Many interventions found to be effective in health services research studies fail to translate into meaningful patient care outcomes across multiple contexts. Health services researchers recognize the need to evaluate not only summative outcomes but also formative outcomes to assess the extent to which implementation is effective in a specific setting, prolongs sustainability, and promotes dissemination into other settings. Many implementation theories have been published to help promote effective implementation. However, they overlap considerably in the constructs included in individual theories, and a comparison of theories reveals that each is missing important constructs included in other theories. In addition, terminology and definitions are not consistent across theories. We describe the Consolidated Framework For Implementation Research (CFIR) that offers an overarching typology to promote implementation theory development and verification about what works where and why across multiple contexts. We used a snowball sampling approach to identify published theories that were evaluated to identify constructs based on strength of conceptual or empirical support for influence on implementation, consistency in definitions, alignment with our own findings, and potential for measurement. We combined constructs across published theories that had different labels but were redundant or overlapping in definition, and we parsed apart constructs that conflated underlying concepts. The CFIR is composed of five major domains: intervention characteristics, outer setting, inner setting, characteristics of the individuals involved, and the process of implementation. Eight constructs were identified related to the intervention (e.g., evidence strength and quality), four constructs were identified related to outer setting (e.g., patient needs and resources), 12 constructs were identified related to inner setting (e.g., culture, leadership engagement), five constructs were identified related to individual characteristics, and eight constructs were identified related to process (e.g., plan, evaluate, and reflect). We present explicit definitions for each construct. The CFIR provides a pragmatic structure for approaching complex, interacting, multi-level, and transient states of constructs in the real world by embracing, consolidating, and unifying key constructs from published implementation theories. It can be used to guide formative evaluations and build the implementation knowledge base across multiple studies and settings.

/pdf/fostering-implementation-of-health-services-research-4xghpqk7rh.pdf

Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science

The following paper presents current thinking and research on fit indices for structural equation modelling. The
paper presents a selection of fit indices that are widely regarded as the most informative indices available to researchers.
As well as outlining each of these indices, guidelines are presented on their use. The paper also provides reporting
strategies of these indices and concludes with a discussion on the future of fit indices.

/pdf/structural-equation-modelling-guidelines-for-determining-muejvmc1az.pdf

Structural Equation Modelling: Guidelines for Determining Model Fit

For structural equation models, a huge variety of fit indices has been developed. These indices, however, can point to conflicting conclusions about the extent to which a model actually matches the observed data. The present article provides some guidelines that should help applied researchers to evaluate the adequacy of a given structural equation model. First, as goodness-of-fit measures depend on the method used for parameter estimation, maximum likelihood (ML) and weighted least squares (WLS) methods are introduced in the context of structural equation modeling. Then, the most common goodness-of-fit indices are discussed and some recommendations for practitioners given. Finally, we generated an artificial data set according to a "true" model and analyzed two misspecified and two correctly specified models as examples of poor model fit, adequate fit, and good fit.

/pdf/evaluating-the-fit-of-structural-equation-models-tests-of-4lmsoac5b1.pdf

Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures.

Marketing managers must know the time orientation of a customer to select and use marketing tools that correspond to the time horizons of the customer. Insufficient understanding of a customer's ti...

https://www.jstor.org/stable/pdfplus/1252265.pdf

Determinants of Long-Term Orientation in Buyer-Seller Relationships:

Cross-sectional studies of attitude-behavior relationships are vulnerable to the inflation of correlations by common method variance (CMV). Here, a model is presented that allows partial correlation analysis to adjust the observed correlations for CMV contamination and determine if conclusions about the statistical and practical significance of a predictor have been influenced by the presence of CMV. This method also suggests procedures for designing questionnaires to increase the precision of this adjustment.

Accounting for common method variance in cross-sectional research designs.

Hayduk is equally at ease explaining the simplest and most advanced applications of the program ...Hayduk has written more than just a solid text for use in advanced graduate courses on statistical modeling. Those with a firm mathematical background who wish to learn about the approach, or those who know a little about the program and want to know more, will find this an excellent reference.

https://www.jstor.org/stable/info/1165186

Structural equation modeling with LISREL: essentials and advances

Personal space: Where we now stand.

LISREL: Issues, Debates, and Strategies examines issues of concern to researchers already familiar with the basics of structural equation modeling. Building on his earlier work in Structural Equation Modeling in LISREL, Leslie Hayduk explains procedures that maximize researchers' control over the meanings of their concepts and integrates the modeling of single and multiple indicators. The constraints and deceptions of the factor model are used to highlight measurement issues and the debate over whether one should estimate a measurement model prior to estimating a structural model is reviewed, extended, and evaluated. For sociologists, political scientists, psychologists, and researchers in science, medicine, and education, LISREL offers a wealth of useful information: A loop-equivalent to the standard recursive model is presented as grounding an interpretation style that can be used to challenge any or all the effect estimates from recursive models. (Loop-equivalent provide an appealing alternative conceptualization of longitudinal processes.) Models with acceptable negative R2's are discussed and LAR2 is introduced as a more appropriate indication of error variance for variables touching reciprocal relationships or loops. The logic connecting partial correlations, tetrads, equivalent models, and LISREL is presented and recent advances in this area are reviewed. Phantom variables and a modeling trick combine to illustrate how stacked models based on differing sets of indicator variables can be used: to identify otherwise unidentified models, to control for unmeasured variables, and to integrate models based on diverse data sets. A more appropriate Monte-Carlo-test model is proposed and a brief review of the recent literature is provided.

LISREL Issues, Debates and Strategies

Testing! testing! one, two, three - Testing the theory in structural equation models!

Background
Structural equation modeling developed as a statistical melding of path analysis and factor analysis that obscured a fundamental tension between a factor preference for multiple indicators and path modeling’s openness to fewer indicators.

/pdf/should-researchers-use-single-indicators-best-indicators-or-1oh1urvarl.pdf

Leslie A. Hayduk

Papers

Structural equation modeling with LISREL: essentials and advances

Personal space: Where we now stand.

LISREL Issues, Debates and Strategies

Testing! testing! one, two, three - Testing the theory in structural equation models!

Should researchers use single indicators, best indicators, or multiple indicators in structural equation models?