The evaluation of interventions and policies designed to promote resilience, and research to understand the determinants and associations, require reliable and valid measures to ensure data quality. This paper systematically reviews the psychometric rigour of resilience measurement scales developed for use in general and clinical populations. Eight electronic abstract databases and the internet were searched and reference lists of all identified papers were hand searched. The focus was to identify peer reviewed journal articles where resilience was a key focus and/or is assessed. Two authors independently extracted data and performed a quality assessment of the scale psychometric properties. Nineteen resilience measures were reviewed; four of these were refinements of the original measure. All the measures had some missing information regarding the psychometric properties. Overall, the Connor-Davidson Resilience Scale, the Resilience Scale for Adults and the Brief Resilience Scale received the best psychometric ratings. The conceptual and theoretical adequacy of a number of the scales was questionable. We found no current 'gold standard' amongst 15 measures of resilience. A number of the scales are in the early stages of development, and all require further validation work. Given increasing interest in resilience from major international funders, key policy makers and practice, researchers are urged to report relevant validation statistics when using the measures.

/pdf/a-methodological-review-of-resilience-measurement-scales-9l403fighb.pdf

A methodological review of resilience measurement scales.

The authors report on the development and calibration of item banks for depression, anxiety, and anger as part of the Patient-Reported Outcomes Measurement Information System (PROMIS®). Comprehensive literature searches yielded an initial bank of 1,404 items from 305 instruments. After qualitative item analysis (including focus groups and cognitive interviewing), 168 items (56 for each construct) were written in a first person, past tense format with a 7-day time frame and five response options reflecting frequency. The calibration sample included nearly 15,000 respondents. Final banks of 28, 29, and 29 items were calibrated for depression, anxiety, and anger, respectively, using item response theory. Test information curves showed that the PROMIS item banks provided more information than conventional measures in a range of severity from approximately -1 to +3 standard deviations (with higher scores indicating greater distress). Short forms consisting of seven to eight items provided information comparable to legacy measures containing more items.

/pdf/item-banks-for-measuring-emotional-distress-from-the-patient-21olowl3pz.pdf

Item banks for measuring emotional distress from the Patient-Reported Outcomes Measurement Information System (PROMIS®): depression, anxiety, and anger.

The current study examines changes over time in a commonly used measure of dispositional empathy. A cross-temporal meta-analysis was conducted on 72 samples of American college students who completed at least one of the four subscales (Empathic Concern, Perspective Taking, Fantasy, and Personal Distress) of the Interpersonal Reactivity Index (IRI) between 1979 and 2009 (total N = 13,737). Overall, the authors found changes in the most prototypically empathic subscales of the IRI: Empathic Concern was most sharply dropping, followed by Perspective Taking. The IRI Fantasy and Personal Distress subscales exhibited no changes over time. Additional analyses found that the declines in Perspective Taking and Empathic Concern are relatively recent phenomena and are most pronounced in samples from after 2000.

https://www.ipearlab.org/media/publications/Changes_in_Dispositional_Empathy_-_Sara_Konrath.pdf

Changes in Dispositional Empathy in American College Students Over Time: A Meta-Analysis

The purpose of this study was to apply a set of rarely reported psychometric indices that, nevertheless, are important to consider when evaluating psychological measures. All can be derived from a standardized loading matrix in a confirmatory bifactor model: omega reliability coefficients, factor determinacy, construct replicability, explained common variance, and percentage of uncontaminated correlations. We calculated these indices and extended the findings of 50 recent bifactor model estimation studies published in psychopathology, personality, and assessment journals. These bifactor derived indices (most not presented in the articles) provided a clearer and more complete picture of the psychometric properties of the assessment instruments. We reached 2 firm conclusions. First, although all measures had been tagged “multidimensional,” unit-weighted total scores overwhelmingly reflected variance due to a single latent variable. Second, unit-weighted subscale scores often have ambiguous interpret...

Applying Bifactor Statistical Indices in the Evaluation of Psychological Measures.

An international team of investigators in 11 countries have worked collaboratively to develop a culturally and contextually relevant measure of youth resilience, the Child and Youth Resilience Measure (CYRM-28). The team used a mixed methods design that facilitated understanding of both common and unique aspects of resilience across cultures. Quantitative and qualitative stages to its development ensure the CYRM-28 has good content-related validity across research sites. Crossover comparison analyses of the findings from the quantitative administration of the pilot measure with 1,451 youth and qualitative interviews with 89 youth support the CYRM-28 as a culturally sensitive measure of youth resilience. The implications of this mixed methods approach to the development of measures for cross-cultural research are discussed.

Assessing Resilience Across Cultures Using Mixed Methods: Construction of the Child and Youth Resilience Measure:

EDITORIAL BOARD Robert Davison Aviles, Bradley University Harley E. Baker, California State University–Channel Islands Jean-Guy Blais, Universite de Montreal, Canada Catherine Y. Chang, Georgia State University Robert C. Chope, San Francisco State University Kevin O. Cokley, University of Missouri, Columbia Patricia B. Elmore, Southern Illinois University Shawn Fitzgerald, Kent State University John J. Fremer, Educational Testing Service Vicente Ponsoda Gil, Universidad Autonoma de Madrid, Spain Jo-Ida C. Hansen, University of Minnesota Charles C. Healy, University of California at Los Angeles Robin K. Henson, University of North Texas Flaviu Adrian Hodis, Victoria University of Wellington, New Zealand Janet K. Holt, Northern Illinois University David A. Jepsen, The University of Iowa Gregory Arief D. Liem, National Institute of Education, Nanyang Technological University Wei-Cheng J. Mau, Wichita State University Larry Maucieri, Governors State College Patricia Jo McDivitt, Data Recognition Corporation Peter F. Merenda, University of Rhode Island Matthew J. Miller, University of Maryland Ralph O. Mueller, University of Hartford Jane E. Myers, The University of North Carolina at Greensboro Philip D. Parker, University of Western Sydney Ralph L. Piedmont, Loyola College in Maryland Alex L. Pieterse, University at Albany, SUNY Nicholas J. Ruiz, Winona State University James P. Sampson, Jr., Florida State University William D. Schafer, University of Maryland, College Park William E. Sedlacek, University of Maryland, College Park Marie F. Shoffner, University of Virginia Len Sperry, Florida Atlantic University Kevin Stoltz, University of Mississippi Jody L. Swartz-Kulstad, Seton Hall University Bruce Thompson, Texas A&M University Timothy R. Vansickle, Minnesota Department of Education Steve Vensel, Palm Beach Atlantic University Dan Williamson, Lindsey Wilson College F. Robert Wilson, University of Cincinnati

measurement and evaluation in counseling and Development

Circuit failure prediction is used to predict occurrences of circuit failures, during system operation, before errors appear in system data and states. This technique is applicable for overcoming major scaled-CMOS reliability challenges posed by aging mechanisms such as Negative-Bias-Temperature-Instability (NBTI). This is possible because of the gradual nature of degradation associated with such aging mechanisms. Circuit failure prediction uses special on-chip circuits called aging sensors. In this paper, we experimentally demonstrate correct functionality and practicality of two flavors of flip-flop designs with built-in aging sensors using 90 nm test chips. We also present an aging-aware timing analysis technique to strategically place such flip-flops with built-in aging sensors at selective locations inside a chip for effective circuit failure prediction. This aging-aware timing analysis approach also minimizes the chip-level area impact of such aging sensors. Results from two 90 nm designs demonstrate the practicality and effectiveness of optimized circuit failure prediction with overall chip-level area impact of 2.5% and 0.6%.

Optimized Circuit Failure Prediction for Aging: Practicality and Promise

The human skin hosts an array of sensors that are capable of detecting and interpreting many traits important to how we function and survive. The goal of mimicking this capability in composites to create intelligent composite materials has led to the development of a bio-inspired stretchable network composed of numerous micro-fabricated sensors capable of detecting multiple stimuli. The components of the network are small scale and flexible making the network embeddable within complexly shaped composite layups and flexible structures with minimal impact on the host structure. This paper outlines recent progress in ongoing work to develop the bio-inspired network in order to create intelligent composite materials.

Bio-inspired stretchable network-based intelligent composites:

This paper uses 90nm transistor-level experimental data, device modeling, and circuit simulations to establish the following results: 1. A transistor with defective gate- oxide, i.e., a gate-oxide early-life failure (ELF) candidate transistor, produces gradually degraded drive currents over time before it completely loses its transistor characteristics; 2. The above phenomenon results in gradual increase in delays of digital circuit paths containing the ELF candidate transistor before the circuit produces functional failures; 3. Gradual delay shifts caused by ELF candidate transistors are large enough to be detected using inexpensive digital techniques. These results can be utilized to overcome scaled-CMOS reliability challenges through ELF identification during production test or on-line during system operation.

Gate-Oxide Early Life Failure Prediction

Magnetic biosensors have emerged as a sensitive and versatile platform for high performance medical diagnostics. These magnetic biosensors require well-tailored magnetic particles as detection probes, which need to give rise to a large and specific biological signal while showing very low nonspecific binding. This is especially important in wash-free bioassay protocols, which do not require removal of particles before measurement, often a necessity in point of care diagnostics. Here we show that magnetic interactions between magnetic particles and magnetized sensors dramatically impact particle transport and magnetic adhesion to the sensor surfaces. We investigate the dynamics of magnetic particles' biomolecular binding and magnetic adhesion to the sensor surface using microfluidic experiments. We elucidate how flow forces can inhibit magnetic adhesion, greatly diminishing or even eliminating nonspecific signals in wash-free magnetic bioassays, and enhancing signal to noise ratios by several orders of magnitude. Our method is useful for selecting and optimizing magnetic particles for a wide range of magnetic sensor platforms.

/pdf/high-performance-wash-free-magnetic-bioassays-through-3mtpzoznub.pdf

Kyunglok Kim

Papers

measurement and evaluation in counseling and Development

Optimized Circuit Failure Prediction for Aging: Practicality and Promise

Bio-inspired stretchable network-based intelligent composites:

Gate-Oxide Early Life Failure Prediction

High performance wash-free magnetic bioassays through microfluidically enhanced particle specificity