1 Class 9 Interpreting Pretest Data, Considerations in Modifying or Adapting Measures November 13, 2008 Anita L. Stewart Institute for Health & Aging University of California, San Francisco

2 Overview of Class 9 u Analyzing pretest data u Modifying/adapting measures u Keeping track of your study measures u Creating and testing scales in your sample

3 Summarize Data on Pretest Interviews u Summarize problems and nature of problems for each item u Determine how important problems are u Results become basis for possible revisions/adaptations

4 Methods of Analysis u Optimal: transcripts of all pretest interviews u For each item - summarize all problems u Analyze dialogue (narrative) for clues to solve problems

5 Behavioral Coding u Systematic approach to identifying problems with items –“interviewer” and “respondent” problems u Can code problems based on: –Standard administration –Responses to specific probes

6 Examples of Interviewer “Behaviors” Indicating Problem Items u Question misread or altered –Slight change – meaning not affected –Major change – alters meaning u Question skipped

7 Examples of Respondent “Behaviors” Indicating Problem Items u Asks for clarification or repeat of question u Did not understand question u Doesn’t know the answer u Qualified answer (e.g., it depends) u Indicates answer falls between existing response choices u Refusal

8 Summarize Behavioral Coding For Each Item u Proportion of interviews (respondents) with each problematic behavior u # of occurrences of problem divided by N –7/48 respondents requested clarification

9 Behavioral Coding Summary Sheet: Standard Administration (N=20) Item # Interviewer: difficulty reading Subject: asks to repeat Q Subject: asks for clarification 12/2001/ /202/20 401/200

10 Can Identify Problems Even When No Problem “Behaviors” Found u Respondents appear to answer question appropriately u Additional problems identified with probes –Probe on meaning: Response indicates lack of understanding –Probe on use of response options: Response indicates options are problematic

11 Behavioral Coding of Probe Results I asked you how often doctors asked you about your health beliefs. What does the term “health beliefs” mean to you? Behavioral coding: # times response indicated lack of understanding as intended –e.g., 2/15 respondents did not understand meaning based on response to probe

12 Behavioral Coding Summary: Standard Administration (N=20) + Probes (N=10) Item # Probe Meaning unclear Interviewer -difficulty reading Subject: asks to repeat Q Subject: asks for clarification 1 102/102/2001/ /151/203/202/ /200

13 Interpret Behavioral Coding Results u Determine if problems are common –Items with only a few problems may be fine u Quantifying “common” problems –several types of problems (many row entries) –several subjects experienced a problem »problem w/item identified in >15% of interviews

14 Continue Analyzing Items with “Common” Problems u Identify “serious” common problems –Gross misunderstanding of the question –Yields completely erroneous answer –Couldn’t answer the question at all u Some less serious problems can be addressed by improved instructions or a slight modification

15 Addressing More Serious Problems u Conduct content analysis of transcript –Use qualitative analysis software (e.g., NVIVO) u For these items: review dialogue that ensued during administration of item and probes –can reveal source of problems –can help in deciding whether to keep, modify or drop items

16 Results: Probing Meaning of Phrase u I asked you how often doctors asked you about your health beliefs? What does the term ‘health beliefs’ mean to you? “.. I don’t want medicine” “.. How I feel, if I was exercising…” “.. Like religion? --not believing in going to doctors?”

17 Results: Probing Meaning of a Phrase u What does the phrase “office staff” mean to you? “the receptionist and the nurses” “nurses and appointment people” “the person who takes your blood pressure and the clerk in the front office”

18 Results: Probing Meaning of Phrase u On about how many of the past 7 days did you eat foods that are high in fiber, like whole grains, raw fruits, and raw vegetables? –Probe: what does the term “high fiber” mean to you? u Behavioral coding of item –Over half of respondents exhibited a problem u Review answers to probe –Over ¼ did not understand the term Blixt S et al., Proceedings of section on survey research methods, American Statistical Association, 1993:1442.

19 Results: No Behavior Coding Issues but Probe Detected Problems u I seem to get sick a little easier than other people (definitely true, mostly true, mostly false, definitely false) u Behavioral coding of item –Very few problems u Review answers to probe –Almost 3/4 had comprehension problems –Most problems around term “mostly” (either its true or its not) Blixt S et al., Proceedings of section on survey research methods, American Statistical Association, 1993:1442.

20 Results: Beck Depression Inventory (BDI) and Literacy u Cognitive interviews: older adults, oncology pts, and less educated adults u Administered REALM (reading literacy test) and some selected BDI items u Asked to paraphrase items TL Sentell, Community Mental Health Journal, 2008;39:323

21 Results: Beck Depression Inventory (BDI) and Literacy (cont) u For each item, from 0-62% correctly paraphrased item u Most misunderstandings: vocabulary confusion u Phrase: I am critical of myself for my weaknesses and mistakes –“Critical is when you’re very sick” –“I don’t know how to explain mistakes”

22 Interpreting Pretest Results of Self- Administered Questionnaires u Missing data is a clue to problematic items –More missing data associated with unclear, difficult, or irrelevant items –Cognitive interviewing can help determine reasons for missing data

23 How Missing Data Prevalence Helps u Items with large percent of responses missing – clue to problem u In H-CAHPS ® pretest: Did hospital staff talk with you about whether you would have the help you needed when you left the hospital? –35% missing for Spanish group –29% missing for English group MP Hurtado et al. Health Serv Res, 2005;40-6, Part II:

24 Exploring Differences by Diverse Groups u Back to issue of “equivalence” of meaning across groups u All cognitive interview analyses can be done separately by group

25 Results: Use of Response Scale u Do diverse groups use the response scale in similar ways? u Re questions about cultural competence of providers –Interviewers reported that Asian respondents who were completely satisfied did not like to use the highest score on the rating scale California Pan-Ethnic Health Network (CPEHN) Report, 2001

26 Results: Use of Response Scale (cont) u Behavioral Risk Factor Surveillance Survey (BRFSS) pretesting u Found that Puerto Rican, Mexican American, and African American respondents more likely to choose extreme response categories than Whites. RB Warnecke et al, Ann Epidemiol, 1997:7:

27 Differential Use of CAHPS® 0-10 Global Rating Scale u Compared Medicaid and commercially insured adults on use of scale u Medicaid enrollees more likely than commercial participants to use extreme ends of scale –All other things being equal PC Damiano et al, Health Serv Outcomes Res Method, 2004:5:

28 Results: Probe on Difficulty: CES-D Item “During the past week, how often have you felt that you could not shake off the blues, even with help from family and friends” u Probe: Do you feel this is a question that people would or would not have difficulty understanding? –Latinos more likely than other groups to report people would have difficulty TP Johnson, Health Survey Research Methods, 1996

29 Overview of Class 9 u Analyzing pretest data u Modifying/adapting measures u Keeping track of your study measures u Creating and testing scales in your sample

30 Now What! u Issues in adapting measures based on pretest results u Cognitive interview pretesting during development phases of measure –Can modify items and continue pretesting u Cognitive interview pretesting prior to using published survey: –More problematic

31 Modification: Probing the Meaning of a Phrase u What does the phrase “office staff” mean to you? “the receptionist and the nurses” “nurses and appointment people” “the person who takes your blood pressure and the clerk in the front office” u We changed the question to receptionist and appointment staff

32 Results: Probing Meaning and Cultural Appropriateness u I asked you how often doctors asked you about your health beliefs? What does the term ‘health beliefs’ mean to you? “.. I don’t want medicine” “.. How I feel, if I was exercising…” “.. Like religion? --not believing in going to doctors?” u We changed the question to “personal beliefs about your health

33 Criteria for Whether or Not to Modify Measure u Contact author –May be open to modifications, working with you u Be sure your opinion is based on extensive pretests with consistent problems –Don’t rely on a few comments in a small pretest u Work with a measurement specialist to assure that proposed modifications are likely to solve problem

34 Tradeoffs of Using Adapted Measures Advantages u Improve internal validity Disadvantages u Lose external validity u Know less about modified measure u Need to defend new measure

35 Adding New (Modified) Items u One approach if you find serious problems with a standard measure –Write new items you think will be better (use same format) –Retain original intact items and add modified items u Can test original scale and revised scale with modified items instead of original items

36 Modifying response categories u If response choices are too few and/or coarse, can improve without compromising too much –Try adding levels within existing response scale

37 One Modification: Too Many Response Choices SF36 version 1 u 1 - All of the time u 2 - Most of the time u 3 - A good bit of the time u 4 - Some of the time u 5 - A little of the time u 6 - None of the time SF36 version 2 u 1 - All of the time u 2 - Most of the time u 3 - Some of the time u 4 - A little of the time u 5 - None of the time

38 Modification of Health Perceptions Response Choices for Thai Translation Usual responses: u 1 - Definitely true u 2 - Mostly true u 3 - Don’t know u 4 - Mostly false u 5 - Definitely false Modified: u 1 – Not at all true u 2 – A little true u 3 - Somewhat true u 4 - Mostly true u 5 – Definitely true e.g., My health is excellent, I expect my health to get worse

39 Modifying Item Stems u If item wording will not be clear to your population –Can add parenthetical phrases u Have you ever been told by a doctor that you have diabetes (high blood sugar)?

40 Strategy for Modified Measures u Test measure in original and adapted form u Choose measure that performs the best

41 Analyzing New (Modified) Measure u Factor analysis –All original items –Original plus new items replacing original u Correlations with other variables –Does the new measure detect stronger associations? u Outcome measure –Does the new measure detect more change over time?

42 Analytic Strategy: CAHPS® 0-10 Global Rating Scale: Response Usual classifications u 0-9, 10 (dichotomy) Proposed classification u 0-8, 9-10 PC Damiano et al, Health Serv Outcomes Res Method, 2004:5: Can’t change the scale – part of standardized survey

43 Overview of Class 9 u Analyzing pretest data u Modifying/adapting measures u Keeping track of your study measures u Creating and testing scales in your sample

44 Questionnaire Guides u Organizing your survey measures –Keep track of measurement decisions u Sample guide to measures (last week) –Documents sources of measures –Any modifications, reason for modification

45 “Sample Guide to Measures” Handout u Type of variable u Concept u Measure u Data source u Number of items/survey question numbers u Number of scores or scales for each measure u References

46 Sample “Summary of Survey Variables..” Handout u Develop “codebook” of scoring rules u Several purposes –Variable list –Meaning of scores (direction of high score) –Special coding –How missing data handled –Type of variable (helps in analyses)

47 Item Naming Conventions u Optimal coding is to assign raw items their questionnaire number –Can always link back to questionnaire easily u Some people assign a variable name to the questionnaire item –This will drive you crazy

48 Variable Naming Conventions u Assigning variable names is an important step –make them as meaningful as possible –plan them for all questionnaires at the beginning u For study with more than one source of data, a suffix can indicate which point in time and which questionnaire –B for baseline, 6 for 6-month, Y for one year –M for medical history, L for lab tests

49 Variable Naming Conventions (cont) Medical History Questionnaire HYPERTMB HYPERTM6 Baseline 6 months

50 Variable Naming Conventions (cont) u A prefix can help sort variable groupings alphabetically –e.g., S for symptoms SPAINB, SFATIGB, SSOBB

51 Overview of Class 9 u Analyzing pretest data u Modifying/adapting measures u Keeping track of your study measures u Creating and testing scales in your sample

52 On to Your Field Test or Study u What to do once you have your baseline data u How to create summated scale scores

53 Preparing Surveys for Data Entry: 4 Steps u Review surveys for data quality u Reclaim missing and ambiguous data u Address ambiguities in the questionnaire prior to data entry u Code open-ended items

54 Review Surveys for Data Quality u Examine each survey in detail as soon as it is returned, and mark any.. –Missing data –Inconsistent or ambiguous answers –Skip patterns that were not followed

55 Reclaim Missing and Ambiguous Data u Go over problems with respondent –If survey returned in person, review then –If mailed, call respondent ASAP, go over missing and ambiguous answers –If you cannot reach by telephone, make a copy for your files and mail back the survey with request to clarify missing data

56 Address Ambiguities in the Questionnaire Prior to Data Entry u When two choices are circled for one question, randomly choose one (flip a coin) u Clarify entries that might not be clear to data entry person

57 Code Open-Ended Items u Open-ended responses have no numeric code –e.g., name of physician, reason for visiting physician u Goal of coding open-ended items –create meaningful categories from variety of responses –minimize number of categories for better interpretability –Assign a numeric score for data entry

58 Example of Open-Ended Responses 1.What things do you think are important for doctors at this clinic to do to give you high quality care? u Listen to your patients more often u Pay more attention to the patient u Not to wait so long u Be more caring toward the patient u Not to have so many people at one time u Spend more time with the patients u Be more understanding

59 Process of Coding Open-Ended Data u Develop classification scheme –Review responses from 25 or more questionnaires –Begin a classification scheme –Assign unique numeric codes to each category –Maintain a list of codes and the verbatim answers for each –Add new codes as new responses are identified u If a response cannot be classified, assign a unique code and address it later

60 Example of Open-Ended Codes Communication = 1 u Listen to your patients more often = 1 u Pay more attention to the patient = 1 Access to care = 2 u Not to wait so long = 2 u Not to have so many people at one time = 2 Allow more time = 3 u Spend more time with the patients = 3 Emotional Support = 4 u Be more understanding = 4 u Be more caring toward the patient

61 Verify Assigned Codes u Have a second person independently classify each response using final codes u Investigator can review a small subset of questionnaires to assure that coding assignment criteria are clear and are being followed

62 Reliability of Open-Ended Codes u Depends on quality of question, of codes assigned, and the training and supervision of coders u Initial coder and second coder should be concordant in over 90% of cases

63 Data Entry u Set up file u Double entry of about 10% of surveys –SAS or SPSS will compare two for accuracy »Acceptable 0-5% error »If 6% or greater – consider re-entering data

64 Print Frequencies of Each Item and Review: Range Checks u Verify that responses for each item are within acceptable range –Out of range values can be checked on original questionnaire »corrected or considered “missing” –Sometimes out of range values mean that an item has been entered in the wrong column »a check on data entry quality

65 Print Frequencies of Each Item and Review: Consistency Checking u Determine that skip patterns were followed u Number of responses within a skip pattern need to equal number who answered “skip in” question appropriately

66 Print Frequencies of Each Item and Review: Consistency Checking (N=90) 1. Did your doctor prescribe any medications? (75 = yes, 15 = no) 1a. If yes, did your doctor explain the side effects of the medication? (80 responses) u Often will find that more people answered the second question than were supposed to

67 Print Frequencies of Each Item and Review: Consistency Checking (cont.) u Go back to a questionnaires of those with problems –check whether initial “filter” item was incorrectly answered or whether respondent inadvertently answered subset –sometimes you won’t know which was correct u Hopefully this was caught during initial review of questionnaire and corrected by asking respondent

68 Deriving Scale Scores u Create scores with computer algorithms in SAS, SPSS, or other program u Review scores to detect programming errors u Revise computer algorithms as needed u Review final scores

69 Creating Likert Scale Scores u Translate codebook scoring rules into program code (SAS, SPSS): –Reverse all items as specified –Apply scoring rules –Apply missing data rules u Sample for SAS (see handout)

70 Testing Scaling Properties and Reliability in Your Sample for Multi-Item Scales u Obtain item-scale correlations –Part of internal consistency reliability program u Calculate reliability in your sample (regardless of known reliability in other studies) –internal-consistency for multi-item scales –test-retest if you obtained it

71 SAS – Chapter 3: Assessing Reliability with Coefficient Alpha u Review statements and output u How to test your scales for internal consistency and appropriate item-scale correlations

72 SAS/SPSS Both Make Item Convergence Analysis Easy u Reliability programs provide: –Item-scale correlations corrected for overlap –Internal consistency reliability (coefficient alpha) –Reliability with each item removed »To see effect of removing an item

73 SAS – Obtaining Item-Scale Correlations and Coefficient Alpha u PROC CORR –DATA=data-set-name –ALPHA –NOMISS –VAR (list of variables) u Output: –Coefficient alpha –Item correlations –Item-scale correlations corrected for overlap SAS Manual, Chapter 3: Assessing Scale Reliability with Coefficient Alpha

74 SAS – Chapter 3: Assessing Scale Reliability with Coefficient Alpha u PROC CORR –DATA=data-set-name –ALPHA –NOMISS –VAR (list of variables) u Output: –Coefficient alpha –Item correlations –Item-scale correlations corrected for overlap

75 Testing Reliability in STATA u Alpha varlist [if] [in] [, options] SEE HANDOUT

76 What if Reliability is Too Low? u Have to decide if you need to modify a scale u New scales under development –Modify using item-scale criteria u Standard scales – cannot change –Simply report problems as caveats in your analyses u If problem is substantial –Can create a modified scale and report results using standard and modified scale

77 Value of Pretesting: Experts Say.. u …evidence from our work suggests that many survey questions are seriously underevaluated u Evaluating items at final pretest phase is often too late in the process –Too late for extensive question redesign u A series of question evaluation steps is needed beginning well before the survey FJ Fowler and CF Cannell. Using behavioral coding to identify problems with survey questions. In Answering Questions…, eds N Schwarz et al, Jossey-Bass, 1996

78 Homework for Class 10 u Conduct 2 pretest interviews with individuals similar to your target population –Administer all questions –Administer your 4 probes u Summarize briefly your pretest results u Indicate whether the measure appears to be appropriate for the 2 pretest subjects –No inferences to broader sample needed.