geront      Gerontologistgeront      The Gerontologist      The Gerontologist      0016-9013      1758-5341              Oxford University Press                    23810.1093/geront/46.2.238                        ASSISTED LIVING                            An Empirical Typology of Residential Care/Assisted Living Based on a Four-State Study                                          Park            Nan Sook                    PhD                      1                                                          Zimmerman            Sheryl                    PhD                      2                                3                                                Sloane            Philip D.                    MD                      2                                4                                                Gruber-Baldini            Ann L.                    PhD                      5                                                Eckert            J. Kevin                    PhD                      6                                      Address correspondence to Nan Sook Park, PhD, School of Social Work, The University of Alabama, Box 870314, Tuscaloosa, AL 35487-0314. E-mail: npark@bama.ua.edu                    4        2006            46      2      238      248                        24          10          2005                          21          3          2005                            Copyright 2006 by The Gerontological Society of America        2006                          Purpose: Residential care/assisted living describes diverse facilities providing non–nursing home care to a heterogeneous group of primarily elderly residents. This article derives typologies of assisted living based on theoretically and practically grounded evidence.Design and Methods: We obtained data from the Collaborative Studies of Long-Term Care, which examined 193 assisted living facilities in four states: Florida, Maryland, New Jersey, and North Carolina. By using mixture modeling, we derived typologies in five different ways, based on: structure; process; resident case-mix; structure and process; and structure, process, and resident case-mix. Results: Although configurations of typologies varied as a function of criterion variables used, common themes emerged from different cluster solutions. A typology based on resident case-mix yielded a five-cluster solution, whereas a typology based on structure, process, and resident case-mix resulted in six distinct clusters. Medicaid case-mix/psychiatric illness and high resident impairment were two clusters identified by both strategies. Implications: Because of the wide variation in structure, residents, and services within assisted living facilities, typologies such as those described here may be useful in clinical practice, research, and policy. To the extent that public payment defines its own cluster, the potential for inequities in care merits careful attention.                    Typology        Residential care        Assisted living        Cluster analysis        Long-term care                              hwp-legacy-fpage          238                          hwp-legacy-dochead          RESEARCH ARTICLE                                      Residential care/assisted living (RC/AL) facilities have become a popular housing option for older Americans. It is estimated that there are more than 35,000 RC/AL facilities in the United States, which provide services to as many as 1 million Americans (Ball et al., 2000); however, the number varies depending on how RC/AL is defined (Hawes, Phillips, Rose, Holan, & Sherman, 2003). Although RC/AL facilities share the commonality of housing people who need assistance in non-nursing home settings, they provide extremely diverse care to a heterogeneous group of people. For example, facilities range in size from a few beds to hundreds of beds; serve residents across a wide spectrum of ages, functional abilities, and health problems; and provide services that vary widely in comprehensiveness (Zimmerman, Sloane, & Eckert, 2001). Because the range of variability is so much broader for RC/AL than for nursing homes, it is difficult to talk about these facilities as if they were monolithic, which in part hinders research efforts (Golant, 2004; Zimmerman et al., 2003).      Developing a typology is especially useful when a group is heterogeneous and when classification systems have not been established (Everitt, Landau, & Leese, 2001), which is certainly the case with RC/AL. Typology development involves a process in which the group is classified into meaningful subgroups that share similar characteristics. In fact, typologies have already been used to identify common patterns in long-term care, allowing researchers to describe and understand the configurations of settings that commonly exist for older adults (Lawton, 2001; Timko & Moos, 1991). The majority of these studies, however, are limited to classifying nursing homes, particularly dementia care units (Davis et al., 2000; Gold, Sloane, Mathew, Bledsoe, & Konanc, 1991; Grant, 1998; Grant, Kane, & Stark, 1995; Holmes & Teresi, 1994; Sloane, Lindeman, Phillips, Moritz, & Koch, 1995). A few studies have addressed RC/AL settings (e.g., Hawes, Phillips, & Rose, 2000; Timko & Moos), but none have studied the entire range of RC/AL across multiple states.      In developing and describing typologies, researchers have commonly employed the structure–process–outcome framework (Donabedian, 1978, 1980, 1988). In this framework, structure refers to the capacity of the facility to provide care, which includes physical amenities (e.g., safety features) and human resources (e.g., the staff-to-resident ratio). Process is how the facility delivers care, which encompasses the activities that occur between care providers and residents (e.g., the provision of organized activity programs). Outcome is the effect of care received. Davis and colleagues (2000) evaluated specialized dementia programs in RC/AL settings based on two structure-related variables: facility size (i.e., small vs large) and administrative relationships with other facilities (i.e., affiliation with other RC/AL facilities); they derived five types. Hawes and colleagues (2000) classified a national sample of 1,251 larger RC/AL facilities based on reported consumer preferences for two process-related characteristics (i.e., privacy and service); they identified four types. Although both of these studies were able to parsimoniously present similarities and differences across clusters, they were limited by a focus on few variables to differentiate the types of care settings.      One component of care that both studies overlooked was resident case-mix, which is likely to be closely related to the structure of care (Wunderlich, Sloan, & Davis, 1996). For instance, facilities consisting of high proportions of residents who are functionally dependent may require more resources and staff than those caring for less impaired individuals. Indeed, Timko and Moos (1991) examined the configurations of different facilities for older adults from a national sample of 235 nursing homes, RC/AL facilities, and congregate-care apartments. They found that both facility characteristics (e.g., level of care, ownership, and size) and resident case-mix (e.g., social resources, functional ability, and gender) contributed to the social climate of facilities. They contended, in essence, that resident case-mix gauges the overall demands on the staff, which in turn affects facility dynamics.      As RC/AL prospered under the recent building boom (i.e., it has accounted for more than 80% of new projects in the senior-housing industry), the heterogeneity in the field has increased (Adler, 1998). Consequently, as recognized by a national task force, it has become increasingly difficult to even agree on a definition of RC/AL (Assisted Living Workgroup, 2003). Being able to delineate the subtypes of facilities that exist should facilitate understanding, discussion, research, policy, and practice. Therefore, this article derives typologies of RC/AL by using criteria from structure, process, and resident case-mix domains. In doing so, it uses a more comprehensive set of variables than has been applied in the past. We used mixture modeling, a special case of cluster analysis developed in the latent variable framework, in order to classify facilities, and we evaluated resultant typologies based on statistical, theoretical, and practical significance.              Methods              Sample and Design        We derived data for this study from the Collaborative Studies of Long-Term Care (CS–LTC), which was funded by the National Institutes of Health to describe the structure, process, and outcomes of care across a diversity of RC/AL settings (Zimmerman et al., 2001). The CS–LTC broadly defined RC/AL as “facilities or discrete portions of facilities, licensed by the state at a non-nursing-home level of care, that provide room, board, 24-hour oversight, and assistance with activities of daily living” (Zimmerman et al., 2001, p. 120). This is the most inclusive and most commonly accepted definition of RC/AL.        The CS–LTC enrolled 2,078 residents in 193 facilities from representative regions across four states: Florida, Maryland, New Jersey, and North Carolina. Baseline data were collected from October 1997 to November 1998. After consultation with national experts and a review of state regulations and current research, the CS–LTC selected these states in consideration of geographical proximity (to enable onsite data collection) and variability (their RC/AL policies reflected the diversity of the field). A purposive sample of counties (sampling regions) was selected within each state based on three criteria: (a) at least 15% of each type of the state's RC/AL facilities were in the region; (b) rural and urban diversity was reflected in terms of demographic and health-service characteristics; and (c) the region represented the state.        To ensure that all facility types meeting the definition of RC/AL were included, the CS–LTC stratified facilities into three types: facilities with fewer than 16 beds, traditional “board-and-care” facilities with 16 or more beds, and “new-model” facilities with 16 or more beds. New-model facilities with 16 or more beds were defined through a pilot study as having been built after January 1, 1987, and having either two or more different private-pay monthly rates, 20% or more residents requiring assistance in transfer, 25% or more residents who are incontinent daily, or either a registered nurse or licensed practical nurse on duty at all times (Zimmerman et al., 2003). A total of 113 smaller facilities and 40 of each of the other facility types were included in the CS–LTC. Administrators of the 193 facilities were interviewed onsite to gather facility-level information. Detailed information about CS–LTC methods can be obtained elsewhere (Zimmerman et al., 2001).                    Variables Used in Typology Development        The CS–LTC offered rich information with which to develop typologies. We organized the variables under study according to the domains of structure, process, and resident case-mix. Data were not available to incorporate outcomes in the analyses. Table 1 presents definitions of the facility-level variables used in these analyses.        We included the following facility-level measures of structure in these analyses: facility size (bed capacity); years in operation; ownership (proprietary vs not for profit); affiliation with other facilities (RC/AL facilities, nursing homes, hospitals, or continuing care retirement communities); staffing (presence/absence of registered nurse or licensed practical nurse, total number of licensed nursing hours or aide-care hours, and aide turnover rate); percentage of private rooms; and environmental quality. We measured the latter by using the Therapeutic Environment Screening Survey for Residential Care (Sloane, Zimmerman, & Walsh, 2001), an observational instrument from which a composite indicator of quality, the Assisted Living Environmental Quality Scale, can be derived. The Assisted Living Environmental Quality Scale summarizes environmental quality with respect to several domains, such as maintenance, safety, cleanliness, and privacy. In addition, neighborhood attractiveness was a rating of whether the surrounding area was in good or bad repair/maintenance.        We evaluated process of care by using eight scales from the Policy and Program Information Form (POLIF) of the Multiphasic Environmental Assessment Procedure (Moos & Lemke, 1996). POLIF has been used in other studies, which have found it to discriminate between different long-term care settings and the types of residents they serve (Zimmerman et al., 2001). The eight POLIF scales included in these analyses evaluated admission policies, acceptance of problem behaviors, policy choice, policy clarity, provision of privacy, resident control, overall provision of services, and availability of social and recreational activities. The range of Cronbach's alphas for the subscales was.69–.84, showing moderately high internal consistency of the items (Moos & Lemke; Zimmerman et al., 2003). We scored each POLIF scale as a range from 0%–100%, with higher percentages indicating endorsement of more items. The analyses in this article excluded two measures (provision of health services and admission policies specific to ADL functioning), which were subsets of other measures, and so were highly correlated.        We used six variables describing a range of resident characteristics and case-mix in typology development: percentage of residents on Medicaid; percentage of residents who required assistance taking care of their own appearance; percentage of residents who needed help getting in and out of bed; percentage of residents who had been diagnosed with dementia; percentage of residents who had a diagnosis of mental or psychiatric illness; and percentage of residents who had behavioral symptoms. An administrator for the whole facility reported these variables (i.e., we did not derive them from resident-level data).                    Analyses        We first examined the distribution of individual variables (e.g., mean, standard deviation) and patterns of missing values. Next, we conducted mixture cluster analyses (Muthén & Muthén, 1998–2001) separately for structure (S), process (P), and resident case-mix (R). Subsequently, we used a combination of structure and process variables as the criterion variables (SP), and, lastly, we ran a combination of structure, process, and resident case-mix (SPR) for the final model. We examined the configurations of clusters (i.e., which clusters were comparable to others and which variables were driving the differences) and fit indices at each step.        Mixture modeling (i.e., mixture cluster analysis), a special case of cluster analysis, presumes that “unobserved heterogeneity” in the sample explains variability among observed variables (Muthén, 2001; Muthén & Muthén, 1998–2001). The heterogeneity is assumed to be latent or unobserved, hence to be inferred from the data. Applying this assumption to the current study, we can say that the diversity of RC/AL is due to the unobserved heterogeneity of the settings that may be explained through the chosen criterion variables from structure, process, and resident case-mix domains.        The assumptions and procedures of cluster analysis resemble exploratory factor analysis in that both approaches can be used as data-reduction techniques. The notable difference is that factor analysis takes a variable-centered approach, whereas cluster analysis takes a case-centered approach (Muthén & Muthén, 2000). Mixture cluster analysis assumes that latent variables are categorical, whereas observed variables can be either categorical or continuous; this assumption is suited to the current study. Mixture cluster analysis is also versatile in terms of handling missing data, whereas missing values are problematic in traditional cluster analysis. That is, mixture modeling using the Mplus program uses all observations available in the data and estimates the parameters through a maximum likelihood estimator (Muthén & Muthén, 1998–2001). In the present analyses, we determined the best-cluster solutions through the largest loglikelihood values using expectation-maximization algorithms, highest entropy (i.e., an index of classification quality), and lowest Bayesian Information Criterion (BIC) values (Muthén, 2001; Muthén & Muthén, 1998–2001; Schwartz, 1978), as well as high posterior probabilities (i.e., probabilities for cases to be in their respective class; Muthén & Muthén, 2000).        Once we had classified the facilities into clusters based on each domain of criterion variables (S, P, or R) and on a combination of the domains (SP or SPR), we used chi-square tests and one-way analysis of variance (ANOVA) in order to examine associations between cluster types by facility and resident characteristics. In ANOVA, we made additional tests to compare groups on a post-hoc basis. We conducted paired comparisons by using Fisher's least significant difference procedure (where F tests were significant at α =.05).        Finally, we examined patterns of cluster membership across the resultant five typologies (e.g., whether facilities belonging to the same cluster in the SP typology stayed together in the SPR typology). We calculated kappa statistics among different combinations of the five typologies, reflecting the degree to which different observers or, in this case, methods, classify a particular case in the identical category (Stokes, Davis, & Koch, 2000). We performed mixture cluster analyses by using Mplus 2.1 (Muthén & Muthén, 1998–2001), chi-square tests and ANOVA by using SPSS 11 (SPSS, 1991–2000), and kappa statistics by using SAS 8.2 (SAS Institute, 1999–2001).                    Results              Descriptive Statistics        Table 2 shows the means and standard deviations or percentages of the 26 variables used for typology development. We examined missing patterns across variables and facilities; we excluded 4 facilities with excessive missing data (i.e., more than 70% of the values missing) from the analyses, resulting in 189 facilities for which data were available. Facilities that were included (n = 189) and excluded (n = 4) were not significantly different with respect to facility size, years in business, proprietary status, affiliation with other facilities, percentage of private rooms, environmental quality, or resident-level functional and cognitive impairment, psychiatric illness, or behavioral symptoms.        Study facilities had an average of 28 beds (range 4–190, the mean being reduced by the disproportionate number of smaller facilities) and had been in business for an average of 14 years. The majority of the facilities were for profit (83%), and roughly 24%–33% were affiliated with other facilities or operated as part of a chain with other RC/AL facilities. About one half of the sample had a registered nurse or licensed practical nurse on duty, who worked an average of two hours per resident per week. In addition, personal care aides worked roughly 11 hours per resident per week. Annual aide turnover was 69%. More than one half of the rooms were private (66%), and most of the facilities (82%) were located in attractive or very attractive neighborhoods. Facilities varied in their environmental quality scores (range 5–26), with an average score of approximately 17.        Facilities tended to have few restrictions for admitting residents; however, some were reluctant to admit those with behavior problems. Generally, facilities scored moderately on policies involving individual freedom, institutional order, and social/recreational activities. With respect to resident case-mix, approximately 15% of residents across facilities were on Medicaid. More residents required help in taking care of themselves than they did transferring (47% vs 16%, respectively). A small proportion of residents had a history of mental illness (12%) or required attention because of behavioral symptoms (9%). Administrators reported that, on average, one third of their residents had dementia.                    Typologies        We derived typologies by using five different combinations of criterion variables based on structure (S); process (P); resident case-mix (R); structure and process (SP); and structure, process, and resident case-mix (SPR). Although each approach derived distinct clusters of RC/AL depending on the criterion variables, the S, P, and SP models proved of limited utility because their results were dominated by facility size. Therefore, we present here only the R and SPR models, because they provide more unique and comprehensive information than the S, P, and SP models. Further, the kappa value between the R typology and SPR typology was 0.26, suggesting slight agreement (p <.01) and the utility of displaying both.                  Cluster Solutions Based on Resident Case-Mix Characteristics (R)          Table 3 shows the means and standard deviations of the variables in the five clusters that were derived from the six facility-level resident characteristics, as well as the results of ANOVA or chi-square tests for each variable. Fit measures of mixture modeling favored a five-cluster solution in that values of loglikelihood, BIC, entropy, and average posterior probabilities indicated reasonably good fit compared with other cluster solutions (log likelihood = −4,633; BIC = 9,475; entropy = 0.87). The five clusters were distinguishable by their criterion variables (i.e., resident case-mix); accordingly, all ANOVA tests for the criterion variables were highly significant at p <.05. Four variables in the structure domain (i.e., years in business, nursing, environment, and privacy) were differentiated by this clustering as well, as were two variables in the process domain (i.e., overall admission policies and privacy). We describe each cluster briefly here:          Cluster R-1 (22 facilities) was distinguished by a high proportion of residents on Medicaid and with mental/psychiatric illness, a moderate level of residents who required self-care assistance, poorer environmental quality, and a lower level of privacy.          Cluster R-2 (25 facilities) was characterized by high proportions of physically impaired residents (53% requiring help in transfer, compared with 4%–28% in other facility types). Although it was not significant, dementia was common (61%), but the rate for behavioral symptoms was low (6%).          Cluster R-3 (8 facilities) contained a very high proportion of residents with behavioral symptoms (81%, compared with less than 10% in all other clusters), many of whom had dementia (64%). Also, 28% of residents in this cluster required help in transfer. There were more licensed nursing hours in this cluster than in any other (5.2 hours/resident/week vs 0.70–2.05 hours/resident/week).          Cluster R-4 (59 facilities) housed residents who were significantly less impaired in physical function (12% and 4% required help in self care and transfer, respectively, vs 44%–80% and 11%–53%, respectively, in other clusters). Facilities in this cluster had the strictest admission policies (60% vs 76%–85% in other facilities), tended to be older, and provided more privacy (nonsignificant).          Cluster R-5 (72 facilities) tended to be mid range across most variables.                          Cluster Solutions Based on Structure, Process, and Resident Case-Mix Characteristics (SPR)          A six-cluster solution was the best fitting model for the SPR analyses (log likelihood = −16,231; BIC = 33,416; entropy = 0.95). Table 4 shows this cluster solution, including means and standard deviations of variables by cluster type and results of test statistics and least significant difference multiple comparisons. We describe each cluster briefly here:          Cluster SPR-1 (14 facilities) offered the fewest services (37% vs 48%–71% in the other clusters, p <.05). Facilities in this cluster scored moderately high in the provision of privacy (65% vs 40%–50% in four of the other clusters). These facilities tended to be older, and residents in this cluster were the least functionally impaired, although differences were not statistically significant.          Cluster SPR-2 (25 facilities) was characterized by higher percentages of individuals on Medicaid (68% vs 3%–49% in other clusters) and with a history of mental and psychiatric illness (19%, which was higher than all clusters except Cluster SPR-4).          Cluster SPR-3 (57 facilities) had a higher proportion of residents with functional, cognitive, and behavioral impairments than any other cluster. Privacy and service provision were moderately high.          Cluster SPR-4 (54 facilities) tended to include large facilities (mean bed capacity = 66 vs 9–16 for other clusters). These facilities had the highest environmental quality score (21 vs 13–16 for other clusters) and scored highest in terms of policy choice, privacy, services, and social activities.          Cluster SPR-5 (7 facilities) had the highest aide turnover rates (600% vs 37%–125% in other clusters), reported the highest proportion of residents with mental illness, and had many residents on Medicaid.          Cluster SPR-6 (32 facilities) was not statistically different than other clusters in any one variable, but it did have the fewest licensed nursing hours, lowest aide turnover, least policy clarity, least resident control, and lowest Medicaid case-mix.                            Discussion      We examined a large, diverse, multistate sample of RC/AL facilities in order to determine the extent to which the facilities could be clustered into distinct subtypes based on facility-level structure (S), process (P), and resident case-mix (R) characteristics. This study is one of the few that has developed typologies focusing on RC/AL, and the only one that includes such a broad and theoretically and empirically justified set of variables. The four states under study (Florida, Maryland, New Jersey, and North Carolina) were well-suited to this exploration because they all have RC/AL regulations that refer to two or more types of housing and service, and they all provide Medicaid funding (Mollica, 2001). The four states also exhibited relevant differences in that New Jersey was the most advanced in articulating regulations and allowing for extensive services and retention, whereas Maryland was just beginning to develop regulations at the time of data collection (Zimmerman et al., 2001).      Facility size was an overriding factor in three of the five subtypes (S, P, and SP). Much has been written about the importance of facility size in the field of RC/AL, with key concepts including the homelike-ness of “mom and pop” styles of care and the dangers of impersonalization and institutionalization in larger models of care (Kane & Wilson, 2001; Morgan, Gruber-Baldini, Eckert, & Zimmerman, 2004). Thus, in order to better understand the RC/AL field, we endeavored to identify criterion variables other than facility size. The typology based on the resident case-mix (R) variables was successful in this regard, identifying five distinctly different facility clusters involving a large variety of variables. Variables that significantly differentiated the clusters included facility age, nursing care, environmental quality, privacy, and resident case-mix (i.e., Medicaid, self-care, transfer, dementia, mental/psychiatric illness, and behavioral symptoms). Clearly, the analytic process was successful in differentiating types of facilities based on the types of residents they serve. Furthermore, there is some indication that the structure and process of care within clusters was consistent with resident needs and/or the challenges they presented, indicating that RC/AL facilities were organized around both resident need and market conditions. For example, in Cluster R-3, high cognitive impairment and behavioral symptoms were associated with more nursing care; in Cluster R-4, low resident impairment was associated with high privacy (structure and process) and stricter admission policies (process).      Two very similar clusters were identified using both the R and the SPR strategies: high Medicaid, high mental/psychiatric illness; and high resident ADL impairment. Unfortunately, findings of the present study also indicate that high Medicaid, high mental/psychiatric illness was paired with a poor physical environment and low privacy, raising issues about a link between public costs and quality of care. Although scholars argue that higher quality is not necessarily associated with higher costs (Davis, 1991; Mukamel & Spector, 2000), the current findings suggest that this may not be the case with regard to environmental quality. Thus, older adults who are economically disadvantaged may be forced to live in poorer physical environments with limited options. However, these data do not report the actual experiences and outcomes of care in such a cluster. Although one study did report a higher risk of hospitalization in facilities with poorer environments (Zimmerman, Gruber-Baldini, Hebel, Sloane, & Magaziner, 2002), further research is necessary to characterize the linkage between environmental quality and other outcomes.      Another notable finding about the SPR model is that the large facilities formed a cluster that carried with it better scores on physical environment quality, privacy, availability of services, and policy choice. This cluster parallels the privacy/service typology identified by Hawes and colleagues (2003), but because it is but one of six clusters, the scope of analysis used here is more comprehensive. The aggregation of large facilities into such a cluster is in line with the notion that large facilities have more resources, activities, and services than smaller facilities (Morgan, Eckert, & Lyon, 1995; Weihl, 1981). The question of whether larger size involves a tradeoff, such as more impersonalization, warrants consideration, but cannot be answered with these data.      The typologies developed in this study expand on what has been done by other researchers, most of which relates to nursing home care. Gold and colleagues (1991) derived a typology of 55 nursing homes through unstructured narratives. By using eight criterion variables related to physical environment, staff/resident interaction, and staff and administrator attitudes toward residents, they identified eight clusters with an indication of good or bad type. Although this qualitatively driven typology suggests differences related to quality, the study is limited in generalizability to nursing homes for memory-impaired older adults. Grant (1998) developed an empirical typology of 390 units in 123 nursing homes through cluster analysis, a similar procedure to that used in the present study. By using seven care attributes including physical environment, resident activity participation, and staff training, he obtained six clusters with a varying combination of care attributes. Grant's typology offered a useful way of classifying dementia care in nursing homes, but, like the typology identified by Gold and colleagues, used a narrow range of variables and was limited to nursing homes. Timko and Moos (1991) developed a typology through cluster analysis using a sample of 235 nursing homes, residential care facilities, and congregate apartments. Six distinct types of facilities emerged from the seven physical and social environment variables. Their study is one of the few that included RC/AL, but it was not specific to RC/AL. Thus, although all of these typology studies provide insights into understanding the configuration of long-term-care facilities using quantitative or qualitative methods, the present study is unique with respect to its focus on a broad range of RC/AL facilities and criterion variables and its use of resident case-mix and an innovative analytic method using a latent variable approach.      Nevertheless, it is important to note that the present study has several limitations. First, although we based clustering variables on theoretical and empirical evidence, the procedure of clustering facilities into several groups may be criticized as being data driven. Indeed, one caution about cluster analysis procedures is that the analytic procedures tend to be structure imposing rather than structure seeking (Aldenderfer & Blashfield, 1984). Replication of these results by applying the same procedures to a different data set of RC/AL facilities would strengthen findings (Milligan & Cooper, 1987). A second limitation involves both sampling and policy issues. This study was based in four states, which may limit generalizability, because state regulations affect RC/AL philosophy, admission and retention criteria, and service provision (Mollica, 2001). However, although the facilities under study were not necessarily representative of all facilities across the country, they certainly were representative of the four study states and, most likely, of common facility types nationally. Third, the resident case-mix data were reported by facility administrators, rather than compiled by a detailed listing of all facility residents. Thus, an unknown possibility of reporting bias may have affected the results. Despite these limitations, however, this typology is a helpful first step in both elucidating different models of care and providing clear criteria by which to differentiate them. Furthermore, this analytic strategy can be replicated in other states for purposes of verification or to add to what is known.      In closing, cluster solutions must be examined in terms of their practicality and relevance (Everitt et al., 2001; Muthén & Muthén, 2000). From a face-validity perspective, many of these clusters parallel distinct subtypes discussed informally by research staff involved in data collection. Some facilities with higher resident ADL and cognitive impairment may be skimming off of the nursing home market and so would be required to provide more intense services. Others, such as those with a mixed resident population, may require that the facilities contract with health professionals to meet their occasional needs for health care. The clusters do not, however, recognize the heterogeneity that exists within the clusters, related to variables that were not under study. That point notwithstanding, the two models identified both the clustering of financing (Medicaid) and mental illness, and of high ADL impairment. These variables are relevant to state regulations regarding the allocation of public dollars, as well as to the level of resident disability likely to be served by RC/AL across the nation. Additional tests of relevance would include a study of the relationship between facility type and outcomes such as resident quality of life, health status, and health care utilization. Thus, although the findings reported here have immediate usefulness in describing and differentiating the range of RC/AL facilities (and perhaps in guiding care provision and helping families and professionals match potential residents with facilities), the ultimate value of the typologies requires their replication in other regions and their application to longitudinal studies of resident outcomes. Finally, continued study is necessary to test the stability and consistency of the cluster solutions within and across states that maintain different regulatory systems and that may well be characterized through the configuration of the typologies.                                      This research was supported by Grants RO1 AG13871, RO1 AG13863, and K02 AG00970 from the National Institute on Aging. We acknowledge and note appreciation for the cooperation of the facilities, residents, and families participating in the Collaborative Studies of Long-Term Care. We give special thanks to Drs. Richard Barth, Natasha Bowen, Shenyang Guo, Lucinda Roff, and Ellen Csikai, who provided invaluable comments on an earlier version of this article.                          1          School of Social Work, The University of Alabama, Tuscaloosa.                          2          Cecil G. Sheps Center for Health Services Research, University of North Carolina at Chapel Hill.                          3          School of Social Work, University of North Carolina at Chapel Hill.                          4          Department of Family Medicine, University of North Carolina at Chapel Hill.                          5          Department of Epidemiology and Preventive Medicine, University of Maryland, Baltimore County.                          6          Department of Sociology and Anthropology, University of Maryland, Baltimore County.                          Decision Editor: Linda S. Noelker, PhD                                      Table 1.                                Description of Facility-Level Variables Used in Typology Development.                                                              Variables                Description                                                                    Structure domain                The capacity of the facility to provide care                                                Facility size                Bed capacity                                                Years in business                Years of facility operation                                                Ownership                Proprietary status (1 = for profit; 0 = not for profit)                                                Affiliated with other facilities                Whether facility is affiliated with a nursing home, a hospital, or a continuing care retirement community (1 = yes; 0 = no)                                                RC/AL chain                Whether facility is in a chain with other RC/AL facilities (1 = yes; 0 = otherwise)                                                RN or LPN                Whether facility has RNs or LPNs on duty (1 = yes; 0 = no)                                                Licensed nursing care hours                The number of RN and LPN hours per week per resident                                                Aide care hours                The number of nursing aide hours per week per resident                                                Aide turnover                Annual nursing aide turnover rate                                                Facility environment                Facility environmental quality measured with the Assisted Living Environmental Quality Scale (AL-EQS), with a higher score indicating a better environment                                                Private rooms                Percent of private rooms                                                Neighborhood                How attractive the neighborhood in which facility is located is rated (1 = attractive; 0 = not attractive)                                            Process domain                The way care is delivered (number of items and internal consistency of subscales)                                                Overall admission policies                The admission expectations related to resident characteristics (24 items; α =.84)                                                Acceptance of problem behavior                The extent to which the facility tolerates behavior problems exhibited by residents (16 items; α =.79)                                                Policy choice                The degree to which residents individualize their routines (17 items; α =.76 )                                                Policy clarity                The extent to which behavioral expectations and facility rules are well defined (10 items; α =.69)                                                Provision of privacy                The extent to which privacy is given to residents (9 items; α =.83)                                                Resident control                The degree to which residents are able to influence facility administration and policy (24 items; α =.80)                                                Overall provision of services                The extent to which the facility offers health and supportive services (20 items; α =.80)                                                Provision of social and recreational services                The degree to which organizational activities are available (13 items; α =.79)                                            Resident case-mix domain                Facility-level resident characteristics                                                Medicaid residents                % of residents on Medicaid                                                Residents requiring help in self care                % of residents who require assistance taking care of their own appearance                                                Residents requiring help in transfer                % of residents who need help getting in and out of bed                                                Residents with a dementia diagnosis                % of residents who are diagnosed with dementia                                                Residents with history of mental or psychiatric illness                % of residents who have a diagnosis of mental or psychiatric illness                                                Residents with behavioral symptoms                % of residents who require attention because of behavioral symptoms                                                                        Notes: RC/AL = residential care/assisted living; RN = registered nurse; LPN = licensed practical nurse. Dichotomous variables are so indicated; other variables are continuously scaled.                                                            Table 2.                                Distribution of Variables Used in Typology Development.                                                              Variable                M (SD) or %                                                                    Structure                                                                Bed capacity (range 4–190)                28.49 (35.24)                                                Years in business (range 0.17–148)                13.95 (17.04)                                                Proprietary ownership                82.54%                                                Affiliation                                                                    Affiliated with NH, hospital, or CCRC                23.94%                                                    RC/AL chain                32.98%                                                Staffing                                                                    Have a RN or LPN                47.09%                                                    Licensed nursing care hours per resident per week (range 0–20)                1.96 (3.42)                                                    Aide care hours per resident per week (range 0–74)                10.66 (9.03)                                                    Aide turnover, annual (range 0–80)                68.92%                                                Environment quality                                                                    Facility environment (range 5–26)a                16.50 (5.13)                                                    % Private rooms (range 0–100)                66.01 (32.43)                                                    Attractive neighborhood                82.01%                                            Process                                                                Requirements for residents                                                                    Overall admission policies (range 0–100)                72.58 (18.38)                                                    Acceptance of problem behavior (range 0–100)                33.88 (21.52)                                                Individual freedom or institutional order                                                                    Policy choice (range 10.53–89.47)                50.24 (15.69)                                                    Policy clarity (range 0–100)                58.72 (23.95)                                                    Provision of privacy (range 22.22–100)                56.87 (21.69)                                                    Resident control (range 0–64)                28.22 (16.22)                                                Provision of service and activities                                                                    Overall provision of services (range 14.81–92.59)                58.39 (17.00)                                                    Provision of social or recreational activities (range 0–96.15)                50.71 (22.46)                                            Resident case mix (all ranges 0–100)                                                                % Medicaid residents                14.19 (23.52)                                                Functional status                                                                    % Require help in self-care                46.51 (30.50)                                                    % Require help in transfer                15.97 (20.40)                                                    % Demented                33.81 (27.05)                                                    % History of mental or psychiatric illness                12.45 (17.55)                                                    % Behavioral symptoms                8.68 (17.56)                                                                        Notes: NH = nursing home; CCRC = continuing care retirement community; RC/AL = residential care/assisted living; RN = registered nurse; LPN = licensed practical nurse. For the table, N = 189. Four facilities with excessive missing values were excluded. Some variables have more missing values than others. The missing values for staffing-related variables range from 32%–54%. Percent of private rooms has 35% missing. For process variables, the missing values range from 2%–6%. For facility-level resident variables, missing values are distributed evenly (4%–6%) across variables except % Medicaid residents, of which 22% of the values are missing.                                      aFacility environment was measured with the Assisted Living Environmental Quality Score, which ranged from 5–26, with higher scores indicating better environment.                                                            Table 3.                                Cluster Solutions Based on Resident Case-Mix Variables.                                                              Variable                Cluster R-1 High Medicaid, Mental, or Psychiatric Illness; Poor Environment; Low Privacy (n = 22)                Cluster R-2 High ADL Impairment, Cognitive Impairment (n = 25)                Cluster R-3 High Cognitive Impairment, Behavioral Symptoms, Nursing Care (n = 8)                Cluster R-4 Low Impairment; Older Facilities; High Privacy; Strict Admission (n = 59)                Cluster R-5 Moderate Structure, Process, Case mix (n = 72)                Test Statistic and Group Comparisonsa                                                                    Structure                                                                                                                                                Bed capacity                14.14 (10.72)                20.80 (22.66)                13.38 (8.23)                35.07 (44.50)                30.53 (33.29)                F = 2.3 (1: 4,5)                                                Years in business                11.50 (8.60)                11.76 (11.86)                8.50 (5.32)                                  21.36 (25.01)                                9.72 (10.16)                F = 4.7** (4: 1,2,3,5)                                                Proprietary ownership                1.00 (0.00)                0.88 (0.33)                0.88 (0.35)                0.75 (0.44)                0.81 (0.40)                χ2 = 42.2***                                                Affiliated with hospital or CCRC                0.14 (0.35)                0.28 (0.46)                0.13 (0.35)                0.28 (0.45)                0.25 (0.44)                χ2 = 70.9***                                                RC/AL chain                0.27 (0.46)                0.28 (0.46)                0.50 (0.53)                0.19 (0.40)                0.47 (0.50)                χ2 = 25.2***                                                Have RN or LPN                0.42 (0.51)                0.35 (0.49)                0.63 (0.52)                0.47 (0.50)                0.51 (0.50)                χ2 = 90.1***                                                Licensed nursing hours per resident per week                0.70 (1.18)                1.77 (3.26)                                  5.15 (6.84)                                1.91 (3.46)                2.05 (3.14)                F = 2.5* (3: 1,2,4,5)                                                Aide care hours per resident per week                8.14 (5.46)                12.33 (7.00)                12.96 (7.48)                6.85 (5.75)                13.58 (11.33)                F = 4.8** (1: 5); (4: 2,5)                                                Aide turnover                0.91 (1.14)                0.69 (1.02)                0.77 (0.81)                0.64 (1.39)                0.66 (0.68)                F = 0.2                                                Environment (AL-EQS)                                  12.77 (5.06)                                17.60 (3.89)                17.88 (3.56)                17.10 (5.16)                16.78 (5.23)                F = 3.8** (1: 2,3,4,5)                                                Proportion private room                                  0.38 (0.35)                                0.66 (0.31)                0.67 (0.39)                0.72 (0.32)                0.69 (0.29)                F = 4.0** (1: 2,3,4,5)                                                Attractive neighborhood                0.73 (0.46)                0.76 (0.44)                0.88 (0.35)                0.86 (0.35)                0.83 (0.38)                χ2 = 3.8                                            Process                                                                                                                                                % Admission policies                77.57 (15.57)                84.67 (11.01)                79.17 (11.14)                                  60.36 (21.39)                                75.68 (13.41)                F = 13.2*** (4: 1,2,3,5); (2: 5)                                                % Acceptance of problem behaviors                27.88 (18.70)                42.27 (23.15)                41.67 (27.08)                30.77 (18.44)                34.86 (22.73)                F = 2.0 (2: 1,4)                                                % Policy choice                41.95 (12.08)                50.82 (15.64)                49.78 (16.62)                52.35 (14.37)                51.01 (17.21)                F = 1.9 (1: 4,5)                                                % Policy clarity                57.27 (20.57)                63.24 (22.28)                65.14 (13.36)                58.77 (25.24)                56.53 (25.46)                F = 0.5                                                % Privacy                40.74 (12.34)                54.11 (20.73)                48.61 (15.64)                                  65.09 (21.51)                                56.81 (22.13)                F = 6.0*** (1: 2,5); (4: 1,2,3,5)                                                % Resident Control                30.51 (17.69)                32.23 (14.15)                20.24 (15.60)                29.79 (15.76)                25.91 (16.67)                F = 1.5                                                % Total no. of services                57.58 (12.30)                64.10 (18.41)                57.87 (20.18)                58.92 (16.11)                55.94 (17.85)                F = 1.1 (2: 5)                                                % Social and recreational activities                41.78 (19.98)                61.63 (19.40)                52.40 (14.96)                43.74 (22.65)                54.58 (22.10)                F = 4.7** (1: 2,5); (4: 2,5)                                            Resident case mix                                                                                                                                                % Residents on Medicaid                                  70.45 (17.61)                                6.79 (10.42)                3.57 (6.56)                7.84 (13.14)                6.71 (10.28)                F = 115.5*** (1: 2,3,4,5)                                                % Residents require help in self-care                                  44.43 (28.10)                                80.37 (15.65)                76.79 (17.99)                                  11.70 (11.25)                                                  60.45 (15.90)                                F = 108.1*** (1: 2,3,4,5); (4: 2,3); (5: 2,3,4)                                                % Residents require help in transfer                11.04 (12.07)                                  53.26 (16.53)                                                  27.83 (32.33)                                                  3.77 (8.69)                                12.96 (11.29)                F = 67.7*** (1: 2,3,4); (2: 3,4,5); (3: 4,5); (4: 5)                                                % Residents demented                24.69 (23.62)                61.37 (25.26)                64.38 (21.26)                15.01 (16.77)                                  39.10 (23.00)                                F = 27.5*** (1: 2,3,5); (2: 4,5); (3: 4,5); (4: 5)                                                % Residents mental or psychiatric illness                                  34.83 (31.14)                                11.82 (13.13)                19.58 (24.33)                7.90 (10.35)                8.64 (10.67)                F = 14.3*** (1: 2,3,4,5); (3: 4)                                                % Residents with behavioral symptoms                2.83 (5.63)                6.24 (7.53)                                  80.89 (17.99)                                3.41 (5.72)                7.59 (9.41)                F = 161.1*** (1: 3,5); (2: 3); (3: 4,5); (4: 5)                                                                        Notes: CCRC = continuing care retirement community; RC/AL = residential care/assisted living; RN = registered nurse; LPN = licensed practical nurse; AL-EQS = Assisted Living–Environmental Quality Score. Data presented are means; standard deviations are presented parenthetically. Three facilities without information on the six resident case-mix variables were excluded, resulting in n = 186 facilities. Bold statistics are those that significantly differentiate that cluster from all others. Depending on the results from multiple comparisons, it is possible to have multiple significant clusters on a variable. Each cluster was labeled to identify those characteristics that significantly differentiate it from the others or had a comparatively high mean score (even if not significant from all others).                                      *p <.05; **p <.01; ***p <.001.                                      aChi-square tests for categorical variables show only test statistics. A statistically significant ANOVA resulted in multiple comparisons, as shown.                                                            Table 4.                                Cluster Solutions Based on Structure, Process, and Resident Case-Mix Variables.                                                              Variable                Cluster SPR-1 Moderately Low Service; High Privacy (n = 14)                Cluster SPR-2 High Medicaid, Mental, or Moderately High Psychiatric Illness (n = 25)                Cluster SPR-3 High Impairment; Moderate Privacy; Moderately High Service (n = 57)                Cluster SPR-4 Large Facilities; Good Environment; High Privacy, Choice, Service (n = 54)                Cluster SPR-5 High Staff Turnover, Medicaid, Mental or Psychiatric Illness (n = 7)                Cluster SPR-6 Lower Nursing Care, Aide Turnover, Policy Clarity, Resident Control, Medicaid Case Mix (n = 32)                Test Statistic and Group Comparisonsa                                                                    Structure                                                                                                                                                                Bed capacity                11.21 (4.46)                13.56 (10.25)                16.25 (15.10)                                  66.46 (44.78)                                11.14 (9.49)                9.25 (5.82)                F = 32.7*** (4: 1,2,3,5,6)                                                Years in business                34.50 (24.81)                11.04 (6.23)                10.56 (12.11)                14.86 (22.60)                21.71 (14.95)                10.00 (6.51)                F = 6.0*** (1: 2,3,4,6)                                                Proprietary ownership                0.64 (0.50)                1.00 (0.00)                0.96 (0.19)                0.52 (0.50)                1.00 (0.00)                1.00 (0.00)                χ2 = 59.8***                                                Affiliated with hospital or CCRC                0.07 (0.27)                0.24 (0.44)                0.14 (0.35)                0.57 (0.50)                0.00 (0.00)                0.00 (0.00)                χ2 = 48.6***                                                RC/AL chain                0.14 (0.36)                0.44 (0.51)                0.51 (0.50)                0.38 (0.49)                0.00 (0.00)                0.00 (0.00)                χ2 = 31.6***                                                Have RN or LPN                0.11 (0.33)                0.41 (0.50)                0.45 (0.50)                0.98 (0.15)                0.00 (0.00)                0.00 (0.00)                χ2 = 77.5***                                                Licensed nursing hours per resident per week                0.50 (1.14)                0.59 (1.13)                2.93 (4.95)                3.35 (2.73)                0.13 (0.19)                0.06 (0.22)                F = 6.2*** (1: 3,4); (2: 3,4); (6: 3,4)                                                Aide care hours per resident per week                1.87 (3.42)                7.65 (4.95)                13.86 (11.89)                9.49 (5.05)                6.85 (10.81)                12.81 (8.80)                F = 4.7*** (1: 3,4,6); (2: 3,4,6)                                                Aide turnover                1.25 (1.06)                0.64 (0.76)                0.80 (0.90)                0.50 (0.44)                                  6.00 (2.83)                                0.37 (0.56)                F = 22.3*** (5: 1,2,3,4,6); (3: 6)                                                Environment (AL-EQS)                15.36 (4.88)                13.28 (4.89)                15.68 (4.07)                                  20.93 (3.83)                                14.14 (6.07)                14.00 (4.13)                F = 17.7*** (2: 3); (4: 1,2,3,5,6)                                                Proportion private room                0.91 (0.16)                0.39 (0.32)                0.65 (0.29)                0.88 (0.21)                0.14 (0.17)                0.52 (0.29)                F = 16.5*** (1: 2,3,5,6); (3: 2,4,5,6); (4: 2,5,6); (5: 6)                                                Attractive neighborhood                0.86 (0.36)                0.76 (0.44)                0.77 (0.42)                0.91 (0.29)                0.57 (0.53)                0.84 (0.37)                χ2 = 7.5                                            Process                                                                                                                                                                % Admission policies                35.12 (16.80)                78.09 (16.82)                82.37 (10.51)                70.14 (15.58)                                  58.33 (13.61)                                74.45 (12.91)                F = 28.4*** (1: 3,4,5); (3: 4,5,6); (4: 2,5); (5: 2,6)                                                % Acceptance of problem behaviors                28.23 (14.36)                26.67 (18.86)                40.22 (24.13)                38.12 (21.55)                23.33 (15.06)                25.63 (17.28)                F = 3.6** (2: 3,4); (6: 3,4)                                                % Policy choice                47.74 (12.12)                41.75 (11.94)                48.16 (14.64)                                  62.18 (14.94)                                49.12 (5.44)                42.11 (12.57)                F = 12.6*** (3: 2,4,5,6); (4: 1,2,5,6)                                                % Policy clarity                36.09 (19.09)                59.33 (16.99)                58.05 (17.31)                81.14 (12.92)                42.67 (23.50)                34.69 (20.58)                F = 36.8*** (1: 2,3,4); (2: 4,5,6); (3: 5,6); (4: 5,6)                                                % Privacy                                  65.08 (14.36)                                40.28 (11.73)                44.08 (13.59)                                  82.29 (13.61)                                40.74 (9.07)                49.60 (14.45)                F = 59.0*** (1: 2,3,4,5,6); (2: 6); (4: 2,3,5,6)                                                % Resident control                20.76 (15.23)                28.30 (17.36)                25.67 (16.29)                38.63 (12.79)                26.77 (8.78)                19.01 (13.06)                F = 8.9*** (4: 1,2,3); (6: 2,3,4)                                                % Total no. of services                                  37.04 (9.85)                                59.85 (11.94)                57.99 (17.12)                                  71.10 (12.52)                                49.67 (12.47)                47.77 (12.25)                F = 20.2*** (1: 2,3,4,5,6); (4: 2,3,5); (6: 2,3,4)                                                % Social and Recreational Activities                26.37 (19.13)                38.00 (20.06)                                  60.21 (17.13)                                                  67.08 (12.59)                                32.05 (17.82)                30.74 (14.64)                F = 35.6*** (2: 1,3,4); (3: 1,4,5,6); (4: 1,5,6)                                            Resident case mix                                                                                                                                                                % Residents on Medicaid                9.31 (14.65)                                  67.64 (20.45)                                7.78 (10.39)                5.02 (9.07)                                  49.17 (18.73)                                3.46 (8.41)                F = 97.2*** (2: 1,3,4,5,6); (5: 1,3,4,6)                                                % Residents require help in self-care                7.35 (13.58)                41.57 (28.04)                                  67.34 (21.52)                                42.27 (29.79)                8.89 (9.81)                45.61 (27.04)                F = 17.2*** (1: 2,3,4,6); (3: 2,4,5,6); (5: 2,4,6)                                                % Residents require help in transfer                0.38 (1.41)                9.58 (12.72)                                  25.95 (21.92)                                14.84 (21.90)                3.33 (8.16)                15.18 (18.46)                F = 5.9*** (1: 3,4,6); (3: 2,4,5,6)                                                % Residents demented                6.34 (9.43)                24.40 (23.30)                                  50.14 (25.45)                                31.62 (24.26)                11.11 (20.18)                32.40 (26.44)                F = 10.8*** (1: 2,3,4,6); (3: 2,4,5,6); (5: 4,6)                                                % Residents with mental or psychiatric illness                10.45 (10.23)                                  24.65 (27.50)                                10.97 (14.10)                7.49 (10.81)                                  42.50 (32.00)                                8.67 (11.28)                F = 8.7*** (2: 1,3,4,5,6); (5: 1,3,4,6)                                                % Residents with behavioral symptoms                4.33 (6.68)                2.76 (5.39)                                  18.56 (27.61)                                5.73 (8.67)                2.78 (6.80)                3.81 (6.79)                F = 5.9*** (3: 1,2,4,5,6); (4: 5)                                                                        Notes: CCRC = continuing care retirement community; RC/AL = residential care/assisted living; RN = registered nurse; LPN = licensed practical nurse; AL-EQS = Assisted Living–Environmental Quality Score. For the table, N = 189. Table data presented are means; standard deviations are shown parenthetically. Bold statistics are those that significantly differentiate that cluster from all others. Depending on the results from multiple comparisons, it is possible to have multiple significant clusters on a variable. Each cluster was labeled to identify those characteristics that significantly differentiate it from the others or had a comparatively high or low mean score (even if not significant from all others).                                      *p <.05; **p <.01; ***p <.001.                                      aChi-square tests for categorical variables show only test statistics. A statistically significant ANOVA resulted in multiple comparisons, as shown.                                                      References              Adler, S., (1998). Get ready for consolidation: Today's product in the pipeline will build tomorrow's big players. Contemporary Long Term Care, 21, (8), 39-45.                    Aldenderfer, M. S., & Blashfield, R. K., (1984). Cluster analysis. Beverly Hills, CA: Sage.                    Assisted Living Workgroup. (2003). Assuring quality in assisted living: Guidelines for federal and state policy, state regulation, and operations. Washington, DC: Author.                    Ball, M. M., Whittington, F. J., Perkins, M. M., Patterson, V. L., Hollingsworth, C., & King, S. V., et al (2000). Quality of life in assisted living facilities: Viewpoints of residents. Journal of Applied Gerontology, 19, 304-325.                    Davis, K. J., Sloane, P., Mitchell, C. M., Preisser, J., Grant, L., & Hawes, M. C., et al (2000). Specialized dementia programs in residential care settings. The Gerontologist, 40, 32-42.                    Davis, M. A., (1991). On nursing home quality: A review and analysis. Medical Care Review, 48, 129-166.                    Donabedian, A., (1978). The quality of medical care. Science, 200, 856-864.                    Donabedian, A., (1980). Explorations in quality assessment and monitoring. Vol. 1, The definition of quality and approaches to its assessment. Ann Arbor, MI: Health Administration Press.                    Donabedian, A., (1988). The quality of care: How can it be assessed? Journal of the American Medical Association, 260, 1743-1748.                    Everitt, B. S., Landau, S., & Leese, M., (2001). Cluster analysis (4th ed.). New York: Oxford University Press.                    Golant, S. M., (2004). Do impaired older persons with health care needs occupy U.S. assisted living facilities? An analysis of six national studies. Journal of Gerontology: Social Sciences, 59B, S68-S79.                    Gold, D. T., Sloane, P. D., Mathew, L. J., Bledsoe, M. M., & Konanc, D. A., (1991). Special care units: A typology of care settings for memory-impaired older adults. The Gerontologist, 31, 467-475.                    Grant, L. A., (1998). Beyond the dichotomy: An empirical typology of Alzheimer's care in nursing homes. Research on Aging, 20, 569-592.                    Grant, L. A., Kane, R. A., & Stark, A. J., (1995). Beyond labels: Nursing home care for Alzheimer's disease in and out of special care units. Journal of the American Geriatrics Society, 43, 569-576.                    Hawes, C., Phillips, C. D., & Rose, M., (2000). High service or high privacy assisted living facilities, their residents, and staff: Results from a national survey. Beachwood, OH: Myers Research Institute.                    Hawes, C., Phillips, C. D., Rose, M., Holan, S., & Sherman, M., (2003). National survey of assisted living facilities. The Gerontologist, 43, 875-882.                    Holmes, D., & Teresi, J., (1994). Characteristics of special care units in the northeast five-state survey: Implications of different definitional criteria. Alzheimer Disease and Associated Disorders, 8, (Suppl. 1), S97-S105.                    Kane, R. A., & Wilson, K. B., (2001). Assisted living at the crossroads: Principles for its future. Portland, OR: Jessie F. Richardson Foundation.                    Lawton, M. P., (2001). The physical environment of the person with Alzheimer's disease. Aging and Mental Health, 5, (Suppl. 1), S56-S64.                    Milligan, G. W., & Cooper, M. C., (1987). Methodology review: Clustering methods. Applied Psychological Measurement, 11, 329-354.                    Mollica, R. L., (2001). State policy and regulations. In S. Zimmerman, P. D. Sloane, & J. K. Eckert (Eds.), Assisted living: Needs, practice, and policies in residential care for the elderly (pp. 9–33). Baltimore, MD: The Johns Hopkins University Press.                    Moos, R. H., & Lemke, S., (1996). Evaluating residential facilities: The multiphasic environmental assessment procedure. Thousand Oaks, CA: Sage.                    Morgan, L. A., Eckert, J. K., & Lyon, S. M., (1995). Small board and care homes: Residential care in transition. Baltimore: Johns Hopkins University Press.                    Morgan, L. A., Gruber-Baldini, A. L., Eckert, J. K., & Zimmerman, S., (2004). Policy and research for small assisted living facilities. Journal of Aging & Social Policy, 16, (4), 1-16.                    Mukamel, D. B., & Spector, W. D., (2000). Nursing home costs and risk-adjusted outcome measures of quality. Medical Care, 38, 78-89.                    Muthén, B. O., (2001). Latent variable mixture modeling. In G. A. Marcoulides & R. E. Schumacker (Eds.), New developments and techniques in structural equation modeling (pp. 1–33). Mahwah, NJ: Erlbaum.                    Muthén, B. O., & Muthén, L. K., (2000). Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism: Clinical and Experimental Research, 24, 882-891.                    Muthén, L. K., & Muthén, B. O., (1998). Mplus user's guide (2nd ed.). Los Angeles: Authors.                    SAS Institute. (1999). SAS system for Windows [Computer software]. Cary, NC: Author.                    Schwartz, G., (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461-464.                    Sloane, P. D., Lindeman, D. A., Phillips, C., Moritz, D. J., & Koch, G., (1995). Evaluating Alzheimer's special care units: Reviewing the evidence and identifying potential sources of study bias. The Gerontologist, 35, 103-111.                    Sloane, P. D., Zimmerman, S., & Walsh, J. F., (2001). The physical environment. In S. Zimmerman, P. D. Sloane, & J. K. Eckert (Eds.), Assisted living: Needs, practice, and policies in residential care for the elderly (pp. 173–197). Baltimore: Johns Hopkins University Press.                    SPSS. (1991). SPSS for Windows [Computer software]. Chicago, IL: Author.                    Stokes, M. E., Davis, C. S., & Koch, G. G., (2000). Categorical data analysis using the SAS system (2nd ed.). Cary, NC: SAS Institute.                    Timko, C., & Moos, R. H., (1991). A typology of social climates in group residential facilities for older people. Journal of Gerontology: Social Sciences, 46, S160-S169.                    Weihl, H., (1981). On the relationship between the size of residential institutions and the well-being of residents. The Gerontologist, 21, 247-250.                    Wunderlich, G. S., Sloan, F. A., & Davis, C. K., (1996). Nursing staff in hospitals and nursing homes: Is it adequate?. Washington, DC: National Academy Press.                    Zimmerman, S., Gruber-Baldini, A. L., Hebel, J. R., Sloane, P. D., & Magaziner, J., (2002). Nursing home facility risk factors for infection and hospitalization: Importance of RN turnover, administration, and social factors. Journal of the American Geriatrics Society, 50, 1987-1995.                    Zimmerman, S., Gruber-Baldini, A. L., Sloane, P. D., Eckert, J. K., Hebel, J. R., & Morgan, L. A., et al (2003). Assisted living and nursing homes: Apples and oranges? The Gerontologist, 43, (Special Issue II), 107-117.                    Zimmerman, S., Sloane, P. D., & Eckert, J. K., (2001). Assisted living: Needs, practice, and policies in residential care for the elderly. Baltimore: Johns Hopkins University Press.