Development of health measurement scales – part 2


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Development of health measurement scales – part 2

  1. 1. Development of health measurement scales – part II Dr. Rizwan S A, M.D. If you cannot express in numbers something that you are describing, you probably have little knowledge about it. 1
  2. 2. Scaling responses • Categorical • Continuous – Direct Estimation Method • Visual Analogue Scale • Adjectival Scale – Discrete – Continuous • Specific Scaling – Likert Scale – Semantic Scale – Comparative Method • Thurstone’s Method • Paired Comparision Method • Guttmann Method – Econometric Method 2
  3. 3. Outline • • • • • Reliability Validity Measuring change Conclusions Article discussion 3
  4. 4. Variance = sum of (individual value – mean value)2 ---------------------------------------------------------------------------------- no. of values 4
  5. 5. Reliability • Whether our tool is measuring the attribute in a reproducible fashion or not • A way to show the amount of error (random and systematic) in any measurement • Sources of error – observers, instruments, instability of the attribute • Day to day encounters – Weighing machine, watch, thermometer 5
  6. 6. Assessing Reliability • Internal Consistency – The average correlation among all the items in the tool • Item-total correlation • Split half reliability • Kuder-Richardson 20 & Cronbach‘s alpha • Multifactor inventories • Stability – Reproducibility of a measure on different occasions • Inter-Observer reliability • Test-Retest reliability (Intra-Observer reliability) 6
  7. 7. Internal consistency • All items in a scale tap different aspects of the same attribute and not different traits • Items should be moderately corr. with each other and each item with the total • Two schools of thought – If the aim is to describe a trait/behaviour/disorder – If the aim is to discriminate people with the trait from those without • The trend is towards scales that are more internally consistent • IC doesn‘t apply to multidimensional scales 7
  8. 8. Item-total correlation • Oldest, still used • Correlation of each item with the total score w/o that item • For k number of items, we have to calculate k number of correlations, labourious • Item should be discarded if r < 0.20 • Best is Pearson‘s R, in case of dichotomous items point-biserial correlation 8
  9. 9. Split half reliability • Divide the items into two halves and calculate corr. between them • Underestimates the true reliability because we are reducing the length of scale to half (r is directly related to the no. of items) – Corrected by Spearman-Brown formula • Should not be used in – Highly timed achievement tests – Chained items 10
  10. 10. KR 20/Cronbach‘s alfa • KR-20 for dichotomous responses • Cronbach‘s alfa for more than two responses • They give the average of all possible split half reliabilities of a scale • If removing an item increases the coeff. it should be discarded • Problems – Depends on the no. of items – A scale with two different sub-scales will prob. yield high alfa – Very high alfa denotes redundancy (asking the same question in slightly different ways) – Thus alfa should be more than 0.70 but not more than 0.90 11
  11. 11. • Cronbach‘s basic equation for alpha n n 1 1 Vi Vtest – n = number of questions – Vi = variance of scores on each question – Vtest = total variance of overall scores on the entire test 13
  12. 12. Calculation of Cronbach‘s coefficient alpha Example: Assessment of emotional health During the past month: Have you been a very nervous person? Yes No 1 0 Have you felt downhearted and blue? 1 0 Have you felt so down in the dumps that nothing could cheer you up? 1 0 14
  13. 13. Results Patient Item 1 Item 2 Item 3 Summed scale score 1 0 1 1 2 2 1 1 1 3 3 0 0 0 0 4 1 1 1 3 5 1 1 0 2 Percentage positive mean score 3/5=.6 4/5=.8 3/5=.6 = 2 15
  14. 14. Calculations Mean score = 2 Sample variance = CC alpha 1 (2 2) 2 (3 2) 2 (0 (5 1 (. 8 )(. 2 ) 1 .5 (. 6 )(. 4 ) (3 2) 2 (2 2) 2 1 .5 k k 3 2 1) (% pos ) i (% neg ) i Var (. 6 )(. 4 ) 2) 1 0 . 86 2 Conclude that this scale has good reliability 16
  15. 15. Multifactor inventories • More sophisticated techniques • Item-total procedure – each item should correlate with the total of its scale and the total of all the scales • Factor analysis – Determining the underlying factors – For eg., if there are five tests • Vocabulary, fluency, phonetics, reasoning and arithmetic • We can theorize that the first three would be correlated under a factor called ‗verbal factor‘ and the last two under ‗logic factor‘ 20
  16. 16. Stability/ Measuring error • A weighing machine shows weight in the range of say 40-80 kg and thus an error of 1kg is meaningful • A ratio will be more useful, measurement error / total variability between subjects But in reality we calculate the ratio variability between subjects / total variability (Total variability includes subjects and measurement error) • So that a ratio of – 1 indicates no measurement error/perfect reliability – 0 indicates otherwise 21
  17. 17. • Reliability = subj. variability / (subj. variability + measurement error) • Statistically ‗variance‘ is the measure of variability so, • Reliability = SD2 of subjects / (SD2 of subjects + SD2 of error) • Thus reliability is the proportion of the total variance that is due to the ‗true‘ differences between the subjects • Reliability has meaning only when applied to specific populations 22
  18. 18. 1 • Measurement error/ total variability between subjects 2 • Variability between subjects/ total variability 3 • Subj. variability/ (subj. variability + measurement error) 4 • SD2 of subjects/ (SD2 of subjects + SD2 of error) 23
  19. 19. Calculation of reliability • The statistical technique used is ANOVA and since we have repeated measurements in reliability, the method is – repeated measures ANOVA 24
  20. 20. 25
  21. 21. Example 26
  22. 22. 27
  23. 23. • Classical definition of reliability • Interpretation is that 88% of the variance is due to the true variance among patients (aka Intraclass Correlation Coefficient, ICC) 28
  24. 24. Fixed/random factor • What happened to the variance due to observers? • Are these the same observers going to be used or they are a random sample? • Other situations where observations may be treated as fixed is subjects answering ‗same items on a scale‘ 29
  25. 25. Other types of reliability • We have only examined the effect of different observers on the same behaviour • But there can be error due to ‗day to day‘ differences, if we measure the same behaviour a week or two apart we can calculate ‗intra-observer reliability coefficient‘ • If there are no observers (self-rated tests) we can still calculate ‗test-retest reliability‘ 31
  26. 26. • Usually high inter-observer is sufficient, but if it is low then we may have to calculate intra-observer reliability to determine the source of unreliability • Mostly measures of internal consistency are reported as ‗reliability‘, because there are easily computed in a single sitting – Hence caution is required as they may not measure variability due to day to day differences 32
  27. 27. Diff. forms of reliability coefficient • So far we have seen forms of ICC • Others – Pearson product-moment correlation – Cohen‘s kappa – Bland – altman analysis 33
  28. 28. Pearson‘s correlation • Based on regression – the extent to which the relation between two variables can be described by straight line 34
  29. 29. Limitations of Pearson‘s R • A perfect fit of 1.0 may be obtained even if the intercept is non-zero and the slope is not equal to one unlike with ICC • So, Pearson‘s R will be higher than truth, but in practice it is usually equal to ICC as the predominant source of error is random variation • If there are multiple observations then multiple pairwise Rs are required, unlike the single ICC • For eg. with 10 observers there will be 45 Pearson‘s Rs whereas only one ICC 35
  30. 30. Kappa coeff. 36
  31. 31. 0.70-0.41/1-0.41 = 0.491 37
  32. 32. 38
  33. 33. • Used when responses are dichotomous/categorical • When the frequency of positive results is very low or high, kappa will be very high • Weighted kappa focuses on disagreement, cells are weighted according to the distance from the diagonal of agreement • Weighting can be arbitrary or using quadratic weights (based on square of the amount of discrepancy) • Quadratic scheme of weighted kappa is equivalent to ICC • Also, the unweighted kappa is equal to ICC based on ANOVA 39
  34. 34. Bland and Altman method • A plot of difference between two observations against the mean of the two observations 40
  35. 35. 41
  36. 36. • The mean diff. is related to observer variance in ICC, and the SD of differences if related to the error variance in ICC • Limits of agreement are calculated as mean difference the error variance) 2 SE (= to • Agreement is expressed as the ‘limits of agreement’. The presentation of the 95% limits of agreement is for visual judgement of how well two methods of measurement agree. The smaller the range between these two limits the better the agreement is. • The question of how small is small depends on the clinical context: would a difference between measurement methods as extreme as that described by the 95% limits of agreement meaningfully affect the interpretation of the results • Limitation - the onus is placed on the reader to juxtapose the calculated error against some implicit notion of true variability 42
  37. 37. Issues in Interpretation SE of measurement and reliability • R is a dimension-less ratio of variances and so it is difficult to interpret R in terms of an individual score • SEM = σ sqrt(1-R) • If we knew the true score of someone, we can estimate the limits within which 68% or 95% of the times the observed value would lie • Eg. A scale with SD 10 and R 0.8. If the true score was 15, we can say 68% of the time his observed value will fall between 10.5 to 19.5 43
  38. 38. Standards for magnitude of reliability coeff. • How much reliability is good? Kelly (0.94) Stewart (0.85) • A test for individual judgment should be higher than that for research in groups • For Research purposes – – Mean score and the sample size will reduce the error – Conclusions are usually made after a series of studies – Acceptable reliability is dependent on the sample size in research 45
  39. 39. Reliability and probability of misclassification • Depends on the property of the instrument and the decision of cut point • Relation between reliability and likelihood of misclassification – Eg. A sample of 100, one person ranked 25th and another 50th – If the R is 0, 50% chance that the two will reverse order on retesting – If R is 0.5, 37% chance, with R=0.8, 2.2% chance • Hence R of 0.75 is minimum requirement for a useful instrument 46
  40. 40. Improving reliability • Increase the subject variance relative to the error variance (by legitimate means and otherwise) • Reducing error variance – Observer/rater training – Removing consistently extreme observers – Designing better scales • Increasing true variance – In case of ‗floor‘ or ‗ceiling‘ effect, introduce items that will bring the performance to the middle of the scale (thus increasing true variance) • Eg. Fair-good-very good-excellent (instead of bad-good) 47
  41. 41. • Ways that are not legitimate – Test the scale in a heterogeneous population (normal and bedridden arthritics) – A scale developed in homogeneous population will have a larger reliability when used in a heterogeneous population • correct for attenuation 48
  42. 42. • Simplest way to increase R is to increase the no. of items • True variance increases as the square of items whereas error variance increases only as the no. of items • If the length of the test is triples – Then Rspearman brown = 3R/ 1 + 2R 49
  43. 43. • In reality the equation overestimates the new reliability • We can also use this equation to determine the length of a test for achieving a pre-decided reliability • To improve test-retest reliability – shorten the interval between the tests • An ideal approach is the examine all the sources of variation and try to reduce the larger ones (generalizability theory) 50
  44. 44. Sample size for reliability studies 51
  45. 45. Summary for Reliability • Pearson R is theoretically incorrect but in practice fairly close • Bland and Altman method is analogous to error variance of ICC but doesn‘t relate this to the range of observations • kappa and ICC are identical and most appropriate 52
  46. 46. Generalizability theory • Backdrop of classical test theory – All variance in scores can be divided into true and error variance (overtly simplistic assumption) – Don‘t exhaust all possible sources of variance – Doesn‘t account for interaction between sources of error variance • G theory – Cronbach et al 1972 – Essence is the recognition that in any measurement situation there are multiple sources of error variance (may be infinite) 53
  47. 47. Reliability Vs. Validity 60
  48. 48. Validity • Two steps to determine usefulness of a scale – Reliability – necessary but not sufficient – Validity – next step • Validity – is the test measuring what it is meant to measure? • Two important issues – The nature of the what is being measured • Temperature Vs. quality of life/social support (physical vs. abstract) – Relation to the purported cause • Sr. creatinine is a measure of kidney func. because we know it is regulated by the kidneys • But whether students who do volunteer work will become better doctors? • Since our understanding of human behaviour is far from perfect, such predictions have to validated against actual performance 61
  49. 49. 32 degree Celsius Depression score - 32 62
  50. 50. Types of validity • Three Cs (conventionally) – Content – Criterion • Concurrent • Predictive – Construct – Others (face validity) • New types – Convergent, discriminant, trait etc., 63
  51. 51. Differing perspectives • Previously validity was seen as demonstrating the properties of the scale • Current thinking - what inferences can be made about the people that have given rise to the scores on these scales? – Thus validation is a process of hypothesis testing (someone who scores on test A, will do worse in test B, and will differ from people who do better in test C and D) – Researchers are only limited by their imagination to devise experiments to test such hypotheses • All types of validity are addressing the same issue of the degree of confidence we can place in the inferences we can draw from the scales 64
  52. 52. • Face validity – On the face of it the tool appears to be measuring what it is supposed to measure – Subjective judgment by one/more experts, rarely by empirical means • Content validity – Measures whether the tool includes all relevant domains or not – Closely related to face validity – aka. ‗validity by assumption‘ because an expert says so • Certain situations where these may not be desired - ? 65
  53. 53. Content validity • Example – cardiology exam; – Assume it contains all aspects of the circulatory system (physiology, anatomy, pathology, pharmacology etc., etc.,) – If a person scores high on this test, we can say ‗infer‘ that he knows much about the subject (i.e., our inferences about the person will right across various situations) – In contrast, if the exam did not contain anything about circulation, the inferences we make about a high scorer may be wrong most of the time and vice versa 66
  54. 54. • Generally, a measure that includes a more representative sample of the target behaviour will have more content validity and hence lead to more accurate inferences • Reliability places an upper limit on validity (the maximum validity is the square root of reliability coeff.) the higher the reliability the higher the maximum possible validity – One exception is that between internal consistency and validity (better to sacrifice IC to content validity) – The ultimate aim of scale is inferential which depends more on content validity than internal consistency 67
  55. 55. Criterion validity • Correlation of a scale to an accepted ‗gold standard‘ • Two types – Concurrent (both the new scale and standard scale are given at the same time) – Predictive – the GS results will be available some time in the future (eg. Entrance test for college admission to assess if a person will graduate or not) • Why develop a new scale when we already have a criterion scale? – Diagnostic utility/substitutability – Predictive utility (no decision can be made on the basis of new scale) • Criterion contamination – If the result of the GS is in part determined in some way by the results of the new test, it may lead to an artificially high correlation 68
  56. 56. Construct validity • Height, weight – readily observable • Psychological - anxiety, pain, intelligence are abstract variables and can‘t be directly observed • For eg. Anxiety – we say that a person has anxiety if he has sweaty palms, tachycardia, pacing back and forth, difficulty in concentrating etc., (i.e., we have a hypothesize that these symptoms are the result of anxiety) • Such proposed underlying factors are called hypothetical constructs/ constructs (eg. Anxiety, illness behaviour) • Such constructs arise from larger theories/ clinical observations • Most psychological instruments tap some aspect of construct 69
  57. 57. Early morning stiffness 3 or more joints involved esp., small joints Rheumatoid arthritis Elevated ESR, RA factor X rays changes 70
  58. 58. Establishing construct validity • IBS is a construct rather than a disease – it is a diagnosis of exclusion • A large vocabulary, wide knowledge and problem solving skills – what is the underlying construct? • Many clinical syndromes are constructs rather than actual entities (schizophrenia, SLE) 71
  59. 59. • Initial scales for IBS – ruling out other organic diseases and some physical signs and symptoms – These scales were inadequate because they lead to many missed and wrong diagnoses – New scales developed incorporating demographical features and personality features • Now how to assess the validity of this new scale – Based on my theory, high scorers on this scale should have • Symptoms which will not clear with conventional therapy • Lower prevalence of organic bowel disease on autopsy 72
  60. 60. Differences form other types 1. Content and criterion can be established in one or two studies, but there is no single experiment that can prove a construct • Construct validation is an ongoing process, learning more about the construct, making new predictions and then testing them • Each supportive study strengthens the construct but one well designed negative study can question the entire construct 2. We are assessing the theory as well as the measure at the same time 73
  61. 61. IBS example • We had predicted that IBS patients will not respond to conventional therapy • Assume that we gave the test to a sample of patients with GI symptoms and treated them with conventional therapy • If high scoring patients responded in the same proportion as low scorers then there are 3 possibilities – Our scale is good but theory wrong – Our theory is good but scale bad – Both scale and theory are bad • We can identify the reason only from further studies 74
  62. 62. • If an experimental design is used to test the construct, then in addition to the above possibilities our experiment may be flawed • Ultimately, construct validity doesn‘t differ conceptually from other types of validity – All validity is at its base some form of construct validity… it is the basic meaning of validity – (Guion) 75
  63. 63. Establishing construct validity • Extreme groups • Convergent and discriminant validity • Multitrait-multimethod matrix 76
  64. 64. Extreme groups • Two groups – as decided by clinicians – One IBS and the other some other GI disease – Equivocal diagnosis eliminated • Two problems – That we are able to separate two extreme groups implies that we already have a tool which meets our needs (however we can do bootstrapping) – This is not sufficient, the real use of a scale is making much finer discriminations. But such studies can be a first step, if the scale fails this it will be probably useless in practical situations 77
  65. 65. • Convergent validity - If there are two measures for the same construct, then they should correlate with each other but should not correlate too much. E.g. Index of anxiety and ANS awareness index • Divergent validity – the measure should not correlate with a measure of a different construct, eg. Anxiety index and intelligence index 78
  66. 66. Multitrait-multimethod matrix • Two unrelated traits/constructs each measured by two different methods • Eg. Two traits – anxiety, intelligence; two methods – a rater, exam Anxiety Rater Anxiety Intelligence – – – – Exam Intelligence Rater Rater 0.53 Exam 0.42 0.79 Rater 0.18 0.17 0.58 Exam 0.15 0.23 0.49 Exam 0.88 Purple – reliabilities of the four instruments (sh be highest) Blue – homotrait heteromethod corr. (convergent validity) Yellow – heterotrait homomethod corr. (divergent validity) Red – heterotrait heteromethod corr. (sh be lowest) • Very powerful method but very difficult to get such a combination 79
  67. 67. Biases in validity assessment • Restriction in range • May be in new scale (MAO level) • May be in criterion (depression score) • A third variable correlated to both (severity) • Eg. A high correlation was found between MAO levels and depression score in community based study, but on replicating the study in hospital the correlation was low 80
  68. 68. 81
  69. 69. 82
  70. 70. Measuring change • Ultimate goal of most treatment studies is to induce a change in the patient‘s status • Controversial views against and for scales which are more sensitive to change in health status • Goals of measuring change – To distinguish between those individuals who change a lot and those who change little – To identify correlates of change – To infer treatment effects from group differences 83
  71. 71. • It is easier to demonstrate a consistent change in all the subjects, rather than different amounts of change in different subjects • Why don‘t we measure change directly? – Ask patients how have they changed since they were put on the treatment, because people simply do not remember how they were at the beginning (validity of such response is debatable) – Most defensible way to assess change is to measure it directly at the beginning of the study and subsequently on one or more occasions 84
  72. 72. Measures of association • Reliability • Sensitivity to change 85
  73. 73. Reliability of change score 86
  74. 74. Sensitivity to change from treatment effects 87
  75. 75. Item response theory • Limitations of G theory – Subject/population specific – Difficult to compare a person‘s score on two or more different tests (convert to z scores, normality assumption, not always correct) – Homoscedasticity assumption that errors are the same at the ends as in the middle range of scores – Assumption that all items have equal valences • Classical test theory – difficult to separate the properties of the test from the attributes of the people taking it – the tool‘s properties change as the people tested change, the people‘s properties change as the test cahnges 91
  76. 76. • IRT – claims to rectify these limitations – Based on two ‗hard assumptions‘ 1. Data are unidimensional (tap only one trait) 2. The probability of answering any item in positive direction is unrelated to the probability of answering any other item positively for people with the same amount of the trait (local independence) – Two postulates 1. Performance of a subject can be predicted by a set of factors called ‗traits‘ or abilities, latent traits (theta) 2. The relationship between a person‘s performance on any item and the underlying trait can be described by an ‘item characteristic curve’ 92
  77. 77. • Some important properties of ICC – They are ‗ogives‘, usually – Monotonic; the prob. of answering in a positive direction consistently increases as the score on the trait increases – Differ from each other in three dimensions • Slope • Location along the trait • The flattening out at the bottom – Can be thought of as ‗imperfect‘ Guttman scales 93
  78. 78. Item characteristic curve • Q. A is a better discriminator than Q. B • Q. B is harder than Q. A 94
  79. 79. Different models of ICC • One parameter model (Rasch model) – Assumes that all items have equal discriminating ability but different difficulty 95
  80. 80. • Two parameter model – Assumes that both discriminating ability and difficulty differ 96
  81. 81. • Three parameter model – In addition to the two parameter the lower end of the tail asymptotes at some probability greater than 0 – Takes care of the fact that when people answer questions by guessing/ items that are correct by chance 97
  82. 82. Deriving the curves • Taking a large number of subjects (200 for one-parameter model, 1000 to estimate the 3 parameter model) • Random sampling Vs. latent trait model – In random sampling – it is not necessary to know much about the items but large pool of items required – Latent trait model – fewer items are required but every item should be known in detail 98
  83. 83. Advantages and disadvantages • Allows test-free measurement; people can be compared to one another even if they took different items • Eg. Wide Range Achievement Test • People in different levels can be given different items and yet be placed on the same scale at the end (adaptive/tailored testing) • Not widely used because – Large sample size needed to estimate the parameters – Assumptions are difficult to meet 99
  84. 84. Future guidelines for developing health measurements 1. Articles/manuals should give full description of purpose, population, intended use 2. Rationale for design of the instrument – conceptual definition if the object of measurement 3. Describe the ways in which questions were selected 4. Revisions if any should be stated along with reliability and validity 5. Clear instructions for standard administration and scoring 6. Reliability and validity testing should examine both internal structure and its relation to alternative measurements of the concept 7. The tool should be testes by users other than the original authors 100
  85. 85. Critical appraisal – Rcq - 36 • • • • • What is the population in this study? What is the type of scale? What is the scaling method used? Have they missed any method for item generation? Is Cronbach‘s alfa calculated appropriately and is the scale reliable? • Is it appropriate to calculate mean (SD) for each domain? • Have they established construct validity in this study? Comment on the MTMM matrix used. • Can this scale be used to measure treatment effects for RC? 101
  86. 86. Thank you ―Belief is no substitute for arithmetic‖ — Henry Spencer 102
  87. 87. Scaling Response Continuous Direct estimation VAS Discrete Categorical Comparative methods Adjectival scale Continuous Econometric methods Specific scaling Likert 103