Conclusions : Of the most commonly used measures , the majority lacked sufficient evidence to warrant their use with older adults .
Based on psychometric evidence , three measures ( Beck Anxiety Inventory , Penn State Worry Question naire , and Geriatric Mental Status Examination ) showed psychometric properties sufficient to justify the use of these instruments when assessing anxiety in older adults .
In addition , two measures developed specifically for older adults ( Worry Scale and Geriatric Anxiety Inventory ) were also found to be appropriate for use with older adults
Objectives : The authors set out to systematic ally review the research literature in order to identify the anxiety measures most commonly used in the assessment of older adults .
Once identified , the literature was review ed to determine the extent to which these instruments had age-relevant norms and psychometric data supporting their use with older adults .
Editorial: an author's checklist for measure development and validation manuscripts.
A recent special issue of the Journal of Pediatric Psychology included papers focused on evidence -based assessment across several broad domains of assessment in pediatric psychology ( e.g. , adherence , pediatric pain , and quality of life ) . In one of these papers , Holmbeck et al. ( 2008 ) review ed strengths and limitations of existing measures of psychosocial adjustment and psychopathology , concluding that many measures lacked supporting psychometric data ( e.g. , basic indices of reliability and validity ) that would permit a complete evaluation of these measures . Given that measure development and validation papers are frequently published in JPP ( Brown , 2007 ) , it is important that we attend to guiding psychometric principles when developing and disseminating data on new measures to be employed with pediatric population s ( Nunnally & Bernstein , 1994 ) . Thus , the purpose of this paper is to present and describe a checklist for authors to use when su bmi tting measure development papers to JPP . This checklist is included in the Appendix and is also included at the following link on the JPP website : http://www.oxfordjournals.org/our_journals/jpepsy/for _ authors /measure%20development%20checklist.pdf Findings presented by Holmbeck et al. ( 2008 ) indicated that 34 of the 37 measures review ed met pre-established “ evidence -based assessment ” ( EBA ) criteria for “ well-established ” measures ( Cohen et al. , 2008 ) . To be considered “ well-established , ” a measure had to have been presented in at least two peer- review ed journal articles by different investigatory teams , have demonstrated adequate levels of reliability and validity , and be accompanied by supporting information ( e.g. , a measure manual ) . Although most measures that we review ed met these criteria , we also found that most of the 34 “ well-established ” measures were hampered by at least one major psychometric flaw and /or lacked important psychometric data . We concluded that a more fine-grained EBA classification system is needed . One important distinction in this literature relates to differences between empirically supported assessment and evidence -based assessment . This type of distinction was first discussed in the literature on clinical interventions ( e.g. , Spring , 2007 ) . An empirically supported intervention is one that has demonstrated efficacy in r and omized clinical trials or clinic-based effectiveness trials . An evidence -based intervention has empirical support in the manner just described , but also “ integrates research evidence , clinical expertise , and patient preferences and characteristics … empirically-supported treatments  ( ESTs )  are an important component of evidence -based practice ( EBP ) , but EBP can not be reduced to ESTs ” ( Spring , 2007 , p.611 ) . Applying these terms to the field of assessment and measure development efforts , an empirically supported assessment measure would be one that demonstrates satisfactory psychometric characteristics , broadly defined . To be evidence based , the measure should also demonstrate utility in clinical setting s , be useful in making diagnoses , be sensitive to treatment effects , and /or provide incremental validity above and beyond other similar measures . Although papers in the special issue of JPP frequently referred to “ evidence -based assessment ” ( Cohen et al. , 2009 ) , the articles included in the issue tended to evaluate the degree to which the measures were empirically-supported rather than evidence based . To be “ evidence -based , ” our review s would have needed to integrate an evaluation of clinical utility , diagnostic utility , and treatment sensitivity with the empirical psychometric data presented in each review . As noted , the published review s were more likely to focus on the latter rather than on the former . As suggested by Mash and Hunsley ( 2005 ) , detailed EBA profiles would provide a complete evaluation of evidence across each of several psychometric and clinical ly relevant dimensions , including : ( a ) internal consistency , ( b ) test – retest reliability , ( c ) the availability of normative data , ( d ) content validity , ( e ) construct validity , ( f ) convergent and discriminant validity , ( g ) criterion-related validity , ( h ) incremental validity , ( i ) clinical utility , ( j ) diagnostic utility , and ( k ) treatment sensitivity . The focus on incremental validity and clinical and diagnostic utility raises the bar from a focus on “ empirical support ” ( i.e. , where the focus would tend to be primarily on psychometric data ) to a broad focus on the “ evidence base ” for a measure . In developing the checklist that is the focus of this article , we attempted to provide a list of criteria relevant to establishing the evidence base ( and not just empirical support ) for a measure . In addition to shifting the focus from providing “ empirical support ” for a measure to providing an “ evidence base ” for our instruments , a checklist for measure development papers would permit JPP review ers to evaluate such papers in the same way that review ers of r and omized clinical trials make use of the Consoli date d St and ards of Reporting Trials ( CONSORT ) checklist and flowchart ( Altman et al. , 2001 ) . The CONSORT checklist contains reporting st and ards with respect to method ological features of and the manner in which results are reported in clinical trials . Moreover , authors are required to provide a flowchart that describes details of sample recruitment and attrition during the course of the study . Thus , a checklist for measure development papers would serve two interrelated purpose s : ( a ) it would provide guidance to authors as they embark on the measure development process and would provide a list of criteria authors can use as they develop an evidence base for their measures , and ( b ) it would begin to st and ardize the manner in which psychometric and other assessment -related data are presented in measure development papers for this journal . Before providing a more detailed overview of the checklist , it is important to note that this checklist is rather exhaustive ( see Appendix ) . As such , it represents what would “ ideally ” be expected for a measure development or validation manuscript rather than minimal criteria for such papers . No one paper can provide a complete evaluation of all important psychometric and clinical ly relevant dimensions that will establish once- and -for-all the evidence base for a measure . Instrument refinement is part of a measure development process that gradually builds an evidence base for a scale ( see Smith & McCarthy , 1995 , for suggestions on measure refinement ) . Indeed , the validation of any measure is a cumulative process that occurs across many different types of research studies and across research programs
Conclusions : Of the most commonly used measures , the majority lacked sufficient evidence to warrant their use with older adults .
Based on psychometric evidence , three measures ( Beck Anxiety Inventory , Penn State Worry Question naire , and Geriatric Mental Status Examination ) showed psychometric properties sufficient to justify the use of these instruments when assessing anxiety in older adults .
In addition , two measures developed specifically for older adults ( Worry Scale and Geriatric Anxiety Inventory ) were also found to be appropriate for use with older adults
Objectives : The authors set out to systematic ally review the research literature in order to identify the anxiety measures most commonly used in the assessment of older adults .
Once identified , the literature was review ed to determine the extent to which these instruments had age-relevant norms and psychometric data supporting their use with older adults .