 409, or 39 percent, passed, The Post has learned. The poor results could leave the FDNY short on supervisors. "OPzL/Our Case Situation: You are the HR Director and have to set the cutoff on the new Police Sergeant Promotional Test. All promotions are internal and any police officer with more than two years of experience may take the test. The cutoff will be used to determine who passes or fails the test. Promotions from those who pass will be made based upon Seniority. There will be no rank ordering. The test is 100 items. It includes job knowledge questions, such as laws and detective work, and also police leadership and administration items. You expect that the average score on the test will be around 70%, but that is based upon past experience and you do not have data on expected scores. There will be at least 200 police officers taking the test. There are currently no police sergeant openings. However, you expect that during the life of the test, anywhere from 1 to 20 openings may have to be filled, depending upon retirements and other personnel events.PJ0RatingsThink of a minimally competent police sergeant applicant, what should be the minimal score they would receive on this test (Can be estimated from Angoff)? Given the type of applicant taking this type of test, what passing rate should be expected; that is, what percentage of the test takers should pass this test? Given the type of applicant taking this type of test, what is the maximum acceptable failure rate? That is, what is the highest percentage of failures that would be tolerated? Given the type of applicant taking this type of test, what is the minimum acceptable failure rate? That is, what is the lowest percentage of failures that would be tolerated? What is the highest cut score on this type of test that you think you could defend? That is, even if everyone who took the test passed, what is the highest or maximum possible cutoff score you think you could possibly set on this type of test? What is the lowest cut score on this type of test that you think you could defend? That is, even if everyone who took the test failed, what is the lowest or minimum possible cutoff score you think you could possibly set on this type of test?P`C Program Input _B\?My PointWe have long recognized that there is a difference between cutoff scores and critical scores, but typically have no easy method for incorporating value judgments and information on likely selection ratios.]@My PointHofstee and Beuk methods allow you to do that in a logical fashion. Program from Assessment Systems Corporation allows you to do that. We can collect these judgments and use them to set cutoff scores. Values of different stakeholders are of interest.^A#Stakeholders for Safety Forces Test$$(Mayor, City Manager and/or Safety Director City Council Chief of Safety Forces Civil Service Commission Non-minority Union Minority Union Court Supervision of Process Including Court Orders, Quotas, etc. Z Citizen Oversight Committee Internal Human Resources Staff Local Media Federal Agencies The Public Add to that Number of Applicants Number of Openings.pZ(Zp(M3Sensitivity ReviewsN1Sensitivity ReviewsA sensitivity review refers to "the process of having a diverse group of professionals review tests to flag material that may unintentionally interact with demographic characteristics of test takers (Sireci 2004, p. 22). Begins with ETS in 1980:``` O2Sensitivity ReviewsUtilizes trained expert judges to  identify and remove invalid aspects of test questions that might hinder people in various groups from performing at levels that allow appropriate inferences about their relevant knowledge and skills (ETS, 2003, p. 1).  ` P5%Why are Sensitivity Reviews Important&&(DMay be seen as required Improve our testing Increased use of CONTEXT)What Is Context?zTraditional items overemphasize memory The definition of murder is: Solution  Context Officer Jones is investigating a crime. Mary killed her husband using a knife in the library. She admits she did it but claims she was drunk at the time. Officer Jones should recommend to the prosecutor that Mary be charged with:L''*Advantages of ContextCorrelated with test-taking motivation Organizational attractiveness Provides realistic job preview Managers more satisfied Easier to defend in court But maybe not before appeals committees Uses richness of available media (Shotland et al 1998)RZ(Z7Z`(`!` +Face Validity ParadoxContext leads to Greater reading More of a g loading Potential stereotype threat Potential cultural bias and insensitivity Why is the murderer female? The police officer male?<k5k5Q4Sensitivity Reviews Seek ToeIdentify Culturally Inappropriate Content Identify Cultural Imbalance Avoid cueing stereotype threatf" fS6Research on Sensitivity Reviews (@We know almost nothing about sensitivity reviews Old days  just find someone in the office from various demographic groups Today  find culturally diverse SMEsT7ResearchmSubjective evaluations of bias do not tend to agree with objective data Could be a number of reasons for that&H&H&U8Current ResearchAnn Marie Ryan, Neal Schmitt, James Grand, Juliya Golubovich (Michigan State University) Candice Young, Dennis Doverspike, Gerald Barrett (University of Akron)%V9What Makes for a Good Reviewer? (Race, Ethnicity, Gender Racial or Cultural Identity Test Wiseness Motivation Subject Matter Knowledge Cognitive Ability Belief About Testing Training In What? BZ ZZ W:8What Do Reviewer s Look For?aDETSvGuideline 1. Treat people with respect. Guideline 2. Minimize the effects of construct-irrelevant knowledge or skills. Guideline 3. Avoid material that is unnecessarily controversial, inflammatory, offensive, or upsetting. Guideline 4. Use appropriate terminology to refer to people. Guideline 5. Avoid stereotypes. Guideline 6. Represent diversity in depictions of people. $vPPwY;Other Variables?Reading level SES G-Other QuestionsWhat makes for effective sensitivity reviews? Do you have guidelines? How do you choose reviewers? Are sensitivity reviews effective? Should gender/culture/etc. be Removed? Balanced? Overrepresented to combat stereotype threat?~.P5PAP@P.5"?Z<Sensitivity ReviewsNeed more research Practical work Training? Guidelines and practices? 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A locally developed test can be defined as a tailored or customized instrument based on local information (i.e., locally validated tests, includes tests such as job knowledge and work sample tests). How many of you develop this type of test  hope it is all of you or my talk may not mean much? In recent years, the use of such judgments appears to have been increasing. This increase has been a result of pressure from federal agencies (including the OFCCP and EEOC) for the greater quantification and documentation of procedures. This has led to increased use of questionnaires completed by SMEs, incumbents and supervisors. Thus, there has been a transfer in decision making from experts, us, me and you, to the mathematics of SMEs, incumbents and supervisors. And that is the general topic we will be discussing today. Now, how many of you use SMEs? Raise hands? As a side note, I have also asked that two papers be distributed to you. The first one is on job analysis for test development. That is a chapter that Winfred Arthur and I wrote for a new book on job analysis. I know you had a separate workshop on this here at MAPAC. I am not going to go over the material in the chapter, as you did have that separate workshop, but wanted to pass it out to you. We are going to be dealing with the more specific topic of SME judgments. Second, since some of you may be involved in public safety selection, I have passed out a paper on Fire and Police selection that was written by myself, Gerald Barrett, and Candace Young. We also will not be discussing that paper, but again, I wanted you to have it. This is a little like Oprah, you come for the talk and you get free gifts. H 4 0ηo~ ? 3380___PPT10. PX.  0 @>(  @^ @ S 0    @ c $009 3   4 In this talk, we will look at the questions we ask in making such judgments and whether we are asking the correct questions, especially in the context of cut off scores. In addition to a discussion of philosophical and psychological issues, we will also look at practical methods or forms for collecting judgments regarding items. In part, we will also be asking the question as to whether SMEs can really make these ratings. Surprisingly, or not surprisingly, we really don t know a lot about whether SMEs can make these ratings or under what are optimal conditions or training for such ratings. With all the research we do, and appears in journals, it is surprising we don t do more research on these topics. Or maybe it is not surprising, but there are different groups of researchers trying to make progress  although a lot of the research tends to be in Education rather than Adult Employment settings. H @ 0ηo~ ? 3380___PPT10.! Vw 0 91 H(  H^ H S 0   + H c $8Ƌ09 3l  TL___PPT9.& cBefore we get more into the topic. Lets look at some of the things we do know. Although be forewarned, like learning how sausage is made, a lot of the detail regarding research on such judgments is not that pretty. You might be better off not knowing. The mathematics we apply to SME judgments (especially to arrive at weights) are not technically permissible. That is, we frequently multiply together numbers which should not and can not be multiplied together. We treat our data as being measured on a ratio scale when at best it is measured on an interval or ordinal basis. So, we should not be multiplying the numbers together. We frequently come up with weights, where the weights really do not matter that much. That is, unit weights tend to give us the same outcomes as relative weights or judgment based weights. This is a good thing. SMEs can make reasonable estimates of item difficulty. However, SMEs cannot tell us how they make those judgments. Or maybe we just have not asked. P" " H H 0ηo~ ? 3380___PPT10.!@ 0 PH@P(  P^ P S 0   B P c $pՋ09 3   Beyond SMEs the assumptions that test takers make are important. Test takers tend to make some assumptions that are high unrealistic. So do civil service commissions, public policy people, and lawyers. Test Takers, and civil service commissions and public policy people, would like tests to have no error Test Takers, and civil service commissions and public policy people, would like items to have no error We know both of the above are impossibleH P 0ηo~ ? 3380___PPT10."@G@ 0 dP(  d^ d S 0    d c $09 3   FIn locally developed tests, job incumbents or SMEs may be asked to make a lot of ratings. These ratings include: Tasks  that would include Importance Time Spent KSAs, Competencies  that would include Criticality When Learned bq(q( H d 0ηo~ ? 3380___PPT10.0ۑt> 0   pN (  p^ p S 0     p c $09 3   D  As psychometricians, we know that tests have error and items have error. Tests have random error and items have specific content error (which we assume to be random over other items) and also random error. So, we tend to have lots of errors or mistakes when we make predictions. Even if our tests were a lot more perfect than they are, even if they were near perfect, we would still have lots of errors. An interesting question is whether we could we have items without errors. Yes we could, if you remember your testing or psychometrics class. There are items, Guttmann items, that are deterministic, have no error. But it turns out there are two problems with Guttmann type items 1 they are next to impossible to write such items, especially a whole test of them 2 they tend to give accurate estimates of ability only in a narrow range. They do not help us discriminate or rank order people. We are usually interested in not only knowing if someone has passes a test, but how they rank on that test compared to other people. Who scored highest? Who scored lowest? For answering the whole set of questions across the board, we are better off using items with error. So, why not just discriminate at point? The passing point? Because  we are interested in information at other points    H p 0ηo~ ? 3380___PPT10.it0 KCx(  x^ x S 0   s= x c $09 3  s As another side note, I think we should comment on the .70 level. At SIOP, we had a session on cutoff scores. It was a very good discussion and a lot of advances are being made on cutoff scores. One of the discussants criticized Angoff method for always coming out to .70; however there is a certain beauty in this. In a way, it is a good think that Angoff always comes out to .70, and suggests that there is some deeper meaning to all of this. H x 0ηo~ ? 3380___PPT10.c 0 J B (  ^  S 0   s<  c $P$s09 3  s ,In standard setting, we have some sort of specific definitions. Cutoff scores  score to get in or receive some treatment (Point at which a person Passes or Fails a test) Critical scores  minimum performance Certification standards  certify for a profession Psychologist Teacher In developing a locally validated test, perhaps the most difficult decisions are those regarding the appropriate cutoffs and weighting for each test. Cutoffs may be the most difficult decision. Other than the use of cognitive ability tests, no topic generates as much controversy as the setting of cutoffs. The establishment of appropriate cutoffs is likely to add another step or questionnaire to the job analysis. Although it is possible to set cutoffs based on statistical methods, in many cases the appropriate cutoff score is arrived at using a judgmental method (Cizek & Bunch, 2007), the most common being the Angoff method. `&3{&3{  H  0ηo~ ? 3380___PPT10.* Oo0 (  ^  S 0   s  c $t?s09 3  s uaIn using the Angoff method, expert judges are asked to provide ratings on individual items. The judges are instructed to imagine a minimally competent person and determine the percentage of minimally competent persons who would answer each item on a test correctly. Thus, for each test item, judges estimate the passing rate among minimally competent persons. Once all judges have completed their ratings, the ratings for each item are summed and averaged, creating an estimate of item difficulty for the minimally competent person. Based upon each item difficulty score, an overall test difficulty score is determined by the summed average of all test items. This score, expressed as a percentage, is then interpreted as the appropriate cutoff score for the test in terms of the percentage of items that must be answered correctly in order to pass the test. This is actually a simpler process than the above description might suggest and judges seem to be both able and willing to make such difficulty judgments. Judgmental methods to setting cutoffs require an additional questionnaire, usually completed by the SMEs. H  0ηo~ ? 3380___PPT10.*P"r0 (  ^  S 0   s  c $Hs09 3  s x Shotland et al. (1998) highlight five specific advantages to improving the face validity of a test. First, face validity has been shown to be positively correlated with test-taking motivation, which in turn has been reliably linked to greater test performance Second, face validity is positively related to organizational attractiveness. A test whose content is transparent and appears job relevant to respondents can act as a signal to the test taker that the employer is not attempting to hide the purpose of the testing instrument in any way. Third, face valid assessments can also serve as realistic job previews in the selection process. A fourth, often overlooked, advantage to using face valid assessment techniques is that managers within the organization typically report greater levels of comfort with and support for using more contextually relevant testing tools (Shotland et al., 1998). Finally, face valid tests are typically less susceptible to legal challenge and are often easier to defend should they be brought to court (Seymour, 1988).  ! `  H  0ηo~ ? 3380___PPT10.V"֝ 0 |(  ^  S 0   sv  c $tds09 3  s Other very important or critical ratings SMEs may be asked to make include Difficulty (cutoff scores  angoff method) Linkages (Forward and reverse) Cultural sensitivity Importance (for weighting) Correctness - Appeals 6LLH  0ηo~ ? 3380___PPT10.ZW50 .&0 (   ^   S 0   s    c $ys09 3  s Corresponds to a C or passing in Academic circles Public Sector  often used as a cutoff Item Writers  seem to sort of naturally write items that have p values around .70 Best point for discrimination, can be shown from theory and from data, that given the typical multiple choice test, around .70 is the optimal item difficulty level H   0ηo~ ? 3380___PPT10.[`[0 5-P(  ^  S 0   s'  c $p|s09 3  s The first type of SME judgment we are going to deal with is The setting of cutoffs. We may use any of several methods, but we tend to use a technique known as Angoff. H  0ηo~ ? 3380___PPT10.[@Հ0 `$(  ^  S 0   s  c $s09 3  s The problem is. Does Angoff answer the right question. Or the question we want answered. There are other questions we may want answered for example. Of the people who get the item right (or pass test), what percentage will be minimally competent? (Public - maximize) Of the people who get the item wrong (or fail test), what percentage will be minimally competent? (Plaintiff - minimize) Of course, again you can think back to your testing class and think, doesn t the Taylor Russell tables give me some of the answers. The answer of course is yes, if we already know the test cutoff, the performance cutoff and the validity of the test. H  0ηo~ ? 3380___PPT10.[P0 c[p(  ^  S 0   sU  c $s09 3  s Nevertheless, there is a certain danger in beauty, including the .70. As a result of the Ricci case I decided to concentrate more on cutoffs and the Angoff method. More of a speech and interactive idea session.H  0ηo~ ? 3380___PPT10.HBA10 `XQ(  X^ X S 0   s X c $Ps09 3  s G3Very important - but know very little about them H X 0ηo~ ? 3380___PPT10.V 40 xp(  X  C 0   p  S 09 3n  VN___PPT90( PIdentify content (e.g., wording, illustrations, situational contexts, etc.) that is sexist, racist, stereotypic, controversial, potentially offensive and upsetting, ethnocentric, or construct irrelevant stimuli the items included in that test appropriately reflect the diversity of gender, race and ethnicity represented in our society @" }}H  0ηo~ ? 3380___PPT10.@8r@@ Sg%j \lnqRCtv x`zy}; 0"$@G%/)@i#6;<@_EHg8O׮GJ/[=#R/[3Sſ'd0clTG# lXf1Oh+'0P `hx   MAPACDennis DoverspikeDennis Doverspike19Microsoft Office PowerPoint@0uc@`<ά@p<Ј G<g   p p &WMFClxu\ EMFUQh$F(GDICx!b $$==% % V0xx x % % $$AA" FGDICF(GDICl.wFGDICF(GDIC6ljwFGDICF(GDIC =FGDICRp@Arialy`ߖT00$Չ0$0(ٖ0@$@\dv%    T AALAssessing the Items: Using O  % ( Rp@Arial`ߖ[T00$`XՉ0$0(ٖ0@$@\dv%    T#AA!LSME judgments for Linkages,  % ( Rp@Arial`ߖvs0`ߖtlt0th@0(ٖ  D 0s DDt0(8@dv%    T#/AA-LSensitivity, and Cut Off Scores % ( F(GDICCdFGDICRp@Arial4`ߖArial$U(@ArialZ 0 ~O00ؖ0(ٖ@0`0@dv%    T8EhJAA8JLpMAPAC Presentation % ( Rp@Arial9`ߖArial$U(@ArialZ 0 <~O00ؖ0(ٖ@0`0@dv%    T4LlPAA4PLDennis Doverspike, PhD, ABPP % ( Rp@Arial$`ߖArial$U(@ArialZ 0 ~O00ؖ0(ٖ@0`0@dv%    T@RaVAA@VLtUniversity of Akron  % ( Rp@Arial`ߖArial$U(@ArialZ 0 } O00ؖ0(ٖ@ 0`0@dv%    TGXY\AAG\ L`Akron Ohio % (   x--$xx--'@Arialy??-. 02 Assessing the Items: Using P ."System-@Arial???-. 12 !SME judgments for Linkages, .-@Arial???-. 62 -Sensitivity, and Cut Off Scores.-@Arial4??-. "2 J8MAPAC Presentation.-@Arial9??-. 12 P4Dennis Doverspike, PhD, ABPP.-@Arial$??-. %2 V@University of Akron .-@Arial??-. 2 \G Akron Ohio.-՜.+,0$    ( On-screen Show Dennis Doverspike Consulting+Y1' 5Arial굴림宋体Default DesignXAssessing the Items: Using SME judgments for Linkages, Sensitivity, and Cut Off Scores Our Purpose SME Ratings SME RatingsThe Big QuestionWhat We Know - SMEs'What We Know – Test Taker Assumption What We Know What We Know: The Beauty of .70The Beauty of .70RicciCriticism of CutoffSIOPFirst Type of JudgmentStandard SettingAngoff Method Slide 17But Question We Want AnsweredOther Questions ResearchAnswerBigger QuestionProgram Slide 24 Slide 25RatingsProgram Input Slide 28 My Point My Point$Stakeholders for Safety Forces TestSensitivity ReviewsSensitivity ReviewsSensitivity Reviews&Why are Sensitivity Reviews ImportantWhat Is Context?Advantages of ContextFace Validity ParadoxSensitivity Reviews Seek To Research on Sensitivity Reviews ResearchCurrent Research What Makes for a Good Reviewer?What Do Reviewer’s Look For?ETSOther Variables?Other QuestionsSensitivity Reviews Thank You!  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