Assessment Scales for Advanced Dementia

8/10/2019 Assessment Scales for Advanced Dementia

http://slidepdf.com/reader/full/assessment-scales-for-advanced-dementia 1/12



3

1

Methodological ConsiderationsABSTRACT: Conducting research with persons who have advanced dementia ismore di cult than during the earlier stages of the disease because of severe cognitiveand language di culties that preclude abstract thinking, being interviewed, or self-reporting. In order to directly obtain data on subjects, researchers require observa-tional scales that can measure agitation, discomfort, engagement, pain, or resistive-ness to care. Family or sta respondents are necessary to measure other factors, suchas disease severity, quality of life, or satisfaction with family visits or end-of-life care.

All scales must meet criteria of conceptual, methodological, operational, and empiri-cal adequacy. This chapter describes approaches used in developing such scales andpresents evaluation criteria in reporting how a scale was developed (purpose, frame-work, literature review, design, and methods) and in critiquing a scale’s merits. Thechapter also describes observer training and evaluation and discusses data manage-ment and recoding strategies. The goal is to overcome any potential inaccuracy inusing scales. The accuracy of a scale depends on the reliability and validity of thescale and on the accuracy of data collection and management.

Four pillars, ranging from abstract to concrete, support excellence in research: conceptual,methodological, operational, and empirical adequacy. A aw in any of these pillars jeop-ardizes the entire project. Each pillar, alone and in combination, needs to be consideredduring the phases of planning, conducting, analyzing, and utilizing research.

Conceptual adequacy relates to the abstract underpinnings of the research topic. A topicdoes not exist alone unconnected to other topics, but instead theoretically as an intan-gible component of a conceptual framework or as a facet of a concept within a model.The framework provides an organized way of thinking about the total project and all ofits parts, thereby creating a lens through which the problem is viewed as well as a road-map that guides methodological choices and connects the study with existing knowledgeof the topic. Whether one is conducting a multisite clinical trial or a unit-based quality-improvement initiative, the framework will logically connect all components of the project.

How scales are conceptualized and concepts/topics are de ned determines how scalesare developed, helps in the selection of speci c scales, and guides how scales should beused. Some scale selections are easy to make. For example, when the person with demen-tia is not interacting with a caregiver, one would not use a scale to measure resistiveness,which is invoked by a caregiving encounter and is de ned as “the repertoire of behav-iors with which persons with dementia withstand or oppose the e orts of a caregiver”(Mahoney et al., 1999, p. 28). Similarly, one would select a pain scale, such as the PAINAD,if planning an intervention to alleviate symptoms associated with an invasive procedurethat will cause tissue damage (Warden, Hurley, & Volicer, 2003).



4 ■ Assessment Scales for Advanced Dementia

Methodological adequacy is achieved when researchers have rigorously used the correctmethods to answer the research questions in order to ensure the quality of the researchproject. The type of study design must be determined (experimental, quasi-experimental,or nonexperimental), which in turn determines what measures and statistical tests should be used. To “guarantee” that the data are accurate and that error is minimized or con-trolled, one must identify procedures and techniques that are appropriate for the projectand consistent with its conceptual underpinnings.

For example, what technique(s) should be used to study a concept, and what are the best methods for collecting data? When studying agitation in a person with advanceddementia, it is clear that self-report is impossible, but should one observe the person di-rectly or use a proxy respondent to provide a retrospective summation and evaluation?Or should researchers conduct direct observation as well as use retrospective reports? Theuse of multiple measures of the same concept strengthens a study and is recommendedwhen feasible. For example, Camberg et al.’s study of enhancing well-being, where agita-tion was considered the inverse state of well-being, measured both direct observation andretrospective recall to provide comprehensive results about the e cacy of using Simu-lated Presence as an intervention for persons with dementia (Camberg et al., 1999). (Formore details about this study, see Chapter 10.) It is important to keep in mind that a care-giver cannot simultaneously provide care and collect objective data using a behavioralobservation scale.

Operational adequacy means that the mechanisms to achieve the project’s goals are “allset”; that is, the research sta , equipment, devices, instruments, and so forth work prop-erly. Operational adequacy relates to the infrastructure of the project, such as the inter-vention (treatment delity) and outcome measures (scales). One can determine a scale’soperational adequacy by examining its psychometric properties (reliability and validity).It is important to examine the initial values obtained when the scale was developed, thescale’s psychometric performance in subsequent projects, and the stated values in the

study being reported. One may view psychometrics as both the “paper” evaluation andthe “people” evaluation, the la er having to do with whether the scale is used with ac-curacy and consistency.

Two other related factors, range and sensitivity, should be considered, each having todo with the scale’s capacity to detect and quantify the concept being studied. While scalesdeveloped in a norm-referenced (versus a criterion-referenced) framework have a range, itis important to consider that the range of the scale may not t the range of the actual phe-nomenon. For instance, the Mini-Mental Status Examination (MMSE) is commonly usedas a measure to rate disease severity (Folstein, Folstein, & McHugh, 1975). Yet, the MMSEhas a “bo om e ect,” meaning that once the lowest scoring option of “0” is reached, theMMSE cannot detect any more changes, although there may be vast di erences betweenpersons scoring “0” in terms of disease severity. The BANS-S (Volicer, Hurley, Lathi, &

Kowall, 1994) was developed in order to detect di erences in disease severity among per-sons with advanced dementia who “bo om” on the MMSE (see Chapter 2). Sensitivity inthis context (versus the context of sensitivity and speci city in criterion-referenced scales)refers to the scale’s capacity to detect clinically signi cant di erences when they do ex-ist. For instance, the RTC-DAT (Mahoney et al., 1999) uses a scoring system whereby theseverity of each item is calculated by multiplying the intensity (3-point scale) by duration(4-point scale) to yield a severity score range of 1 to 12 for each item rated as present (seeChapter 11). Thus, individual items are sensitive enough to detect wide gradations in thetarget behavior and contribute to the overall sensitivity of the RTC-DAT.






help potential users evaluate a scale’s merits. We provide some citations to reference spe-ci c statements, but other information on measurement and research terms or statisticaltests can be found in classic texts (Tabachnick & Fidell, 2013; Wal , Strickland, & Lenz,2010), or via the Internet.

Purpose, Framework, and Literature ReviewThe purpose for developing a scale (need for making a speci c concept operational)should be clear, linked to the framework, and supported by the literature. One mustcarefully read both the initial scale development article and reports of projects that usedthe scale to verify that the scale’s topic (concept) does not overlap with other concepts.The topic should be clearly de ned, consistently used, and visibly linked to its theoreti-cal roots. One needs to look for consistencies and/or discrepancies in how the scale wasconceptualized, reviewing theoretical and operational de nitions of the topic measured.Some studies using the scale may o er additional psychometric support, providing evi-dence suggesting use of the scale. Other studies may nd that even when researchers usethe scale as the developers speci ed, psychometrics are inadequate and do not provideevidence supporting further use. Or, a study might use the scale inconsistently with itsconceptualization or alter the scale without providing reliability and validity results tosuggest that the original and altered scales are equivalent.

The framework drives the approach for developing and using a scale. A frameworkmay have been used for several decades (see Chapter 6) or may be developed empiri-cally as the project is conceptualized and carried out (see example of the model devel-oped for the RTC-DAT scale in Chapter 11). When frameworks are depicted graphi-cally, relationships among concepts are explained in a straightforward manner, makingit easy to visualize the place of the concept within the framework and its relationshipswith other concepts (see model in Chapter 6). When not depicted graphically, drawyour own model to facilitate learning whether or not the framework informed the devel-opment of the scale, from the conceptualization of research questions, the selection anduse of appropriate methods, and elimination of potential validity threats to nal resultsand conclusions.

The literature review should provide a concise synthesis and critical analysis of rel-evant research using both seminal (possibly decades old) and current work to explicatethe concept measured by the scale and to identify relationships among di erent works.Published works should inform readers about the state of the science regarding the stud-ied concept, illuminating what is known, unknown, generally accepted, disputed, or in-consistent about the topic (e.g., major methodological aws, gaps in research, or issuesthat need further study). The literature review for the original scale report should supportwhy the new scale is needed. This original report should also de ne where and how to

collect the data and should indicate who should collect data when administering the scale(e.g., archived database or electronic health record, self-report, proxy caregiver or fam-ily respondent, direct or recorded observation). In addition to providing the results ofhypothesis testing, subsequent articles using or reviewing the scale should provide thescale’s psychometrics in that study and enough data so that others can compute an e ectsize for planning follow-up studies.

Design and MethodsInstrument development reports should share the steps used in developing an instrumentand follow a logical plan with a series of consecutive phases. The scales presented in this



Methodological Considerations ■ 7

book adhered to the tenents of classical measurement theory as suggested in Measure-ment in Nursing and Health Research (Wal et al., 2010). Look for a blueprint of how theinvestigators proceeded and criteria to ensure conceptual and empirical adequacy, theninvestigate how these drove scale development procedures. For instance, did the investi-gators determine that the scale was empirically grounded (Tilden, Nelson, & May, 1990); judged to have content validity (Lynn, 1986); and accepted by potential users (Wya &Altman, 1995)? Did the scale have adequate initial reliability estimates of internal consis-tency (Nunnally & Bernstein, 1994)? Were stages of the development process delineatedand followed? The development process usually consists of four basic stages: 1) determin-ing the blueprint, 2) creating the item pool, 3) examining content validity, and 4) critiqu-ing readability and procedures.

A scale blueprint is a plan that sets forth how the scale will be developed, similar tohow a building blueprint informs construction workers and tradespersons as they trans-late a paper diagram into the speci ed edi ce. The blueprint provides guidelines that helpresearchers identify potential scale items (whether from the literature, from potential subjects or users, or by interview or observation.) Look for both conceptual and operationalde nitions of the concept and examine them to judge if they meet criteria of conceptualand operational adequacy. Then look to see how the item pool was developed and howitems were combined, re ned, and edited. To what degree does the item pool represent“the universe” of empirical indicators of the concept? Review how content validity was judged. An established method of judging content validity (Wal et al., 2010) involvesusing content experts to rate each item and its description for relevance and congruencewith the concept’s operational de nition. One of the seminal articles to guide this processis Lynn’s “Determination and Quanti cation of Content Validity” (Lynn, 1986), in whicha grid guides investigators on the number of judges and agreements needed to provide acontent validity index beyond the .05 level of signi cance for retaining items.

One should also review how administration and scoring procedures were deter-

mined. For instance, why was a visual analogue scale with a 100 mm line or a Likert-typescoring system anchored with the descriptors selected? Anchors can re ect polar oppo-sites of the item, degrees of intensity, numbers of de ning characteristics, or durationduring a speci c rating period. A “not applicable box” (NA) should be available for ratersto separate “NA” from a scoring of “0.” Typically, high scores equal a high presence of theconcept being studied. Items can be reverse scored so that highly positive is represented by high scores. Occasionally, low scores mean a high presence of the concept, as in theQUALID (Weiner et al., 2000) (see Chapter 4). When low scores indicate a high presenceof the concept being studied and two scales are correlated to examine convergent validity,such as the EOLD-SM (Volicer, Hurley, & Blasi, 2001) and the QUALID, the correlation(in this case, r = –.64) needs to show a minus sign, since low QUALID scores indicate goodquality of life. Also, it is typical to use the mean scores of items, subscales, and scale totals.

When examining re-test stability (assessed with the paired t-test), researchers need to de-vise and use a system (speci ed in the administration instructions) that describes how tocompare data obtained from the same person during two di erent time periods when it isnecessary to maintain that person’s privacy.

TestingLook for phases and types of reliability testing. Wya uses the criterion of credibility (Wya& Altman, 1995) to learn empirically if the items and scale administration procedures areacceptable to potential users. To be a valuable outcome measure, a scale must be consid-ered reasonable by those who would respond (or not) to individual items. We do not use




the term face validity , but instead suggest seeking input beyond the research team, such asa focused group discussion including potential end-point users or their representatives.While clarifying the team’s intent for each of the items, the group can discuss how othersunderstand each item, resolving concerns and incorporating suggestions (e.g., rewordingitems considered to address two distinct areas so that they become separate items).

Several steps should then follow to make empirically derived item-reduction deci-sions, explore conceptual dimensions, compute reliability estimates for internal con-sistency and retest stability (if used), and con rm the nal scale and administrationprocedures. Look for selection of sites and subjects to avoid potential confounders (non-representative se ings or subjects) and to obtain diverse subjects with desired characteris-tics for examining the scale. While instrument development projects are not “powered” toidentify the minimum required number of subjects (after data cleaning), a rule of thumb isto plan for a minimum of ve participants per item for examining factor structure (Knapp& Brown, 1995).

One should make an a priori decision as to the percentage of items that must be com-pleted in order to retain a subject’s data. Researchers should review demographic charac-teristics to ensure that they are consistent with the proposed use of the scale (e.g., subjectswith advanced dementia who would not interact with an observer versus a cognitivelyintact person). In general, a parsimonious yet reliable scale is desired, so only items con-sidered to perform well are retained. When reviewing internal consistency, delete thoseitems without a corrected item–total correlation between .3 and .7, because they wouldnot contribute to a cohesive set of items. Review the remaining items to see if the alphavalue is above the minimal internal consistency criterion of a new scale, which is generallyset at a minimum of .7 (Nunnally & Bernstein, 1994).

If the concept is not one-dimensional, the presence of potential subscales (factorialdimensions) can be examined by computing a Principal Components Analysis (PCA). APCA can also con rm that there is one construct in the scale. Examine how the research-

ers addressed missing values (how they handled a missing percent [e.g., mean substitu-tion]). Look for 1) how many factors a reported scree plot indicated and percent varianceexplained, 2) cut-o scores, and 3) the required di erence to indicate (or not) side loading.Side loading items should be placed with the factor that has the most conceptual congru-ence with the item. To learn congruity, review the de nitions of the items and comparethem with the conceptual and operational de nitions of the concept being measured. Re-view the reliability and variability of subscales. If a scale has high reliability but minimumvariability (frequency distribution), it is unlikely to detect di erences between groups. Ifretest stability was examined, look to see that the proper statistic was used and that thevalue with its probability value demonstrated that the scores were similar. If there weredi erences in sites or conditions where one might expect the concept to di er, compareresults by site(s).

There are advantages and disadvantages to both direct and recorded observations.Direct observations do not require expensive equipment, which can be di cult to incor-porate into the se ing without changing the natural environment. A skilled rater can beunobtrusive, and while observing for the targeted behavior(s), the rater can also obtaindata on the context in which the behavior(s) occurred. These data, whether scored on apaper form or electronic device, can be immediately entered into the database. The dis-advantage of direct observation is the potential for error by missing or incorrectly clas-sifying behavior(s). Rating recorded observations allows greater control and decreasespotential bias. For example, in one study, the Sloane group (2004) recorded observationaldata on digitalized videotapes and then randomly presented the data for coding to raters




who were blinded to the study aims, the assignment of subjects, and preintervention orpost-intervention status. However, problems can arise when recruiting subjects for stud-ies. One example would be for a study that includes videotaping persons either dressingor being bathed. For instance, the wife of a potential subject expressed concern about herhusband being recorded as he was bathing as part of a study on resistiveness. She wouldnot allow researchers to record him. She told the project director that although she under-stood from personal experience exactly why the study was being done, she also realizedhow embarrassed her husband would be if he knew how he was behaving. She just couldnot allow him to be in the study. There are often justi able reasons for why the numbersof potentially available subjects, numbers of enrolled subjects, and numbers of subjectswho completed a project di er.

In addition to reviewing the overall summary in instrument development projects,look to see if the authors provide recommendations for how the scale should be used anddescribe how the scale was developed and tested. Do the authors disclose study limita-tions or explain why there may have been score di erences among subgroups of subjects?Do they share whether additional testing is suggested to enhance the use of the scale or toexamine use of the scale with other populations? The authors should relate the new scaleto the existing literature (some of which may be more recent than the literature reviewconducted before the study was done). Do they explain what high or low scores couldmean and o er suggestions for improving conditions so as to obtain “desired” scores?The authors need to provide enough overall information for readers to feel con dent thattheir interpretation of the results means that the scale is appropriate for use with the de-sired population.

Since ve of the scales described in this book rely on direct observation, we are in-cluding a section on observers. The type of project and available resources will determineobserver criteria. For a unit-based quality-improvement project, caregiving sta may betaught to observe (a plus being the cost e ciency of using existing resources, while poten-

tial minuses include insu cient time to teach sta members or relieve them from clinicalresponsibilities to observe). For a multisite-controlled trial, researchers may hire speci cobservers, who should be carefully selected based on background and interests consistentwith the project and the scales being used. For instance, when rating pain in residentswith advanced dementia for a quality-improvement project in a long-term care site, nurs-ing assistants were reliable after 2 hours of training on the PAINAD (Warden, Hurley, &Volicer, 2003). To observe agitation, college graduates with a background in psychologyor nursing and a special interest in older adults were reliable after 20 hours of classroomand eld training (Hurley et al., 1999).

Observer Training and Evaluation

Whether scoring rating forms while in the eld se ing or rating videotaped segments inan o ce, observers will need to be accurate and consistent. The content and complexity ofthe scale and the rating and scoring required determine the curriculum that should beginin the classroom and proceed to the eld (where real-time direct observation is used).Researchers should develop a rater training program with handouts that highlight com-ponents that need to be memorized. Didactic content should include a description of theoverall project, the conceptual basis of the scale and rationale for its use, items’ labels withoperational de nitions and de ning characteristics, and scoring schema. During classroomtraining, raters should be tested on how accurately they list de ning characteristics anddescribe the rating scheme. Videotapes of speci c behaviors can be used for teaching the




observers how to rate as well as ongoing competency testing. For instance, to rate ob-served discomfort (Hurley, Volicer, Hanrahan, Houde, & Volicer, 1992), we were ableto use videotapes of actual persons with advanced dementia. To rate observed agitation(Hurley et al., 1999), we videotaped clinical sta and research team members acting outspeci c behaviors on the scale with varying degrees of intensity as well as those behaviorsthat were not manifestations of agitation.

In the classroom, the observers-in-training should be evaluated on their knowledgeof the research project. Classroom activities should consist of mastery testing of behav-ior de nitions, rating scheme, and scoring decision rules; practice in rating videotapedexamples of behavior; debrie ng discussions to clarify rating ambiguities; and measure-ment and feedback on individual performance. Reliability estimates during classroomtraining should be based on a comparison of the observer’s score against the criterionscore for the videotaped segment established by the research team. Field training shouldconsist of operationalizing the protocol and observing actual research subjects. Each ratershould conduct observations concurrently with the study master trainer. There are severalstatistical options for obtaining an index of rater reliability that make it less possible to ob-tain identical scores by chance (for this reason, percent agreement is not recommended).Cohen’s Kappa is a widely used statistic. While ≥ .8 is desired, ≥ .6 values are consideredacceptable. Paired t-test is another option. If raters are reliable, there would be high, sta-tistically signi cant Pearson correlation and low, statistically insigni cant t-test values.Interclass correlation, a form of ANOVA in which raters are the grouping variable andscale scores are the dependent variable, can also be used.

Data ManagementFor both scale development and utilization projects, issues of data management must be considered. While many of the speci c details are not reported in research reports,

a “behind-the-scenes process” to ensure the accuracy of the dataset should precede anyanalysis (i.e., the data must pass a “quality assurance check”). Embedding error-preven-tion strategies into the project can reduce many problems, but no ma er how carefullythe project is designed and carried out, it is impossible to eliminate all issues. Researchersshould plan to actively search for potential errors and correct them. They might encoun-ter errors with coding (e.g., missing or incorrect recode) or value (e.g., missing or outof range). Data cleaning is the process of detecting, diagnosing, and editing faulty data,while data editing involves changing the value of incorrect data (Van den Broeck, Arge-seanu Cunningham, Eeckels, & Herbst, 2005). The Van den Broeck team recommends thatreports provide details of data-cleaning methods, error types and rates, and error deletionand correction rates.

Some have viewed data editing as suspect, even bordering on data manipulation.

There are justi able concerns about where to draw the line between manipulation andresponsible editing. Good practice guidelines for data management require transparencyand proper documentation of all procedures. Once errors (e.g., extreme or missing val-ues) are identi ed, it is important to discern and follow the appropriate course of action.Researchers must decide whether to correct, transform, delete, or do nothing with theraw data. If pa erns of missing data are random and deleting a few subjects does not ad-versely a ect power, then cases can be deleted. Otherwise, values can be estimated fromprior knowledge or imputed (mean substitution).

Research projects that use the scales described in this book will either answer researchquestions or test hypotheses and will be classi ed as experimental, quasi-experimental, or




nonexperimental. These research projects need to use appropriate statistical tests. Severalfactors determine the selection of suitable tests, including study design, types and numbers of subjects, scale characteristics, and error prevention.

The researcher must ask and answer several questions during the initial planning ofthe project to prevent Type I and Type II errors. To avoid a Type I error, avoid commonmistakes in using statistics. For instance, use multivariate analysis of variance (MANOVA)instead of sequential t-tests. An analysis of covariance (ANCOVA) should be used to ac-count for di erences between groups and to test for changes over time as well as interac-tions between independent variables. A multivariate analysis of covariance (MANCOVA)tests for di erences when there are many dependent variables (with any number of inde-pendent variables) to examine the e ect of one or more independent variables on severaldependent variables simultaneously. A MANCOVA is used to examine how a dependentvariable varies over time, measuring that variable several times during a given time pe-riod. A MANCOVA can also determine whether or not other variables predict variabilityin the dependent variable over time.

To avoid a Type II error, a power analysis must be performed to determine thenumber of subjects required to detect a statistically signi cant di erence (if indeed oneexists) in the outcome measure (scale used) between control and intervention groups.Once the statistical test has been determined, the researcher then uses a table or com-puter program to calculate the sample size. Three numbers are placed in the formula.Two numbers are set by convention: 1) alpha = .05, to avoid a Type I error, and 2) power =.8, to avoid a Type II error. E ect size is the third number. The researcher selects an ef-fect size based on the best estimate of anticipated score di erences between groups. Forpreviously used scales, a review of published work would indicate meaningful scoredi erences between groups to help determine an estimated e ect size. When there areno data from previous research, as in the case of a newly developed scale, Munro (1999)suggests using one-half of the standard deviation reported in the development article to

estimate a moderate e ect size.Prior to using any of these statistical tests, the data should be closely examined and“managed.” Descriptive statistics should be computed on all study variables and reviewedfor potential issues that need addressing, especially systematic missing data, skewness,and outliers. When conducting research with persons who have advanced dementia, it isnot uncommon for the data to be skewed, since so many subjects may score “0” becausea scale “bo oms” or none of the de ning characteristics of a behavioral observation scaleare present. In this case, since the data are not normally distributed, nonparametric sta-tistics could be used. If one wants to use skewed data with parametric statistics, the datacan be transformed by using the square root of the value or –log 10 transformations. Also,one should consider data management when there are one or two severe outliers in alarge sample and when the mean di ers greatly from the median. If a few outliers’ scores

cause skewed data, consider recoding those scores to make them less extreme by assign-ing the outliers a score of one unit larger or smaller than the next extreme score in thedistribution. We suggest renaming edited variables for subsequent examination with theraw dataset (i.e., to compare the two datasets, raw and transformed, and to report thesedecisions and outcomes in the research report). Internal consistency reliabilities should becomputed on all subscales as well as scale totals for all time periods. If satisfactory alphas(≥ .7) are obtained, the scales can be used in subsequent analyses. If not, one must considerwhether or not to delete that variable from further analyses.

Before computing any analysis, the dataset needs to be tested to ensure that assump-tions underlying the statistic are met. For example, in addition to testing for and addressing




the normality of the sampling distribution, homogeneity of variance–covariance matrices,linearity, multicollinearity, and singularity need to be checked before using MANCOVA.Tests for univariate and multivariate outliers should be computed separately for each cellin the design in each hypothesis so that appropriate transformations or deletions of out-lying cases can be carried out.

When evaluating how scales performed after the initial report, look to see if the scalewas used as intended and check the methods section to review whether the se ings, sites,and subjects in the present study are congruent with those in the initial study wherevalidity/reliability were rst obtained. Is there a reason why the scale should have beenvalidated with the new population? Was the scale changed in any way that would requireadditional psychometric testing to ensure its accuracy? Were procedures and controls inplace to ensure that the data were collected and scored accurately and that the dataset isready to be used in the analysis?

Since this book focuses on scales, we concentrated on those design and methodsissues speci cally related to measurement. The goal is to overcome any potential inac-curacy in using scales. The accuracy of a scale depends both on the reliability and validityof the scale and on the accuracy with which data are collected. One also has to weigh theplusses and minuses of selecting a newly developed scale versus choosing a scale used inmany studies in order to make comparisons. Regardless, to ensure that the research usingthe scales in this book will contribute to the overall goal of improving care for personswith advanced dementia, studies’ outcome variables must be sensitive, reliable, and valid.

The 11 scales described in this book are suggested for use as outcome measures forexamining interventions to provide optimal care to persons with advanced dementia.Each scale has adequate psychometric properties speci c for this population. Readers areprovided with a description of the concepts measured by the scales, the original researchreport outlining development and testing of each scale, and summaries of how the scaleshave been used by others. We have also provided administration procedures and copies

of the scales that are suitable for duplication and make it easier to use the scales.

ReferencesCamberg, L., Woods, P., Ooi, W.L., Hurley, A., Volicer, L., Ashley, J., et al. (1999). Evaluation of simulated pres-

ence: A personalized approach to enhance well-being in persons with Alzheimer’s disease. Journal of the American Geriatrics Society, 47(4), 446–452.

Folstein, M., Folstein, S., & McHugh, P.J. (1975). “Mini-mental state,” a practical method for grading the cogni-tive state of patients for clinicians. Journal of Psychiatric Research, 12,189–198.

Hurley, A.C., Volicer, B.J., Hanrahan, P., Houde, S., & Volicer, L. (1992). Assessment of discomfort in advancedAlzheimer patients. Research in Nursing and Health , 15, 309–317.

Hurley, A.C., Volicer, L., Camberg, L., Ashley, J., Woods, P., Odenheimer, G., et al. (1999). Measurement ofobserved agitation in patients with dementia of the Alzheimer type. Journal of Mental Health and Aging , 5(2)117–133.

Knapp, T.R., & Brown, J.K. (1995). Focus on psychometrics: Ten measurement commandments that oftenshould be broken. Research in Nursing and Health , 18, 465–469.

Lynn, M.R. (1986). Determination and quanti cation of content validity. Nursing Research , 35, 382–385.Mahoney, E.K., Hurley, A.C., Volicer, L., Bell, M., Gianotis, P., Harsthorn, M., et al. (1999). Development and

testing of the resistiveness to care scale. Research in Nursing and Health , 22, 27–38.Munro, B.H. (1999). Quantitative research methods. Alzheimer Disease and Associated Disorders , 13(Suppl. 1),

S50–S53Nunnally, J.C., & Bernstein, I.H. (1994). Psychometric theory (3rd ed.). New York: McGraw Hill Book Company,

Inc.Sloane, P.D., Hoe er, B., Mitchell, C.M., McKenzie, D.A., Barrick, A.L., Rader, J., et al. (2004). E ect of person-

centered showering and the towel bath on bathing-associated aggression, agitation, and discomfort innursing home residents with dementia: A randomized, controlled trial. Journal of the American GeriatricsSociety , 52(11), 1795–1804.




Tabachnick, B.G., & Fidell, L.S. (2013). Using multivariate statistics. Boston: Allyn and Bacon.Tilden, V.P., Nelson, C.A., & May, B.A. (1990). Use of qualitative methods to enhance content validity. Nursing

Research , 39, 172–175.Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K. (2005). Data cleaning: Detecting,

diagnosing, and editing data abnormalities. PLOS Medicine (Open Access) , 2(10), e267.Volicer, L., Hurley, A.C., & Blasi, Z.V. (2001). Scales for evaluation of end-of-life care in dementia. Alzheimer

Disease & Associated Disorders , 15, 194–200.Volicer, L., Hurley, A.C., Lathi, D.C., & Kowall, N.W. (1994). Measurement of severity in advanced Alzheimer’s

disease. Journal of Gerontology , 49, M223–M226.Wal , C.F., Strickland, O.L., & Lenz, E.R. (2010). Measurement in nursing and health research (4th ed.). New York:

Springer Publishing Company.Warden, V., Hurley, A.C., & Volicer, L. (2003). Development and psychometric evaluation of the pain assess-

ment in advanced dementia (PAINAD) scale. Journal of the American Medical Directors Association, 9–15.Weiner, M.F., Martin-Cook, K., Svetlik, D.A., Saine, K., Foster, B., & Fontaine, C.S. (2000). The quality of life

in late-stage dementia (QUALID) scale. Journal of the American Medical Directors Association, May/June (1),114–116.

Wya , J., & Altman, D. (1995). Prognostic models: Clinically useful or quickly forgo en? British Medical Journal ,311(9), 1539–1541.

Documents

Assessment Scales for Advanced Dementia