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The German Inpatient Satisfaction Scale: A Large-Scale Survey of Perceived Quality by Inpatients

The German Inpatient Satisfaction Scale: A Large-Scale Survey of Perceived Quality by Inpatients A patient satisfaction instrument that is based on a large, representative sample does not yet exist in the German language. The objective of this article was to fill this gap by providing initial validation evidence of the German Inpatient Satisfaction Scale as an instrument for German-speaking countries. We performed an exploratory factor analysis and exploratory structural equation modeling in a cross-sectional design. The instrument was administered to N = 116,325 patients in hospitals in German-speaking countries. The overall response rates ranged from 63% to 98%. We found that a four-factor solution fit the data well. The four factors represented satisfaction with doctors’ care, nursing care, service facilities, and secondary care facilities. Cronbach’s alpha ranged from .72 to .90. The findings may be of practical interest for health care providers for measuring patient satisfaction. Keywords patient satisfaction, survey, questionnaire, validation, factor analysis, inpatient The idea of assessing the quality of care from the patient’s as well as for improving treatment. A number of instruments point of view has been a research topic since the 1950s, but it for assessing patient satisfaction have been developed in has recently received a surge in interest (Görtz-Dorten, Breuer, English, including, as a prominent example, the Hospital Hautmann, Rothenberger, & Döpfner, 2011; Hansen et al., Consumer Assessment of Health-Care Providers and Systems 2010; Parsons, 1951). The importance of this area of research (HCAPS; Giordano, Elliott, Goldstein, Lehrman, & Spencer, is threefold. First, patient satisfaction is a health care outcome 2010), but a need for culturally sensitive adaptations has in its own right. Second, satisfied patients are more likely to been identified (Aharony & Strasser, 1993). With regard to show better compliance with treatments. Third, from an orga- German-speaking countries, some patient satisfaction instru- nizational point of view, understanding the causes, correlates, ments exist (Kleeberg et al., 2005), but most of these have and consequences of patient satisfaction with health care ser- been based on relatively small sample sizes that are most vices can help to improve the quality of these services. In sum, likely not representative of the general patient population. patient satisfaction is an element of medical care of increasing Furthermore, studies have largely failed to address importance (S. J. Williams & Calnan, 1991). whether patient satisfaction is homogeneous across different One definition of patient satisfaction can be summarized as a subgroups (e.g., patients in different medical departments). “positive evaluation of distinct dimensions of the health care For a given scale, research must confirm that such subgroups system” (Linder-Pelz, 1982, p. 578). In more detail, patient sat- share a common understanding of patient satisfaction for the isfaction entails beliefs, evaluations, and reactions to the con- common practice of comparing and combining such scores text, process, and results of a health care provider’s service to be meaningful. (Pascoe, 1983). A large number of different approaches that can be used to explain the causes of and the processes behind patient Applied University for Health and Sports, Berlin, Germany satisfaction have been proposed. For example, discrepancy Forschungsgruppe Metrik, Bermuthshain, Germany models have been put forth to explain patient satisfaction as a FOM University of Applied Sciences, Munich, Germany function of perceived quality and expectancy—the smaller the 4 Samueli Institute, Alexandria, VA, USA gap, the higher the resulting satisfaction (B. Williams, 1994). University of Tübingen, Germany Although some insights into the processes behind patient Corresponding Author: satisfaction have been achieved, more questions await Sebastian Sauer, Institute of Business Psychology, FOM University of answers. To this end, valid measurement instruments are a Applied Sciences, Arnulfstr. 32, 80335 Munich, Germany. prerequisite for furthering the understanding of the construct Email: Sebastian.sauer@fom.de Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open In light of this, the aim of the present study was to present in a number of studies, for example, to evaluate medical care initial validation evidence for an instrument designed to (Clever, Jin, Levinson, & Meltzer, 2008; Hannemann-Weber, measure inpatient satisfaction in German-speaking coun- Kessel, Budych, & Schultz, 2011; Hojat et al., 2011; tries. In addition, we tested whether the factor structure of Zickmund, Hillis, Barnett, Ippolito, & LaBrecque, 2004). the present instrument would be invariant across different Although the need for a validated language-specific patient medical departments. satisfaction questionnaire is obvious, it appears that there is a paucity of measures that were developed with a rigorous methodology for German-speaking countries. It was the aim Current Developments and Research of this article to help fill this gap. We thus hypothesized that Questions the instrument under investigation in the present article, the German Inpatient Satisfaction Scale (GISS), would demon- The use of patient satisfaction surveys became a routine pro- strate adequate psychometric properties. cedure over the course of the 1990s (Di Palo, 1997). One instrument that was developed in 1995 in the United States and that received a substantial amount of interest was the Method HCAPS. As it was extended and refined over the years that followed, the instrument increasingly became a widely Procedure and Sample acknowledged and employed survey instrument (Quigley, Hospital staff members in German-speaking countries Elliott, Hays, Klein, & Farley, 2008). A German version has (Germany, Switzerland, and Austria) collected the data dur- also been presented (Squires et al., 2012). ing the patients’ stay at the respective hospital. Care was A frequent problem encountered with many satisfaction taken so that the questionnaire was distributed after lunch instruments is item skewness, which thus limits the research- and collected before dinner. er’s ability to distinguish between patient satisfaction levels In sum, 143 hospitals participated, out of which 115 (80%) when using the instrument (Sitzia & Wood, 1997). For exam- were from Germany, six (4%) came from the German-speaking ple, many respondents tend to choose good and very good as parts of Switzerland, and 22 (16%) stemmed from Austria. It the answer to a given item. Administered answer options should be noted that the results apply mainly for Germany. In should be designed to allow for enough differentiation in the total, 24% of the hospitals were in private ownership, and the light of such response patterns. One current remedy is to hospitals are spread all over Germany, Switzerland, and include a response category above very good, such as excel- Austria. Some of them belong to same, and some to different lent (Young, Meterko, & Desai, 2000). clinic groups. The sample was not drawn randomly, but the hospitals were approached for participation in the study in the Patient Satisfaction in Different Health course of business partnership with the first author at that time. As an incentive to participate in the study, each hospital Care Systems received feedback about their average level of patient satisfac- As mentioned above, the health care system has a large influ- tion in comparison with the respective mean. Hospitals were ence on the satisfaction of the patients and what the patients allowed to use this feedback for internal quality management can or must be asked about. Most patients in the United purposes. States have to pay for their treatment, and therefore it would Overall, our sample size was N = 116,325 patients. The be appropriate to ask them how they evaluate the price-per- mean number of patients per hospital was 813 (SD = 775). Data formance ratio. In German-speaking countries, the treatment were collected between 2005 and 2009. We took care to access is mostly covered by the mandatory health insurance. a representative sample of all different somatic medical depart- Therefore, different health care systems need different instru- ments. The inclusion criteria were an age of 18 or older, status ments, and the results from one country cannot be transferred as an inpatient in the hospital under investigation when the sur- to another (Aharony & Strasser, 1993). vey was administered, and the ability to understand and respond to the items. An exclusion criterion was admission to a psychi- atric or psychosomatic ward. Patients were informed about the Patient Satisfaction in German- aims of the study and filled out the survey anonymously. Speaking Countries To gauge the degree to which our sample is representative What is known about the situation in German-speaking for the general population, we compared age and sex distribu- countries? For example, the Munich Patient Satisfaction tion of our sample with the general patients’ population based Scale (MPSS-24) is a 24-item scale that was developed with on statistics from the Statistisches Bundesamt (2015). Over and exploratory factor analysis in three small samples (n = 85, above, the figures for the age distribution for our sample and n = 161, n = 91; Möller-Leimkühler et al., 2002); one princi- the overall population were similar (15-44 years: 21% vs. 17%; pal factor was found. The authors reported a high internal 45-64 years: 31% vs. 25%; 65 years and older: 48% vs. 59%). consistency as well as a good reliability; convergent validity As to sex, the figures were identical (GISS sample male: 47%; was found to be satisfactory. In addition, it has been utilized general population 47%). Thus, some support for the notion Zinn et al. 3 that our sample was representative is present. However, more we used an exploratory structural equation modeling characteristics would be needed to back up this claim. approach (ESEM). The ESEM model is estimated separately for each group, and some parameters can be constrained to be invariant across those groups. In the present study, we Questionnaire used multigroup ESEM tests of full measurement invariance To develop the GISS’s initial item set, we included results of EFA factors across all groups. We thus tested whether the from focus groups, expert interviews, published research, factor structure was identical in all subgroups. To test for the patient feedback, as well as the clinical practices of the appropriateness of this invariance assumption, we used com- authors and their colleagues. This process resulted in an ini- mon fit indices for this purpose; that is, we used the com- tial set of 36 satisfaction items. In addition, we asked the parative fit index (CFI), the root mean square error of patients how important the main aspects of the hospital (e.g., approximation (RMSEA), the standardized root mean square doctors, nursing staff, eating) were to them. In a second step, residual (SRMR), and the chi-square test statistic to evaluate the instrument received a revision, which resulted in a reduc- the goodness of fit. The CFI ranges from 0 to 1, with values tion in the number of items. Later, the instrument was greater than .90 and .95 typically taken to reflect acceptable extended by including additional items as a result of analyses and excellent fits to the data, respectively. RMSEA values of of patients’ qualitative comments (Zinn, 2010, p. 177). less than .05 and .08 reflect close and reasonable fits, respec- The present study examined the 28 satisfaction items tively; values between .08 and .10 reflect a moderate fit, and from the GISS. Each item stem was formulated as a semantic values greater than .10 are generally considered unaccept- differential with five response options (1 = the best I ever able. Given normally distributed outcomes and a large sam- experienced, 2 = very good, 3 = good, 4 = acceptable, 5 = ple size, the cutoff value for the SRMR should be close to .07 bad). All items were later reverse-coded so that they would (Hu & Bentler, 1995). Prior to analysis, we examined the reflect satisfaction rather than dissatisfaction. intraclass correlation coefficient (ICC) of all items. The ICC represents the proportion of variance that can be attributed to higher level units (i.e., hospitals). Analysis We used Mplus 5.2 (Muthén & Muthén, 2007) for all analyses. Results Alpha was set at .01. The primary objective was to analyze the psychometric quality of the items by means of exploratory The overall response rate per department for each hospital principal axis factor analysis (EFA). To determine the number ranged from 63% to 98%. The average age was 59.1 years of factors, we used Horn’s parallel analysis (Glorfeld, 1995) (SD = 18.4); 53% of the patients were female. The mean and Velicer’s minimum average partial (MAP) test (O’Connor, length of stay was 7.6 days (SD = 8.2). It is informative to 2000). Horn’s parallel analysis compares the number of compare these figures with German averages: 53% of inpa- extracted factors with the number of factors that would have tients in German hospitals are female, and the mean duration been extracted if the data had been completely random. The is 7.4 days (Statistisches Bundesamt, 2015). focus of the MAP test lies on the relative amounts of system- All items were checked for nonnormality prior to the atic variance remaining in the correlation matrix after extract- analyses. Skewness and kurtosis in the data were small (for ing increasing numbers of factors. For EFA, we decided to use all indicators, skewness ranged from 0.04 to 0.58, kurtosis a varimax rotation. In addition, more basic criteria (the from 0.27 to 0.73). A robust Maximum Likelihood (MLR) screeplot “elbow”; the Kaiser criterion, that is, the number of estimation was employed as the estimation method for the eigenvalues > 1) were considered. Following, descriptive sta- analysis. Due to only small skewness and kurtosis in the tistics were calculated to describe means, standard deviations, data, (largely) unbiased model fit estimates could be expected and reliabilities of derived factor scores. In addition, the large (West, Finch, & Curran, 1995). sample size allowed for a more fine-grained analysis on the The overall score for the 28 satisfaction items was between level of medical departments. The following 10 departments 2.10 and 2.96 (on a scale ranging from 1 = best I ever experi- were included for this purpose: surgery, internal medicine, enced to 5 = bad). The ICC coefficients for the items ranged gynecology, orthopedics, ophthalmology, otolaryngology from .06 to .13, so that 6% to 13% of the variance in patients’ (ear/nose/throat), urology, neurology, oncology, and other. We ratings was explained by the difference in hospitals. These used full information maximum likelihood (FIML) estimation results indicate that a relatively small percentage of item vari- in Mplus (Version 7.1) to deal with missing data. In the meth- ances was attributable to variations among hospitals. odological literature on missing data (Schafer & Graham, 2002), there is a growing consensus that FIML estimations EFA (and other procedures, for example, multiple imputation) are preferable to casewise or listwise deletion. An examination of the screeplot indicated a one-factor solu- In addition to factor analysis, to test whether the factor tion with an eigenvalue of 12.91 (see Figure 1). By contrast, solution held across all subgroups of medical departments, the Kaiser criterion favored a four-factor solution as four 4 SAGE Open Factor Structure in Subgroups Next, we were interested in whether the factor structure would be invariant across different medical departments. To compare patient satisfaction across different departments, it is necessary that the factor structure be identical or at least similar across the subgroups (see Table 3 for sample sizes in different medical departments). For eye patients, the factor structure differed from the rest of the medical departments; thus, we removed eye patients from the ESEM. As the factor score results showed moderate but substantial correlations, we decided to use an oblimin rotation for the ESEM analysis. The nine-group model with no invariance constraints provided a good fit to the data, χ (2448) = 88495.1, RMSEA = .05, CFI = .94, Tucker–Lewis index (TLI) = .92, and SRMR = .03. These results supported Figure 1. Screeplot for the GISS factors. the configural invariance of the four proposed factors, mean- Note. GISS = German Inpatient Satisfaction Scale. ing that the same factor structure was able to fit the data for each group. Next, we constrained the factor loadings, factor intercepts, and item uniqueness to be invariant across the nine factors had eigenvalues > 1 (eigenvalues: Factor 1: 12.91; groups. To do this, 84 new constraints were added. A lack of Factor 2: 1.63; Factor 3: 1.31; Factor 4: 1.15). support for this model would suggest that the measurement On a more fine-grained level, Horn’s parallel analysis model was not comparable across the nine groups. The differ- (see Figure 1) and the MAP test were employed on the basis ences in the fit indices were small, χ (3744) = 106438.0, of indicator correlations. Both parallel analysis and the MAP RMSEA = .046, CFI = .927, TLI = .934, and SRMR = .041. test indicated four factors. Given these results, we decided to Although the chi-square value for the configural invariance force four factors in the factor analysis. model was significantly smaller than that of the full invari- The four-factor solution showed a clearly interpretable ance model (due to the large sample size), the chi-square/df structure. The results can be found in Table 1. For better ratio was substantially smaller. The small changes in the fit readability, values above .3 are printed in bold. The four fac- indices supported interpretations of invariance (Cheung & tors explained 61% of the variance (Factor 1: 19%; Factor 2: Rensvold, 2001) and provided clear evidence for the compa- 18%; Factor 3: 12%; Factor 4: 12%). The first factor could rability of factor solutions across all of the patient groups. tentatively be called satisfaction with medical doctors’ care. Taken together, the results of the ESEM showed that the The second factor described satisfaction with nursing care; factor structure was similar across all medical departments the third factor indicated satisfaction with service facilities with the exception of ophthalmology. Ophthalmology (e.g., food, patient rooms, cafeteria), and the fourth factor patients seemed to have a different understanding of the captured satisfaction with secondary care facilities (e.g., meaning of patient satisfaction compared with all other physiotherapy, X-rays). Some of the items were excluded somatic departments (including neurology). For ophthalmol- due to cross-loadings or relatively small factor loadings. ogy patients, we found that several items loaded on two After dropping seven items, 21 of the initial 28 items factors. remained, showing a clear factor structure (see Table 1). In sum, we conclude that the results of the ESEM analysis could be taken as additional support for the validity of the Internal Consistencies of Factor Scores instrument. On the basis of the established factor solution, we further analyzed the psychometric properties of the factor scores. Discussion The internal consistency of the first factor (satisfaction with medical doctors’ care) was high. For the second scale (satis- The overall goal of the present study was to present initial faction with nursing care), the reliability was again high. validation evidence for an instrument measuring inpatient Excluding any one item increased alpha by only .01. The satisfaction in German-speaking countries (the GISS). In alpha value for the third scale (service facilities) was accept- addition, we tested whether the factor structure of the GISS able again; excluding any of the items did not increase alpha. would remain invariant across different medical departments. For the fourth scale (secondary care services), alpha was The particular strength of the study was its large-scale scope, again good. Excluding any one item did not increase alpha. which allowed us to derive a representative picture of The correlations between the scales are shown in Table 2. German-speaking inpatients across most of the main medical The Pearson correlation coefficients ranged from .43 to .66. departments. Zinn et al. 5 Table 1. Varimax Rotated Factor Loadings of the Items on the Four Factors. Factors 1 2 3 4 No. Item Doctors Nurses Service Secondary care h 12 I feel that the medical doctors adequately inform me 0.80 0.24 0.17 0.19 0.76 about my treatment 11 The doctors answer my questions during their ward 0.77 0.26 0.17 0.20 0.73 rounds in an informative and friendly manner 13 The doctors are friendly 0.67 0.31 0.19 0.22 0.63 10 Diagnoses are transmitted with a lot of empathy 0.59 0.36 0.20 0.26 0.59 26 I am well informed about the possible complications of 0.49 0.28 0.30 0.28 0.48 my condition that I may face after I leave the hospital 15 My medical care has been successful so far 0.42 0.29 0.21 0.29 0.39 14 My pain has been alleviated efficiently 0.38 0.37 0.20 0.30 0.41 27 On the basis of all of the experiences I have had so far, 0.42 0.42 0.46 0.27 0.63 my overall judgment of the hospital is positive 28 I will recommend the hospital 0.40 0.40 0.47 0.23 0.59 25 I am well prepared to do what I need to do after I 0.39 0.29 0.34 0.29 0.43 leave the hospital 20 My family is well informed 0.37 0.30 0.30 0.30 0.41 6 The daily support of the nursing staff is excellent 0.24 0.75 0.21 0.17 0.69 8 The nursing staff members are very friendly 0.24 0.71 0.20 0.16 0.62 4 The nursing staff members are considerate of the 0.26 0.70 0.19 0.21 0.64 things that worry me or make me sad 5 The nursing staff members are open to my suggestions 0.24 0.66 0.21 0.22 0.59 for how services could be improved 7 The nursing staff members keep me well informed 0.30 0.62 0.22 0.22 0.57 about hospital procedures 9 The nursing staff members respect my privacy 0.27 0.56 0.23 0.26 0.51 3 The general personnel are very friendly upon 0.24 0.44 0.23 0.28 0.38 admission to the hospital 21 Patient rooms are excellent 0.14 0.17 0.73 0.12 0.59 22 All facilities are very hygienic 0.16 0.22 0.70 0.15 0.59 24 The food is good 0.15 0.17 0.40 0.21 0.26 23 The cafeteria is good 0.12 0.12 0.34 0.24 0.20 1 Signposting and directions are very clear 0.18 0.19 0.30 0.30 0.25 18 My experiences with the X-ray department have been 0.17 0.17 0.17 0.77 0.67 good 19 My experiences with other medical services such as 0.23 0.21 0.17 0.71 0.62 ECG or endoscopy have been good 16 Waiting times are not too long 0.23 0.19 0.22 0.54 0.43 17 My experiences with the physiotherapy service are 0.17 0.22 0.14 0.44 0.29 good 2 The forms are easy to understand 0.22 0.27 0.27 0.35 0.32 Items were excluded for psychometric reasons (i.e., either low factor loading or cross-loadings on additional factors). ECG = electrocardiogram. Factor loadings greater than .30 were shown in bold face indicating items’ main loading. In summary, our data spoke in favor of a four-factor solu- with medical doctors’ care, the second factor captured satis- tion of inpatient satisfaction as measured by the GISS. We faction with nursing care, the third factor could be explained found initial support for the validity of the instrument: The as satisfaction with service facilities, and the fourth factor four factors were clearly interpretable and reflected the main captured satisfaction with secondary care facilities. domains of patients’ experiences in a hospital. A substantial The analysis of the subgroups provided an additional valida- amount of variance was explained by the four factors, thus tion of the factor structure because most somatic departments indicating that the model was able to account for most of the showed the same factor structure. This was confirmed not only variability in the data. The first factor described satisfaction by individual exploratory factor analyses, but additional 6 SAGE Open Table 2. First-Order Correlations for the Four Factor scores. Blitstein, 2004) and the modeling of patient ratings at different levels of analysis. For instance, it would be very interesting to Factor 1 Factor 2 Factor 3 Factor 4 see if the derived factor structure is also suitable to assess the quality of care at the level of departments or hospitals. An Doctors Nurses Service Secondary care extension of the present analysis in terms of multilevel factor Factor 1 1 .66 .48 .53 analysis is highly promising and points to an important aspect Factor 2 1 .50 .49 for future research. Factor 3 1 .43 Some shortcomings of the present study need to be borne Factor 4 1 in mind. For example, any questionnaire needs to be vali- M 2.49 2.41 2.78 2.75 dated with regard to more than just its internal structure (e.g., SD 0.65 0.63 0.65 0.71 by using EFA). In addition, external criteria need to be con- Cronbach’s α .89 .90 .72 .80 sidered. For example, is patient satisfaction as measured by the GISS positively associated with “hard” quality indicators such as time between patient admission and surgery? There Table 3. Sample Sizes per Medical Department. are certainly a substantial number of external criteria that Medical department Sample size Percentage of total sample may serve as (convergent or divergent) indicators of the external validity of the GISS. Internal medicine 34,214 29 The analysis of patient satisfaction was conducted at the Surgery 28,062 24 level of individual patients. Further research should examine Other 17,396 15 if the derived factor structure (satisfaction with doctors, Gynecology 11,479 10 nurses, service, and secondary care) is also suitable to assess Orthopedics 6,734 6 the quality of care at the level of departments or hospitals. Urology 5,308 5 The fact that systematic variations in patient satisfaction Neurology 4,574 4 were found for all survey items (ICCs ranged between .06 Oncology 3,518 3 Otolaryngology 3,140 3 and .13) points to an important aspect for future research. Ophthalmology 1,900 2 For the time being, we conclude that the results of the present study provide initial support that the GISS provides an adequate measure of inpatient satisfaction on four dimen- psychometric methodology (ESEM) was also able to back up sions: doctors’ care, nursing care, service facilities, and sec- ondary care. From a practical point of view, care providers this claim—with the exception of patients in ophthalmology may consider using the GISS for satisfaction surveys. More departments. For patients in ophthalmology departments, 14 research is needed to substantiate knowledge about the value items loaded on two or more factors, suggesting that such of the instrument; for example, the nested structure of the patients seem to have a different understanding of their satisfac- data should be analyzed in future studies. Moreover, other tion and what it means. One plausible post hoc explanation for German-speaking countries than Germany should be given this finding is that such patients are in a special situation as their more emphasis in future studies to provide insight whether visual perception is damaged. Thus, their perception of what patient satisfaction differs in those countries. satisfaction means to them is different on an elementary percep- tual level. It is a novel finding that independent research may be Declaration of Conflicting Interests needed for ophthalmology patients but not for other somatic patients. Clearly, more research is needed before definitive con- The author(s) declared no potential conflicts of interest with respect clusions can be drawn. to the research, authorship, and/or publication of this article. The fact that the item responses did not show strong skewness is particularly encouraging because positive skew- Funding ness (i.e., many responses well above a medium level of sat- The author(s) received no financial support for the research and/or isfaction) is a problem for satisfaction questionnaires as authorship of this article. many procedures, including factor analysis, rely on the assumption of normality (Gavra, 1997; Montanari & Viroli, References 2010; Peterson & Wilson, 1992). Aharony, L., & Strasser, S. (1993). Patient satisfaction: What we Finally, it has to bear in mind that the analysis of patient know about and what we still need to explore. Medical Care satisfaction was conducted at the level of individual patients. Review, 50, 49-79. 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A skew-normal factor model for larly predictive modeling) and patient safety management. the analysis of student satisfaction towards university courses. Journal of Applied Statistics, 37, 473-487. Sebastian Sauer has worked as a consultant focusing on healthcare Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design and industry. Currently, he works as a research professor for Industrial analysis of group-randomized trials: A review of recent meth- Psychology at FOM University of Applied Sciences in Munich. His odological developments. American Journal of Public Health, research interests include psychological methods including predic- 94, 423-432. tive modeling, and mindfulness. Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th Richard Göllner is a post-doctoral researcher at the Hector ed.). Los Angeles, CA: Muthén & Muthén. Research Institute of Education Sciences and Psychology in O’Connor, B. P. (2000). SPSS and SAS programs for determin- Tübingen. His main research interests concern the assessment of ing the number of components using parallel analysis and contextual attributes as determinants of individuals’ emotional, Velicer’s MAP test. Behavior Research Methods Instruments social and personal development. & Computers, 32, 396-402. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png SAGE Open SAGE

The German Inpatient Satisfaction Scale: A Large-Scale Survey of Perceived Quality by Inpatients

Zinn, Winfried; Sauer, Sebastian; Göllner, Richard
SAGE Open , Volume 6 (2): 1 – Apr 26, 2016
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Abstract

A patient satisfaction instrument that is based on a large, representative sample does not yet exist in the German language. The objective of this article was to fill this gap by providing initial validation evidence of the German Inpatient Satisfaction Scale as an instrument for German-speaking countries. We performed an exploratory factor analysis and exploratory structural equation modeling in a cross-sectional design. The instrument was administered to N = 116,325 patients in hospitals in German-speaking countries. The overall response rates ranged from 63% to 98%. We found that a four-factor solution fit the data well. The four factors represented satisfaction with doctors’ care, nursing care, service facilities, and secondary care facilities. Cronbach’s alpha ranged from .72 to .90. The findings may be of practical interest for health care providers for measuring patient satisfaction. Keywords patient satisfaction, survey, questionnaire, validation, factor analysis, inpatient The idea of assessing the quality of care from the patient’s as well as for improving treatment. A number of instruments point of view has been a research topic since the 1950s, but it for assessing patient satisfaction have been developed in has recently received a surge in interest (Görtz-Dorten, Breuer, English, including, as a prominent example, the Hospital Hautmann, Rothenberger, & Döpfner, 2011; Hansen et al., Consumer Assessment of Health-Care Providers and Systems 2010; Parsons, 1951). The importance of this area of research (HCAPS; Giordano, Elliott, Goldstein, Lehrman, & Spencer, is threefold. First, patient satisfaction is a health care outcome 2010), but a need for culturally sensitive adaptations has in its own right. Second, satisfied patients are more likely to been identified (Aharony & Strasser, 1993). With regard to show better compliance with treatments. Third, from an orga- German-speaking countries, some patient satisfaction instru- nizational point of view, understanding the causes, correlates, ments exist (Kleeberg et al., 2005), but most of these have and consequences of patient satisfaction with health care ser- been based on relatively small sample sizes that are most vices can help to improve the quality of these services. In sum, likely not representative of the general patient population. patient satisfaction is an element of medical care of increasing Furthermore, studies have largely failed to address importance (S. J. Williams & Calnan, 1991). whether patient satisfaction is homogeneous across different One definition of patient satisfaction can be summarized as a subgroups (e.g., patients in different medical departments). “positive evaluation of distinct dimensions of the health care For a given scale, research must confirm that such subgroups system” (Linder-Pelz, 1982, p. 578). In more detail, patient sat- share a common understanding of patient satisfaction for the isfaction entails beliefs, evaluations, and reactions to the con- common practice of comparing and combining such scores text, process, and results of a health care provider’s service to be meaningful. (Pascoe, 1983). A large number of different approaches that can be used to explain the causes of and the processes behind patient Applied University for Health and Sports, Berlin, Germany satisfaction have been proposed. For example, discrepancy Forschungsgruppe Metrik, Bermuthshain, Germany models have been put forth to explain patient satisfaction as a FOM University of Applied Sciences, Munich, Germany function of perceived quality and expectancy—the smaller the 4 Samueli Institute, Alexandria, VA, USA gap, the higher the resulting satisfaction (B. Williams, 1994). University of Tübingen, Germany Although some insights into the processes behind patient Corresponding Author: satisfaction have been achieved, more questions await Sebastian Sauer, Institute of Business Psychology, FOM University of answers. To this end, valid measurement instruments are a Applied Sciences, Arnulfstr. 32, 80335 Munich, Germany. prerequisite for furthering the understanding of the construct Email: Sebastian.sauer@fom.de Creative Commons CC-BY: This article is distributed under the terms of the Creative Commons Attribution 3.0 License (http://www.creativecommons.org/licenses/by/3.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage). 2 SAGE Open In light of this, the aim of the present study was to present in a number of studies, for example, to evaluate medical care initial validation evidence for an instrument designed to (Clever, Jin, Levinson, & Meltzer, 2008; Hannemann-Weber, measure inpatient satisfaction in German-speaking coun- Kessel, Budych, & Schultz, 2011; Hojat et al., 2011; tries. In addition, we tested whether the factor structure of Zickmund, Hillis, Barnett, Ippolito, & LaBrecque, 2004). the present instrument would be invariant across different Although the need for a validated language-specific patient medical departments. satisfaction questionnaire is obvious, it appears that there is a paucity of measures that were developed with a rigorous methodology for German-speaking countries. It was the aim Current Developments and Research of this article to help fill this gap. We thus hypothesized that Questions the instrument under investigation in the present article, the German Inpatient Satisfaction Scale (GISS), would demon- The use of patient satisfaction surveys became a routine pro- strate adequate psychometric properties. cedure over the course of the 1990s (Di Palo, 1997). One instrument that was developed in 1995 in the United States and that received a substantial amount of interest was the Method HCAPS. As it was extended and refined over the years that followed, the instrument increasingly became a widely Procedure and Sample acknowledged and employed survey instrument (Quigley, Hospital staff members in German-speaking countries Elliott, Hays, Klein, & Farley, 2008). A German version has (Germany, Switzerland, and Austria) collected the data dur- also been presented (Squires et al., 2012). ing the patients’ stay at the respective hospital. Care was A frequent problem encountered with many satisfaction taken so that the questionnaire was distributed after lunch instruments is item skewness, which thus limits the research- and collected before dinner. er’s ability to distinguish between patient satisfaction levels In sum, 143 hospitals participated, out of which 115 (80%) when using the instrument (Sitzia & Wood, 1997). For exam- were from Germany, six (4%) came from the German-speaking ple, many respondents tend to choose good and very good as parts of Switzerland, and 22 (16%) stemmed from Austria. It the answer to a given item. Administered answer options should be noted that the results apply mainly for Germany. In should be designed to allow for enough differentiation in the total, 24% of the hospitals were in private ownership, and the light of such response patterns. One current remedy is to hospitals are spread all over Germany, Switzerland, and include a response category above very good, such as excel- Austria. Some of them belong to same, and some to different lent (Young, Meterko, & Desai, 2000). clinic groups. The sample was not drawn randomly, but the hospitals were approached for participation in the study in the Patient Satisfaction in Different Health course of business partnership with the first author at that time. As an incentive to participate in the study, each hospital Care Systems received feedback about their average level of patient satisfac- As mentioned above, the health care system has a large influ- tion in comparison with the respective mean. Hospitals were ence on the satisfaction of the patients and what the patients allowed to use this feedback for internal quality management can or must be asked about. Most patients in the United purposes. States have to pay for their treatment, and therefore it would Overall, our sample size was N = 116,325 patients. The be appropriate to ask them how they evaluate the price-per- mean number of patients per hospital was 813 (SD = 775). Data formance ratio. In German-speaking countries, the treatment were collected between 2005 and 2009. We took care to access is mostly covered by the mandatory health insurance. a representative sample of all different somatic medical depart- Therefore, different health care systems need different instru- ments. The inclusion criteria were an age of 18 or older, status ments, and the results from one country cannot be transferred as an inpatient in the hospital under investigation when the sur- to another (Aharony & Strasser, 1993). vey was administered, and the ability to understand and respond to the items. An exclusion criterion was admission to a psychi- atric or psychosomatic ward. Patients were informed about the Patient Satisfaction in German- aims of the study and filled out the survey anonymously. Speaking Countries To gauge the degree to which our sample is representative What is known about the situation in German-speaking for the general population, we compared age and sex distribu- countries? For example, the Munich Patient Satisfaction tion of our sample with the general patients’ population based Scale (MPSS-24) is a 24-item scale that was developed with on statistics from the Statistisches Bundesamt (2015). Over and exploratory factor analysis in three small samples (n = 85, above, the figures for the age distribution for our sample and n = 161, n = 91; Möller-Leimkühler et al., 2002); one princi- the overall population were similar (15-44 years: 21% vs. 17%; pal factor was found. The authors reported a high internal 45-64 years: 31% vs. 25%; 65 years and older: 48% vs. 59%). consistency as well as a good reliability; convergent validity As to sex, the figures were identical (GISS sample male: 47%; was found to be satisfactory. In addition, it has been utilized general population 47%). Thus, some support for the notion Zinn et al. 3 that our sample was representative is present. However, more we used an exploratory structural equation modeling characteristics would be needed to back up this claim. approach (ESEM). The ESEM model is estimated separately for each group, and some parameters can be constrained to be invariant across those groups. In the present study, we Questionnaire used multigroup ESEM tests of full measurement invariance To develop the GISS’s initial item set, we included results of EFA factors across all groups. We thus tested whether the from focus groups, expert interviews, published research, factor structure was identical in all subgroups. To test for the patient feedback, as well as the clinical practices of the appropriateness of this invariance assumption, we used com- authors and their colleagues. This process resulted in an ini- mon fit indices for this purpose; that is, we used the com- tial set of 36 satisfaction items. In addition, we asked the parative fit index (CFI), the root mean square error of patients how important the main aspects of the hospital (e.g., approximation (RMSEA), the standardized root mean square doctors, nursing staff, eating) were to them. In a second step, residual (SRMR), and the chi-square test statistic to evaluate the instrument received a revision, which resulted in a reduc- the goodness of fit. The CFI ranges from 0 to 1, with values tion in the number of items. Later, the instrument was greater than .90 and .95 typically taken to reflect acceptable extended by including additional items as a result of analyses and excellent fits to the data, respectively. RMSEA values of of patients’ qualitative comments (Zinn, 2010, p. 177). less than .05 and .08 reflect close and reasonable fits, respec- The present study examined the 28 satisfaction items tively; values between .08 and .10 reflect a moderate fit, and from the GISS. Each item stem was formulated as a semantic values greater than .10 are generally considered unaccept- differential with five response options (1 = the best I ever able. Given normally distributed outcomes and a large sam- experienced, 2 = very good, 3 = good, 4 = acceptable, 5 = ple size, the cutoff value for the SRMR should be close to .07 bad). All items were later reverse-coded so that they would (Hu & Bentler, 1995). Prior to analysis, we examined the reflect satisfaction rather than dissatisfaction. intraclass correlation coefficient (ICC) of all items. The ICC represents the proportion of variance that can be attributed to higher level units (i.e., hospitals). Analysis We used Mplus 5.2 (Muthén & Muthén, 2007) for all analyses. Results Alpha was set at .01. The primary objective was to analyze the psychometric quality of the items by means of exploratory The overall response rate per department for each hospital principal axis factor analysis (EFA). To determine the number ranged from 63% to 98%. The average age was 59.1 years of factors, we used Horn’s parallel analysis (Glorfeld, 1995) (SD = 18.4); 53% of the patients were female. The mean and Velicer’s minimum average partial (MAP) test (O’Connor, length of stay was 7.6 days (SD = 8.2). It is informative to 2000). Horn’s parallel analysis compares the number of compare these figures with German averages: 53% of inpa- extracted factors with the number of factors that would have tients in German hospitals are female, and the mean duration been extracted if the data had been completely random. The is 7.4 days (Statistisches Bundesamt, 2015). focus of the MAP test lies on the relative amounts of system- All items were checked for nonnormality prior to the atic variance remaining in the correlation matrix after extract- analyses. Skewness and kurtosis in the data were small (for ing increasing numbers of factors. For EFA, we decided to use all indicators, skewness ranged from 0.04 to 0.58, kurtosis a varimax rotation. In addition, more basic criteria (the from 0.27 to 0.73). A robust Maximum Likelihood (MLR) screeplot “elbow”; the Kaiser criterion, that is, the number of estimation was employed as the estimation method for the eigenvalues > 1) were considered. Following, descriptive sta- analysis. Due to only small skewness and kurtosis in the tistics were calculated to describe means, standard deviations, data, (largely) unbiased model fit estimates could be expected and reliabilities of derived factor scores. In addition, the large (West, Finch, & Curran, 1995). sample size allowed for a more fine-grained analysis on the The overall score for the 28 satisfaction items was between level of medical departments. The following 10 departments 2.10 and 2.96 (on a scale ranging from 1 = best I ever experi- were included for this purpose: surgery, internal medicine, enced to 5 = bad). The ICC coefficients for the items ranged gynecology, orthopedics, ophthalmology, otolaryngology from .06 to .13, so that 6% to 13% of the variance in patients’ (ear/nose/throat), urology, neurology, oncology, and other. We ratings was explained by the difference in hospitals. These used full information maximum likelihood (FIML) estimation results indicate that a relatively small percentage of item vari- in Mplus (Version 7.1) to deal with missing data. In the meth- ances was attributable to variations among hospitals. odological literature on missing data (Schafer & Graham, 2002), there is a growing consensus that FIML estimations EFA (and other procedures, for example, multiple imputation) are preferable to casewise or listwise deletion. An examination of the screeplot indicated a one-factor solu- In addition to factor analysis, to test whether the factor tion with an eigenvalue of 12.91 (see Figure 1). By contrast, solution held across all subgroups of medical departments, the Kaiser criterion favored a four-factor solution as four 4 SAGE Open Factor Structure in Subgroups Next, we were interested in whether the factor structure would be invariant across different medical departments. To compare patient satisfaction across different departments, it is necessary that the factor structure be identical or at least similar across the subgroups (see Table 3 for sample sizes in different medical departments). For eye patients, the factor structure differed from the rest of the medical departments; thus, we removed eye patients from the ESEM. As the factor score results showed moderate but substantial correlations, we decided to use an oblimin rotation for the ESEM analysis. The nine-group model with no invariance constraints provided a good fit to the data, χ (2448) = 88495.1, RMSEA = .05, CFI = .94, Tucker–Lewis index (TLI) = .92, and SRMR = .03. These results supported Figure 1. Screeplot for the GISS factors. the configural invariance of the four proposed factors, mean- Note. GISS = German Inpatient Satisfaction Scale. ing that the same factor structure was able to fit the data for each group. Next, we constrained the factor loadings, factor intercepts, and item uniqueness to be invariant across the nine factors had eigenvalues > 1 (eigenvalues: Factor 1: 12.91; groups. To do this, 84 new constraints were added. A lack of Factor 2: 1.63; Factor 3: 1.31; Factor 4: 1.15). support for this model would suggest that the measurement On a more fine-grained level, Horn’s parallel analysis model was not comparable across the nine groups. The differ- (see Figure 1) and the MAP test were employed on the basis ences in the fit indices were small, χ (3744) = 106438.0, of indicator correlations. Both parallel analysis and the MAP RMSEA = .046, CFI = .927, TLI = .934, and SRMR = .041. test indicated four factors. Given these results, we decided to Although the chi-square value for the configural invariance force four factors in the factor analysis. model was significantly smaller than that of the full invari- The four-factor solution showed a clearly interpretable ance model (due to the large sample size), the chi-square/df structure. The results can be found in Table 1. For better ratio was substantially smaller. The small changes in the fit readability, values above .3 are printed in bold. The four fac- indices supported interpretations of invariance (Cheung & tors explained 61% of the variance (Factor 1: 19%; Factor 2: Rensvold, 2001) and provided clear evidence for the compa- 18%; Factor 3: 12%; Factor 4: 12%). The first factor could rability of factor solutions across all of the patient groups. tentatively be called satisfaction with medical doctors’ care. Taken together, the results of the ESEM showed that the The second factor described satisfaction with nursing care; factor structure was similar across all medical departments the third factor indicated satisfaction with service facilities with the exception of ophthalmology. Ophthalmology (e.g., food, patient rooms, cafeteria), and the fourth factor patients seemed to have a different understanding of the captured satisfaction with secondary care facilities (e.g., meaning of patient satisfaction compared with all other physiotherapy, X-rays). Some of the items were excluded somatic departments (including neurology). For ophthalmol- due to cross-loadings or relatively small factor loadings. ogy patients, we found that several items loaded on two After dropping seven items, 21 of the initial 28 items factors. remained, showing a clear factor structure (see Table 1). In sum, we conclude that the results of the ESEM analysis could be taken as additional support for the validity of the Internal Consistencies of Factor Scores instrument. On the basis of the established factor solution, we further analyzed the psychometric properties of the factor scores. Discussion The internal consistency of the first factor (satisfaction with medical doctors’ care) was high. For the second scale (satis- The overall goal of the present study was to present initial faction with nursing care), the reliability was again high. validation evidence for an instrument measuring inpatient Excluding any one item increased alpha by only .01. The satisfaction in German-speaking countries (the GISS). In alpha value for the third scale (service facilities) was accept- addition, we tested whether the factor structure of the GISS able again; excluding any of the items did not increase alpha. would remain invariant across different medical departments. For the fourth scale (secondary care services), alpha was The particular strength of the study was its large-scale scope, again good. Excluding any one item did not increase alpha. which allowed us to derive a representative picture of The correlations between the scales are shown in Table 2. German-speaking inpatients across most of the main medical The Pearson correlation coefficients ranged from .43 to .66. departments. Zinn et al. 5 Table 1. Varimax Rotated Factor Loadings of the Items on the Four Factors. Factors 1 2 3 4 No. Item Doctors Nurses Service Secondary care h 12 I feel that the medical doctors adequately inform me 0.80 0.24 0.17 0.19 0.76 about my treatment 11 The doctors answer my questions during their ward 0.77 0.26 0.17 0.20 0.73 rounds in an informative and friendly manner 13 The doctors are friendly 0.67 0.31 0.19 0.22 0.63 10 Diagnoses are transmitted with a lot of empathy 0.59 0.36 0.20 0.26 0.59 26 I am well informed about the possible complications of 0.49 0.28 0.30 0.28 0.48 my condition that I may face after I leave the hospital 15 My medical care has been successful so far 0.42 0.29 0.21 0.29 0.39 14 My pain has been alleviated efficiently 0.38 0.37 0.20 0.30 0.41 27 On the basis of all of the experiences I have had so far, 0.42 0.42 0.46 0.27 0.63 my overall judgment of the hospital is positive 28 I will recommend the hospital 0.40 0.40 0.47 0.23 0.59 25 I am well prepared to do what I need to do after I 0.39 0.29 0.34 0.29 0.43 leave the hospital 20 My family is well informed 0.37 0.30 0.30 0.30 0.41 6 The daily support of the nursing staff is excellent 0.24 0.75 0.21 0.17 0.69 8 The nursing staff members are very friendly 0.24 0.71 0.20 0.16 0.62 4 The nursing staff members are considerate of the 0.26 0.70 0.19 0.21 0.64 things that worry me or make me sad 5 The nursing staff members are open to my suggestions 0.24 0.66 0.21 0.22 0.59 for how services could be improved 7 The nursing staff members keep me well informed 0.30 0.62 0.22 0.22 0.57 about hospital procedures 9 The nursing staff members respect my privacy 0.27 0.56 0.23 0.26 0.51 3 The general personnel are very friendly upon 0.24 0.44 0.23 0.28 0.38 admission to the hospital 21 Patient rooms are excellent 0.14 0.17 0.73 0.12 0.59 22 All facilities are very hygienic 0.16 0.22 0.70 0.15 0.59 24 The food is good 0.15 0.17 0.40 0.21 0.26 23 The cafeteria is good 0.12 0.12 0.34 0.24 0.20 1 Signposting and directions are very clear 0.18 0.19 0.30 0.30 0.25 18 My experiences with the X-ray department have been 0.17 0.17 0.17 0.77 0.67 good 19 My experiences with other medical services such as 0.23 0.21 0.17 0.71 0.62 ECG or endoscopy have been good 16 Waiting times are not too long 0.23 0.19 0.22 0.54 0.43 17 My experiences with the physiotherapy service are 0.17 0.22 0.14 0.44 0.29 good 2 The forms are easy to understand 0.22 0.27 0.27 0.35 0.32 Items were excluded for psychometric reasons (i.e., either low factor loading or cross-loadings on additional factors). ECG = electrocardiogram. Factor loadings greater than .30 were shown in bold face indicating items’ main loading. In summary, our data spoke in favor of a four-factor solu- with medical doctors’ care, the second factor captured satis- tion of inpatient satisfaction as measured by the GISS. We faction with nursing care, the third factor could be explained found initial support for the validity of the instrument: The as satisfaction with service facilities, and the fourth factor four factors were clearly interpretable and reflected the main captured satisfaction with secondary care facilities. domains of patients’ experiences in a hospital. A substantial The analysis of the subgroups provided an additional valida- amount of variance was explained by the four factors, thus tion of the factor structure because most somatic departments indicating that the model was able to account for most of the showed the same factor structure. This was confirmed not only variability in the data. The first factor described satisfaction by individual exploratory factor analyses, but additional 6 SAGE Open Table 2. First-Order Correlations for the Four Factor scores. Blitstein, 2004) and the modeling of patient ratings at different levels of analysis. For instance, it would be very interesting to Factor 1 Factor 2 Factor 3 Factor 4 see if the derived factor structure is also suitable to assess the quality of care at the level of departments or hospitals. An Doctors Nurses Service Secondary care extension of the present analysis in terms of multilevel factor Factor 1 1 .66 .48 .53 analysis is highly promising and points to an important aspect Factor 2 1 .50 .49 for future research. Factor 3 1 .43 Some shortcomings of the present study need to be borne Factor 4 1 in mind. For example, any questionnaire needs to be vali- M 2.49 2.41 2.78 2.75 dated with regard to more than just its internal structure (e.g., SD 0.65 0.63 0.65 0.71 by using EFA). In addition, external criteria need to be con- Cronbach’s α .89 .90 .72 .80 sidered. For example, is patient satisfaction as measured by the GISS positively associated with “hard” quality indicators such as time between patient admission and surgery? There Table 3. Sample Sizes per Medical Department. are certainly a substantial number of external criteria that Medical department Sample size Percentage of total sample may serve as (convergent or divergent) indicators of the external validity of the GISS. Internal medicine 34,214 29 The analysis of patient satisfaction was conducted at the Surgery 28,062 24 level of individual patients. Further research should examine Other 17,396 15 if the derived factor structure (satisfaction with doctors, Gynecology 11,479 10 nurses, service, and secondary care) is also suitable to assess Orthopedics 6,734 6 the quality of care at the level of departments or hospitals. Urology 5,308 5 The fact that systematic variations in patient satisfaction Neurology 4,574 4 were found for all survey items (ICCs ranged between .06 Oncology 3,518 3 Otolaryngology 3,140 3 and .13) points to an important aspect for future research. Ophthalmology 1,900 2 For the time being, we conclude that the results of the present study provide initial support that the GISS provides an adequate measure of inpatient satisfaction on four dimen- psychometric methodology (ESEM) was also able to back up sions: doctors’ care, nursing care, service facilities, and sec- ondary care. From a practical point of view, care providers this claim—with the exception of patients in ophthalmology may consider using the GISS for satisfaction surveys. More departments. For patients in ophthalmology departments, 14 research is needed to substantiate knowledge about the value items loaded on two or more factors, suggesting that such of the instrument; for example, the nested structure of the patients seem to have a different understanding of their satisfac- data should be analyzed in future studies. Moreover, other tion and what it means. One plausible post hoc explanation for German-speaking countries than Germany should be given this finding is that such patients are in a special situation as their more emphasis in future studies to provide insight whether visual perception is damaged. Thus, their perception of what patient satisfaction differs in those countries. satisfaction means to them is different on an elementary percep- tual level. It is a novel finding that independent research may be Declaration of Conflicting Interests needed for ophthalmology patients but not for other somatic patients. Clearly, more research is needed before definitive con- The author(s) declared no potential conflicts of interest with respect clusions can be drawn. to the research, authorship, and/or publication of this article. The fact that the item responses did not show strong skewness is particularly encouraging because positive skew- Funding ness (i.e., many responses well above a medium level of sat- The author(s) received no financial support for the research and/or isfaction) is a problem for satisfaction questionnaires as authorship of this article. many procedures, including factor analysis, rely on the assumption of normality (Gavra, 1997; Montanari & Viroli, References 2010; Peterson & Wilson, 1992). Aharony, L., & Strasser, S. (1993). Patient satisfaction: What we Finally, it has to bear in mind that the analysis of patient know about and what we still need to explore. Medical Care satisfaction was conducted at the level of individual patients. Review, 50, 49-79. 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A skew-normal factor model for larly predictive modeling) and patient safety management. the analysis of student satisfaction towards university courses. Journal of Applied Statistics, 37, 473-487. Sebastian Sauer has worked as a consultant focusing on healthcare Murray, D. M., Varnell, S. P., & Blitstein, J. L. (2004). Design and industry. Currently, he works as a research professor for Industrial analysis of group-randomized trials: A review of recent meth- Psychology at FOM University of Applied Sciences in Munich. His odological developments. American Journal of Public Health, research interests include psychological methods including predic- 94, 423-432. tive modeling, and mindfulness. Muthén, L. K., & Muthén, B. O. (2007). Mplus user’s guide (5th Richard Göllner is a post-doctoral researcher at the Hector ed.). Los Angeles, CA: Muthén & Muthén. Research Institute of Education Sciences and Psychology in O’Connor, B. P. (2000). SPSS and SAS programs for determin- Tübingen. His main research interests concern the assessment of ing the number of components using parallel analysis and contextual attributes as determinants of individuals’ emotional, Velicer’s MAP test. Behavior Research Methods Instruments social and personal development. & Computers, 32, 396-402.

Journal

SAGE Open – SAGE

Published: Apr 26, 2016

Keywords: patient satisfaction; survey; questionnaire; validation; factor analysis; inpatient

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