Translation, adaptation and validation of the American short form Patient Activation Measure (PAM13) in a Danish version
© Maindal et al; licensee BioMed Central Ltd. 2009
Received: 08 December 2008
Accepted: 29 June 2009
Published: 29 June 2009
The Patient Activation Measure (PAM) is a measure that assesses patient knowledge, skill, and confidence for self-management. This study validates the Danish translation of the 13-item Patient Activation Measure (PAM13) in a Danish population with dysglycaemia.
358 people with screen-detected dysglycaemia participating in a primary care health education study responded to PAM13. The PAM13 was translated into Danish by a standardised forward-backward translation. Data quality was assessed by mean, median, item response, missing values, floor and ceiling effects, internal consistency (Cronbach's alpha and average inter-item correlation) and item-rest correlations. Scale properties were assessed by Rasch Rating Scale models.
The item response was high with a small number of missing values (0.8–4.2%). Floor effect was small (range 0.6–3.6%), but the ceiling effect was above 15% for all items (range 18.6–62.7%). The α-coefficient was 0.89 and the average inter-item correlation 0.38. The Danish version formed a unidimensional, probabilistic Guttman-like scale explaining 43.2% of the variance. We did however, find a different item sequence compared to the original scale.
A Danish version of PAM13 with acceptable validity and reliability is now available. Further development should focus on single items, response categories in relation to ceiling effects and further validation of reproducibility and responsiveness.
Several initiatives have been taken to develop evidence-based activities to improve care for chronic conditions and, as in the Chronic Care Model, collaborative care and patient activation are cornerstones [1, 2]. Evaluations of these initiatives are essential for further research and development. Although patient activation has been a central concept of chronic care for decades, there is a general lack of clarity regarding the definition of "activation", and consequently a lack of adequate assessment tools.
Thirteen-Item Patient Activation Measure
When all is said and done, I am the person who is responsible for managing my health condition
Taking an active role in my own health care is the most important factor in determining my health and ability to function
I am confident that I can take actions that will help prevent or minimize some symptoms or problems associated with my health condition
I know what each of my prescribed medications do
I am confident that I can tell when I need to go get medical care and when I can handle a health problem myself
I am confident I can tell my health care provider concerns I have even when he or she does not ask
I am confident that I can follow through on medical treatments I need to do at home
I understand the nature and causes of my health condition(s)
I know the different medical treatment options available for my health condition
I have been able to maintain the lifestyle changes for my health that I have made
I know how to prevent further problems with my health condition
I am confident I can figure out solutions when new situations or problems arise with my health condition
I am confident that I can maintain lifestyle changes like diet and exercise even during times of stress
The aims of this study were to translate and adapt the original American PAM13 into a Danish version and to report data quality and psychometric scale properties in a Danish population with dysglycaemia.
Translation and adaptation of the PAM13
A systematic approach to translation and adaptation was conducted as recommended by WHO . It implies five steps: forward-translation, expert panel discussion, backward translation, a pre-test, a cognitive briefing and a consensus on the final version. Two independent native translators with Danish as their mother-tongue translated the original version of PAM13 from American English to Danish. These two translators comprised the expert panel together with three experts in chronic care and measurement development. The panel reconciled the forward translation into a single translation by identifying and resolving inadequate expressions or concepts. Back-translation was done as a quality control step to ensure that the original meaning of the concepts was derived. The back-translations were conducted by two independent native translators with American/English as their mother tongue and without knowledge of the questionnaire beforehand.
A pretest investigated the level of comprehensibility and cognitive equivalence of the translation [9, 10] among 12 patients with newly diagnosed type 2 diabetes from a local diabetes outpatient clinic. The patients filled in the questionnaire at home and participated in a focus group interview on the following day. The interviewer (first author) facilitated a cognitive briefing on general comprehensiveness followed by a review of each question. The participants were asked to think out loud, highlight problems and express their attitude to the question. The final version of the questionnaire was resolved by the expert panel, and a simple and acceptable language was ensured in accordance with the WHO guidelines .
The Danish version of PAM13 was sent to 467 participants in "The Ready to Act" health education randomised controlled study (296 in the intervention and 171 in the control group) at the 12-month follow-up . Participants were between 43 and 75 years and diagnosed with different aspects of dysglycaemia (Impaired Fasting Glucose, Impaired Glucose Tolerance and Type 2 diabetes) by a step-wise screening procedure in general practice within the last five years [12, 13]. Participants received a mail-administered PAM13 as part of a larger 16-page 1-year follow questionnaire on the psychological and behavioural outcomes of the "Ready to Act" study. A reminder including a new questionnaire was sent to participants who did not respond within three weeks.
Ethical approval of the study was attained from the local Science Ethics Committee of Aarhus County, Denmark (protocol no: 20000183). All participants gave informed content. The Danish Data Surveillance Authority permitted the collection and storing of data (journal no: 2000-41-0042).
Each item had five response categories with scores from 1 to 5: (1) strongly disagree, (2) disagree, (3) agree, (4) agree strongly and (5) not applicable. PAM is scored as a sum-scale. Only PAM-questionnaires with answers to seven or more items were included in the analyses. If included questionnaires had missing observations, these observations were omitted from the analysis, but not the corresponding persons or items. The raw scores were transformed into natural logarithms to achieve a better expression of the relative distances between the scores . Further, items were calibrated from the logit metric to a user-friendly 0 to100 metric (0 = lowest activation level, 100 = highest activation)  to compare the Danish results to the original data.
Analyses and statistical methods
The psychometric elements of the PAM13 Danish version were examined in two parts.
First, we assessed the data quality, internal consistency and correlations between items and the sum of the other items. Data quality was assessed in terms of mean for each item with standard deviation, median, percentage of missing data, number of "not applicable" answers and extent of ceiling and floor effects. Floor and ceiling effects between 1–15% were defined as optimal . Internal consistency was assessed using Cronbach's alpha and average inter-item correlation. We defined an alpha of 0.80 as the lowest acceptable value [17–19]. In contrast to alpha, the average inter-item correlation is independent of the number of items and sample size when measuring internal consistency. We aimed at an average inter-item correlation between 0.15–0.50 .
We assessed whether each item had a high correlation with the sum score of the rest of the scale (internal item convergence), which is assumed in a unidimensional scale . Correlations were fixed at a minimum of 0.60 to reflect a high level of internal convergence .
Secondly, we used Rasch Rating Scale Model [21, 22] to investigate whether the scale was unidimensional, which is a prerequisite for the summation of the items . The following criteria for Rasch model were investigated; item statistics, person and item reliability, rating scale diagnostics, factorial test of residuals and differential item functioning.
In the Rasch analysis, person and item scores were used to calibrate items on a logit scale where the midpoint of the scale is 0. Items at one end of the scale are "easier"/"less severe" and items at the other end are more "difficult"/"more severe". In the current analysis, items with a positive calibration were those indicating a high level of patient activation (more difficult to achieve).
From the Rasch model, we reported reliability and separation index for persons and items, and item statistics for measure order. Reliability expresses the reproducibility of the relative measure. A high reliability indicates that in all probability, persons (or items) with high measures actually do have higher measures than persons (or items) estimated with low measures. Winsteps  computes upper (Model) and lower (Real) boundary values for reliability. The true reliability can be found between these boundaries. Person reliability of 0.9 means that the scale may discriminate the sample into 3–4 levels, 0.8 into 2–3 levels and 0.5 into 1–2 levels. High item reliability merely indicates that the sample is big enough to precisely locate the items on the latent variable. We compared Rasch person reliability for subgroups to the original data .
An important characteristic of a high-quality scale is a good overall separation of persons and items assessed with the scale. The separation index is an estimate of the spread or separation of persons (or items) on the measured dimension. The separation index should be at least 2, indicating that the measure separated persons, items or both into at least two distinct groups . Individual items that are at least 0.15 logits apart represent individual strata . Otherwise one item is not distinctly separate from the next.
Two item fit mean square (MNSQ) statistics (infit and outfit) were computed to check whether the items fitted the expected model. MNSQ determines how well each item contributes to defining a single underlying construct (unidimensionality). Infit is more sensitive to misfitting responses to items closest to the person's ability level, while outfit is more sensitive to misfitting items that are farther away. If the data fit the Rasch model, the fit statistics should be between 0.6 and 1.4 .
The assumption is that the use of response categories for each item reflects the way people answer the items that are close to each other. However, this is only true if the distances between each response category are similar. The step measure (Rasch-Andrich threshold) is a calibrated measure of the transition between response categories. The thresholds are expected to increase monotonically. If not, the response categories do not reflect a reasonable interval on the latent variable, and consequently indicate substantial problems with the category definitions. Thresholds should increase by at least 1.4 logits, to show distinction between categories, but not more than 5.0 logits, to avoid large gaps in the variable .
Local independence of items was tested using Principal Components Analysis (PCA) on the Rasch item measure residuals. The purpose of PCA of residuals is to analyse the amount of unexplained variance and whether this unexplained variance indicates that there may be more than one dimension. Simulation studies indicate that even Rasch-conforming data produces residual-factors with eigenvalues up to 2.0. Thus, if there is more than one contrast (factors) in the residuals, there may be a second dimension. Contrasts in the Rasch analysis of residuals contradict unidimensionality .
As the last part of the Rasch analysis, we assessed differential item functioning (DIF) by estimating item parameters separately by groups of participants (sex, age groups, diagnosis, education, self-rated health and randomisation group). The scale should work uniformly, irrespective of the group assessed. The criteria used for the DIF analysis was DIF contrast >0.50. We tested using t-test and compared the probability multiplied by the number of DIF tests for each variable with a significance level of 0.05 to correct for multiple comparisons by the Bonferroni method.
Translation and adaptation
The two translations from American to Danish agreed on most items. Different Danish words were used, but were semantically equivalent. A few conceptual discrepancies were identified; for example "health care", "medical care" and "treatment" had slightly different meanings, when used directly translated into Danish (item 5, 7, 9). The two translated versions were reconciled into a single translation relevant for Danish terminology at expert panel meetings between the translators and the research group.
The first back-translation included all items; the second back-translation included four items (item 2, 4, 7, 13). The emphasis in the back translation was on the conceptual and cultural equivalence, and not the linguistic as suggested by WHO . We recognised a few general problems when comparing the two back-translations with the original version  and the first Danish draft: As in the forward-translation, we had difficulty translating health service terminology, partly because of organisational differences, and partly because a lack of specific Danish words for health care, illness and disease.
In the pre-test, the participants found all thirteen items relevant for measuring activation. The participants found the introductory wordings easy to understand; in addition they considered the response-categories exhaustive and exclusive. The participants found that the word "treatment" directed their attention to medication rather than diet or exercise. As half of the participants did not receive medication, they suggested that this elaborated term; "treatment (e.g. medicine, diet and exercise)" was used in the introductory wordings and in item two and seven. The expert group incorporated the results from the briefing process in the draft version, and proofreaders corrected the spelling and grammar.
A total of 358 of 467 (76.7%) returned the questionnaire. Excluding "not applicable" answers, 344 had answered at least 50% of the items (>6 items) in PAM13. The mean age of the participants was 62.3 (s.d.: 7.1), 44.8% were female, 60.5% had type 2 diabetes and 39.5% had a pre-diabetic condition. The respondents had been diagnosed within the last five years (median 2 year (interquartiles 0–4).
Data quality and item-rest correlations of the 13-items Patient Activation Measure Danish version in a population with dysglycaemia (n = 358).
% of N = 358
% of N = 358
Thirteen-items Patient Activation Measure with Item Calibrations ordered by difficulty calibration
N = 344
Measure (0–100 scale)
Person and item reliability
Reliability of 13-item Patient Activation Measure of the Danish version compared with the American version 
Rasch Person Reliability
Short (1–3 years)
Higher (>3 years)
Type 2 diabetes
Rating scale diagnostics
Response category measures and fit for 13-items Patient Activation Measure (n = 344)
Factorial test of residuals
PCA of item measure residuals revealed one dimension. A total of 43.2% of the variance in the data was explained by the measures and with a perfect model fit, this was expected to be 43.1%. The eigenvalue of the first PCA contrast was 2.5, which corresponded to 11% of the variance in the data.
Differential item functioning
No significant DIF was found in subgroups of self-rated health, diagnosis or randomisation groups. Items 1 and 2 were easier to endorse for highly educated persons compared to persons with short education (p-values Bonferroni-corrected) (DIF contrast = 1.35, p = 0.027 and DIF contrast = 1.22, p = 0.031). Item 10 was easier to endorse for men (DIF contrast = -0.65, p = 0.049) and item 13 was easier to endorse for persons between 65 and 74 years compared to persons under 55 (DIF contrast = 1.3, p<0.001) and compared to persons between 55 and 64 years (DIF contrast = 0.9, p = 0.023).
We found it possible to make a standardised translation and adaption of the original PAM13. The forward-backward translation was successfully conducted and the few discovered conceptual differences were primarily due to differences in health care systems. These findings are supported by the fact that e.g. the reliabilities in subgroups were comparable with the American version.
The psychometric assessment of the Danish version replicated to a great extent the findings from the original version  showing similar data quality and internal consistency. We found that the items had a different order. The items are arranged progressively in order of difficulty to reflect the developmental continuum of patient activation in an American population with chronic diseases. However, this was not confirmed by the results in this study, meaning that this population simply found some questions easier to answer compared with an American population. This could be due to the specific population of people with screen-detected newly diagnosed dysglycaemia compared with people with more manifest chronic diseases. If a similar item sequence is found in future studies in a Danish population, the PAM13 may need revision. The test items should be reordered to represent consecutive item difficulty, if the originally proposed model with four stages of activation identified by Hibbard et al  is to be of significance in the Danish version. Further, the person reliability did not indicate an ability to separate four levels at all. This may be due to differences between the Danish and the American populations, but as mentioned, the psychometric results in many instances replicate the American findings.
The investigation of the scale properties in general showed that PAM13 may be regarded as a unidimensional scale performing as a Guttmann sum-scale. This was particularly true for the reliability measures, measures of unidimensionality and aspects of the response categories. We found e.g. that the scale could distinguish between 2–3 levels as the person reliability was between 0.8–0.9, and the statistics indicated that each item could be regarded as part of one dimension (infit and outfit). The high-reliability estimates at the person level indicate that the scale is appropriate on an individual basis to diagnose activation and individualise plans for future health care as suggested .
However, we noted some possible problems with ceiling effect, potentially irrelevant items, and important aspects for responsiveness and separation difficulties at three points in the scale. Most PAM13 items demonstrated a ceiling effect and Items 1 to 3 had more than 50% of the answers in "agree strongly". This percentage suggests that the response categories do not cover relevant answers for the study population. Caution must be taken in future studies if ceiling effects are common in Danish populations. The high ceiling effect may be a problem if PAM13 is to be used for measuring change over time (e.g. in randomised studies) because of low responsiveness.
The five items with more than 10% answers in "not applicable" indicate that the scale cannot be used for all types of patients with chronic diseases and a revision of these items might be necessary.
On three points, there seemed to be no additional information when answering the next item in the scale (no separation). This means that 2–3 items can be omitted from the scale as a simple sum-scale. Further research will clarify the items to be omitted.
Although we may conclude that the Rasch analysis supports the PAM13 as a unidimensional sum-scale, some results do, however, indicate a need for improvement. We noted that six items had low correlations with the sum of the rest of the items, which indicates that they may not be absolutely true to one dimension. In addition, the test for other dimensions (PCA) revealed a possible additional factor. However, criteria for deciding whether there are two or more dimensions and when a deviation becomes a dimension have yet to be established. To the best of our knowledge, the rule of thumb is  that variance explained by measures four times greater than the variance explained by the additional factor and the size of the components less than three, is good. Our analysis does therefore not indicate more than one dimension.
When testing for differential item-function, most items did not have DIF in subgroups. However, items revealing DIF (item 1, 2, 10 and 13) showed possible explanations for this. Items 1 and 2 may appeal to educated people, being responsible and taking action. Gender seemed to play a role for item 10, which was endorsed by men more often than women, setting less demanding goals for their lifestyle changes. Patients aged 65–74 who answered more convincingly to item 13 may be explained by more experience of maintaining lifestyle even during stress.
The activation score in the Danish version covered the range from 33.3–57.5, which is more than the range of 38.6–53.0 for the American data . However, this may not be enough to be able to detect changes in underlying behaviour studies, and in particular clinically relevant changes, which subsequently have to be tested.
Strengths and limitations
The systematic translation approach was a strength in this study. Translation has no best practice as yet [9, 28] and in particular, the value of back-translation has recently been questioned . In our study, the backward translation procedure contributed with new perspectives on the cultural differences in the health care concepts.
We obtained a high response rate with 74% answering more than half of the items. This may minimise the risk of selection bias. The sample size of 344 persons was sufficient for this validation study as a minimum of 300 respondents is recommended to replicate structural analyses . The mean square statistics used in the Rasch analysis are moderately insensitive of sample size for polytomous data .
The fact that PAM13 was delivered as part of a larger questionnaire at the 12-month follow-up of a health education intervention study might have affected the actual score level, but it is unlikely to have changed the scale properties. The number of missing values may have been higher that at the baseline questionnaire due the respondents being fatigued by the questionnaire.
The rather heterogeneous group of patients may be regarded as a weakness in many instances. However, when assessing scale properties, the use of a population representing many levels of activation is an advantage. A population screen-detected with dysglycaemia represents merely one of a range of chronic conditions.
A Danish version of PAM13 measuring the latent variable of patient activation in chronic care is now available, although further development is recommended before use in daily practice. The PAM13 questionnaire was translated and adapted into Danish in a sample with screen-detected dysglycaemia showing initial reasonably good validity and reliability.
The Danish version formed a unidimensional, Guttman-like scale. The order of the items differed compared with the American version and therefore the suggested four activation stages in the American version were not relevant. Our findings show that the Danish PAM13 has promising psychometric properties indicating that going on with further validation in other populations with chronic diseases is expedient. However, special attention to discrimination and responsiveness is required to be able to use the score as a screening tool for tailored interventions. These studies have to be carried out before we have a much requested fully evidence-based activation measure for use in Danish chronic care intervention studies and in daily practice.
Differential Item Functioning
Patient Activation Measure 13-items
Principal Components Analysis.
We thank the participants that filled in the PAM13. We are grateful to Judith Hibbard and colleagues for the permission to translate PAM13 into Danish. We appreciated the help from Martin Tusler, who shared his experience and provided information on the development of the original PAM. Our work was funded by the Centre for Innovation in Nursing Education, Aarhus, the Danish Nurses' Organization, Novo Nordic and the Danish Diabetes Association. Thanks to Annelli Sandbaek, Morten Pilegaard, Eva Hojmark, Robert Hay and Janet Mikkelsen for participating in the expert panel.
- Wagner EH, Austin BT, Davis C, Hindmarsh M, Schaefer J, Bonomi A: Improving chronic illness care: translating evidence into action. Health Aff (Millwood). 2001, 20 (6): 64-78. 10.1377/hlthaff.20.6.64.View ArticleGoogle Scholar
- Bodenheimer T, Wagner EH, Grumbach K: Improving Primary Care for Patients With Chronic Illness. JAMA. 2002, 288 (14): 1775-9. 10.1001/jama.288.14.1775.View ArticlePubMedGoogle Scholar
- Hibbard JH, Stockard J, Mahoney ER, Tusler M: Development of the Patient Activation Measure (PAM): Conceptualizing and Measuring Activation in Patients and Consumers. Health Services Research. 2004, 39 (4p1): 1005-26. 10.1111/j.1475-6773.2004.00269.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Hibbard JH, Mahoney ER, Stockard J, Tusler M: Development and Testing of a Short Form of the Patient Activation Measure. Health Services Research. 2005, 40 (6p1): 1918-30. 10.1111/j.1475-6773.2005.00438.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Hibbard JH, Tusler M: Assessing activation stage and employing a "next steps" approach to supporting patient self-management. J Ambul Care Manage. 2007, 30 (1): 2-8.View ArticlePubMedGoogle Scholar
- Hibbard JH, Mahoney ER, Stock R, Tusler M: Do Increases in Patient Activation Result in Improved Self-Management Behaviors?. Health Services Research. 2007, 42 (4): 1443-63. 10.1111/j.1475-6773.2006.00669.x.View ArticlePubMedPubMed CentralGoogle Scholar
- Mosen DM, Schmittdiel J, Hibbard J, Sobel D, Remmers C, Bellows J: Is patient activation associated with outcomes of care for adults with chronic conditions?. J Ambul Care Manage. 2007, 30 (1): 21-9.View ArticlePubMedGoogle Scholar
- World Health organization: Process of translation and adaptation of instruments. WHO. 2007, [http://www.who.int/substance_abuse/research_tools/translation/]Google Scholar
- Wild D, Grove A, Martin M, Eremenco S, McElroy S, Verjee-Lorenz A, et al: Principles of Good Practice for the Translation and Cultural Adaptation Process for Patient-Reported Outcomes (PRO) Measures: report of the ISPOR Task Force for Translation and Cultural Adaptation. Value Health. 2005, 8 (2): 94-104. 10.1111/j.1524-4733.2005.04054.x.View ArticlePubMedGoogle Scholar
- Willis GB: Cognitive Interviewing: A "How To" Guide. Reducing survey error through research on the cognitive and design processes in surveys. Short course presented at the 1999 meeting of the American Statistical Association. Research Triangle Institute. 1999, Research Triangle InstituteGoogle Scholar
- Maindal TH: Development and Evaluation of "The Ready to Act" education intervention focusing on Action Competence in a screen-detected population with dysglycaemia. PhD thesis. 2009, University of Aarhus, AarhusGoogle Scholar
- Sandbaek A, Griffin SJ, Rutten G, Davies M, Stolk R, Khunti K, et al: Stepwise screening for diabetes identifies people with high but modifiable coronary heart disease risk. The ADDITION study. Diabetologia. 2008, 51 (7): 1127-34. 10.1007/s00125-008-1013-0.View ArticlePubMedPubMed CentralGoogle Scholar
- Lauritzen T, Griffin S, Borch-Johnsen K, Wareham NJ, Wolffenbuttel BH, Rutten G: The ADDITION study: proposed trial of the cost-effectiveness of an intensive multifactorial intervention on morbidity and mortality among people with Type 2 diabetes detected by screening. Int J Obes Relat Metab Disord. 2000, 24 (Suppl 3): S6-11.View ArticlePubMedGoogle Scholar
- Bond TG, Fox CM: Applying the Rasch Model. Fundamental Measurement in the Human Sciences. 2007, Mahwah, New Jersey, Lawrence Erlbaum Associates, 2Google Scholar
- Linacre J: Estimating measures with known polytomous item difficulties. Rasch Measurement Transactions. 1998, 12 (2): 638-Google Scholar
- McHorney CA, Tarlov AR: Individual-patient monitoring in clinical practice: are available health status surveys adequate?. Qual Life Res. 1995, 4 (4): 293-307. 10.1007/BF01593882.View ArticlePubMedGoogle Scholar
- Sitzia J: How valid and reliable are patient satisfaction data? An analysis of 195 studies. Int J Qual Health Care. 1999, 11 (4): 319-28. 10.1093/intqhc/11.4.319.View ArticlePubMedGoogle Scholar
- McDowell I: Measuring Health – A Guide to Rating Scales and Questionnaires. 2006, Oxford: Oxford University Press, IncView ArticleGoogle Scholar
- Clark A, Watson D: Constructing Validity: Basic Issues in Objective Scale Development. Psychological Assesment. 1995, 7: 309-19. 10.1037/1040-3522.214.171.1249.View ArticleGoogle Scholar
- Fayers PM, Machin D: Quality of Life. Assessment, Analysis and Interpretation. 2007, Chichester: John Wiley & Sons, LtdGoogle Scholar
- Rasch G: Probabilistic models for some intelligence and attainment tests. Expanded edition (1980) with foreword and afterword by B.D. Wright ed. 1960, Chicago: (Danish Institute for Educational Research, Copenhagen)Google Scholar
- Andrich D: A rating formulation for ordered response categories. Psychometrika. 1978, 43: 357-74. 10.1007/BF02293814.View ArticleGoogle Scholar
- Streiner DL, Norman GR: Health Measurement Scales – a practical guide to their development and use. 2003, Oxford University Press, [http://www.oup.com]Google Scholar
- Linacre J: User's guide and program manual to WINSTEPS: Rasch model computer programs. 2007, Chicago: MESA PressGoogle Scholar
- Silverstein BP, Kilgore KM, Fisher WP, Harley JP, Harvey RF: Applying psychometric criteria to functional assessment in medical rehabilitation: I. Exploring unidimensionality. Arch Phys Med Rehabil. 1991, 72 (9): 631-637.PubMedGoogle Scholar
- Smith RM: Polytomous Mean-Square Fit Statistics. Rasch Measurement Transactions. 1996, 10 (3): 516-7.Google Scholar
- StataCorp: Stata Statistical Software: Release 10.0. 2007, College Station, TX: StataCorpGoogle Scholar
- Acquadro C, Conway K, Hareendran A, Aaronson N: Literature Review of Methods to Translate Health-Related Quality of Life Questionnaires for Use in Multinational Clinical Trials. Value Health. 2008, 11 (3): 509-521. 10.1111/j.1524-4733.2007.00292.x.View ArticlePubMedGoogle Scholar
- McKenna SP, Doward LC: The translation and cultural adaptation of patient-reported outcome measures. Value Health. 2005, 8 (2): 89-91. 10.1111/j.1524-4733.2005.08203.x.View ArticlePubMedGoogle Scholar
- Smith AB, Rush R, Fallowfield LJ, Velikova G, Sharpe M: Rasch fit statistics and sample size considerations for polytomous data. BMC Med Res Methodol. 2008, 8: 33-10.1186/1471-2288-8-33.View ArticlePubMedPubMed CentralGoogle Scholar
- The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-2458/9/209/prepub
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.