PENGARUH REWARD TERHADAP KNOWLEDGE SHARING PERANGKAT DESA BERDAMPAK PENINGKATAN PARTISIPASI MASYARAKAT

Penelitian ini memiliki tujuan mendapatkan hasil dari pengaruh reward terhadap knowledge sharing perangkat desa berdampak terhadap peningkatan partisipasi masyarakat. Reward pada penelitian ini terbagi atas Extrinsic Rewards dan Intrinsic Rewards. Penelitian ini dilaksanakan di Kabupaten Kerinci dengan Perangkat desa sebagai subjek penelitian. Penelitian ini dilaksanakan pada Bulan Juni 2020 - September 2020. Perangkat Desa di Kabupaten Kerinci merupakan populasi dalam penelitian ini dengan menggunakan metode penarikan sampel adalah Cluster Sampling dengan mengelompokkan sampel didasari wilayah dengan jumlah sampel adalah 108 responden. Sumber data didapatkan dari wawancara (interview) serta daftar pertanyaan (questionnaire). Pada penelitian ini menggunakan analisis data Structural Equation Models (SEM) serta menggunakan AMOS sebagai alat analisis. Hasil penelitian didapatkan koefisien determinasi besar pengaruh knowledge sharing yang dapat dijelaskan oleh variabel extrinsic rewards dan intrinsic rewards sebesar 17%. Sedangkan koefisien determinasi persamaan Partisipasi Masyarakat sebesar 20,2%. Hasil dari penelitian didapatkan dari pengujian hipotesis bahwa extrinsic rewards dan intrinsic rewards memiliki pengaruh yang positif dan signifikan terhadap knowledge sharing, knowledge sharing dan intrinsic rewards berpengaruh positif dan signifikan terhadap partisipasi masyarakat sedangkan extrinsic rewards berpengaruh tidak signifikan terhadap partisipasi masyarakat. Knowledge Sharing dalam penelitian ini bukan merupakan variabel intervening karena pengaruh langsung extrinsic rewards terhadap partisipasi masyarakat lebih besar dari pada pengaruh tidak langsung melalui knowledge sharing dan pengaruh langsung intrinsic rewards terhadap partisipasi masyarakat juga lebih besar dari pengaruh tidak langsung terhadap partisipasi masyarakat melalui knowledge sharing. Hasil dari penelitian ini memberikan bukti empiris sebagai panduan bagi pemerintahan dan perangkat desa untuk menetapkan strategi yang tepat dalam knowledge sharing dan meningkatkan partisipasi masyarakat termasuk dampak terhadap pembangunan daerah

Download Full-text

Structural equation modelling with complex sampling designs and non-random attrition: A tutorial using lavaan and Mplus

10.31234/osf.io/jcz4d ◽

2021 ◽

Author(s):

Aja Louise Murray ◽

Anastasia Ushakova ◽

Helen Wright ◽

Tom Booth ◽

Peter Lynn

Keyword(s):

Structural Equation ◽

Large Scale ◽

Structural Equation Models ◽

Cluster Sampling ◽

Complex Sampling ◽

Latent Trajectory ◽

Adequate Sampling ◽

Complex Sampling Designs ◽

Selection Probabilities ◽

Pseudo Maximum Likelihood

Complex sampling designs involving features such as stratification, cluster sampling, and unequal selection probabilities are often used in large-scale longitudinal surveys to improve cost-effectiveness and ensure adequate sampling of small or under-represented groups. However, complex sampling designs create challenges when there is a need to account for non-random attrition; a near inevitability in social science longitudinal studies. In this article we discuss these challenges and demonstrate the application of weighting approaches to simultaneously account for non-random attrition and complex design in a large UK-population representative survey. Using an auto-regressive latent trajectory model with structured residuals (ALT-SR) to model the relations between relationship satisfaction and mental health in the Understanding Society study as an example, we provide guidance on implementation of this approach in both R and Mplus is provided. Two standard error estimation approaches are illustrated: pseudo-maximum likelihood robust estimation and Bootstrap resampling. A comparison of unadjusted and design-adjusted results also highlights that ignoring the complex survey designs when fitting structural equation models can result in misleading conclusions.

Download Full-text

Measuring the Big Five in Self-Report and Other Ratings

European Journal of Psychological Assessment ◽

10.1027//1015-5759.16.1.31 ◽

2000 ◽

Vol 16 (1) ◽

pp. 31-43 ◽

Cited By ~ 24

Author(s):

Claudio Barbaranelli ◽

Gian Vittorio Caprara

Keyword(s):

Big Five ◽

Structural Equation ◽

Structural Equation Models ◽

Error Variance ◽

The Self ◽

Self Report ◽

Sources Of Information ◽

Third Person ◽

Fit Indexes ◽

Response Modes

Summary: The aim of the study is to assess the construct validity of two different measures of the Big Five, matching two “response modes” (phrase-questionnaire and list of adjectives) and two sources of information or raters (self-report and other ratings). Two-hundred subjects, equally divided in males and females, were administered the self-report versions of the Big Five Questionnaire (BFQ) and the Big Five Observer (BFO), a list of bipolar pairs of adjectives ( Caprara, Barbaranelli, & Borgogni, 1993 , 1994 ). Every subject was rated by six acquaintances, then aggregated by means of the same instruments used for the self-report, but worded in a third-person format. The multitrait-multimethod matrix derived from these measures was then analyzed via Structural Equation Models according to the criteria proposed by Widaman (1985) , Marsh (1989) , and Bagozzi (1994) . In particular, four different models were compared. While the global fit indexes of the models were only moderate, convergent and discriminant validities were clearly supported, and method and error variance were moderate or low.

Download Full-text

Collective Efficacy and Organizational Commitment in an Italian City Hall

European Psychologist ◽

10.1027/1016-9040.14.4.363 ◽

2009 ◽

Vol 14 (4) ◽

pp. 363-371 ◽

Cited By ~ 14

Author(s):

Laura Borgogni ◽

Silvia Dello Russo ◽

Laura Petitta ◽

Gary P. Latham

Keyword(s):

Organizational Commitment ◽

Collective Efficacy ◽

Structural Equation ◽

Structural Equation Models ◽

Practical Significance ◽

City Hall ◽

Italian City ◽

Public Sector Managers ◽

The Individual ◽

Level Group

Employees (N = 170) of a City Hall in Italy were administered a questionnaire measuring collective efficacy (CE), perceptions of context (PoC), and organizational commitment (OC). Two facets of collective efficacy were identified, namely group and organizational. Structural equation models revealed that perceptions of top management display a stronger relationship with organizational collective efficacy, whereas employees’ perceptions of their colleagues and their direct superior are related to collective efficacy at the group level. Group collective efficacy had a stronger relationship with affective organizational commitment than did organizational collective efficacy. The theoretical significance of this study is in showing that CE is two-dimensional rather than unidimensional. The practical significance of this finding is that the PoC model provides a framework that public sector managers can use to increase the efficacy of the organization as a whole as well as the individual groups that compose it.

Download Full-text

A Cautionary Note on Incremental Fit Indices Reported by LISREL

Methodology ◽

10.1027/1614-1881.1.2.81 ◽

2005 ◽

Vol 1 (2) ◽

pp. 81-85 ◽

Cited By ~ 5

Author(s):

Stefan C. Schmukle ◽

Jochen Hardt

Keyword(s):

Factor Analysis ◽

Maximum Likelihood ◽

Structural Equation ◽

Structural Equation Models ◽

Null Model ◽

Likelihood Estimation ◽

Parameter Estimates ◽

Fit Indices ◽

Cautionary Note ◽

Confirmatory Factor

Abstract. Incremental fit indices (IFIs) are regularly used when assessing the fit of structural equation models. IFIs are based on the comparison of the fit of a target model with that of a null model. For maximum-likelihood estimation, IFIs are usually computed by using the χ2 statistics of the maximum-likelihood fitting function (ML-χ2). However, LISREL recently changed the computation of IFIs. Since version 8.52, IFIs reported by LISREL are based on the χ2 statistics of the reweighted least squares fitting function (RLS-χ2). Although both functions lead to the same maximum-likelihood parameter estimates, the two χ2 statistics reach different values. Because these differences are especially large for null models, IFIs are affected in particular. Consequently, RLS-χ2 based IFIs in combination with conventional cut-off values explored for ML-χ2 based IFIs may lead to a wrong acceptance of models. We demonstrate this point by a confirmatory factor analysis in a sample of 2449 subjects.

Download Full-text

Forced Zero Cross-Loading Misspecifications in Measurement Component of Structural Equation Models

Methodology ◽

10.1027/1614-2241/a000084 ◽

2014 ◽

Vol 10 (4) ◽

pp. 138-152 ◽

Cited By ~ 5

Author(s):

Hsien-Yuan Hsu ◽

Susan Troncoso Skidmore ◽

Yan Li ◽

Bruce Thompson

Keyword(s):

Structural Equation ◽

Structural Model ◽

Structural Equation Models ◽

Equation Model ◽

Model Fit ◽

Model Specification ◽

Simulation Design ◽

Measurement Models ◽

Fit Statistics ◽

Path Coefficients

The purpose of the present paper was to evaluate the effect of constraining near-zero parameter cross-loadings to zero in the measurement component of a structural equation model. A Monte Carlo 3 × 5 × 2 simulation design was conducted (i.e., sample sizes of 200, 600, and 1,000; parameter cross-loadings of 0.07, 0.10, 0.13, 0.16, and 0.19 misspecified to be zero; and parameter path coefficients in the structural model of either 0.50 or 0.70). Results indicated that factor pattern coefficients and factor covariances were overestimated in measurement models when near-zero parameter cross-loadings constrained to zero were higher than 0.13 in the population. Moreover, the path coefficients between factors were misestimated when the near-zero parameter cross-loadings constrained to zero were noteworthy. Our results add to the literature detailing the importance of testing individual model specification decisions, and not simply evaluating omnibus model fit statistics.

Download Full-text