The Research Self-Efficacy, Interest in Research, and Research Mentoring Experiences of Doctoral Students in Counselor Education

Doctoral programs in counselor education are believed to be developing effective researchers, yet there are few studies that examine the research constructs within counselor educator programs. The purpose of this study is to investigate a national sample of doctoral counselor education students’ research quality by measuring three constructs: 1) research self-efficacy, 2) interest in research and 3) research mentoring. A cross-sectional, correlational research design was used to test if doctoral students programs could predict these constructs. Also, the study investigated whether students’ research practices, (e.g., publishing refereed journal articles, et al.) correlated with their response levels. Keywords: counselor education and development, interest in research interest, research self-efficacy, research mentorin

How Low Should You Go? Low Response Rates and the Validity of Inference in IS Questionnaire Research

We believe IS researchers can and should do a better of job of improving (assuring) the validity of their findings by minimizing nonresponse error. To demonstrate that there is, in fact, a problem, we first present the response rates reported in six well-regarded IS journals and summarize how nonresponse error was estimated and handled in published IS research. To illustrate how nonresponse error may bias findings in IS research, we calculate its impact on confidence intervals. After demonstrating the impact of nonresponse on research findings, we discuss three post hoc remedies and three preventative measures for the IS researcher to consider. The paper concludes with a general discussion about nonresponse and its implications for IS research practice. In our delimitations section, we suggest directions for further exploring external validity

Number of Predictors and Multicollinearity: What are their Effects on Error and Bias in Regression?

The present Monte Carlo simulation study adds to the literature by analyzing parameter bias, rates of Type I and Type II error, and variance inflation factor (VIF) values produced under various multicollinearity conditions by multiple regressions with two, four, and six predictors. Findings indicate multicollinearity is unrelated to Type I error, but increases Type II error. Investigation of bias suggests that multicollinearity increases the variability in parameter bias, while leading to overall underestimation of parameters. Collinearity also increases VIF. In the case of all diagnostics however, increasing the number of predictors interacts with multicollinearity to compound observed problems

Multiple Indicator Stationary Time Series Models

This article is intended to complement previous research (Sivo, 1997; Sivo & Willson, 1998, in press) by discussing the propriety and practical advantages of specifying multivariate time series models in the context of structural equation modeling for time series and longitudinal panel data. Three practical considerations motivated this article. Unlike Marsh (1993), Sivo andWillson (2000) did not offer multiple indicator (latent order) equivalents to their autoregressive (AR), moving average (MA), and autoregressive-moving average (ARMA) models. Moreover, such models have yet to be discussed, despite Marsh\u27s (1993) advocacy for multiple indicator models in general. Further motivating multiple indicator extensions of the AR, MA, and ARMA equivalent models is the fact that longitudinal studies often collect data on more than 1 related variable per occasion. Such multiple indicator models capitalize on 1 of the chief analytical advantages of structural equation modeling in that measurement error may be estimated directly. © 2001, Lawrence Erlbaum Associates, Inc

Sensitivity Of Fit Indexes To Misspecified Structural Or Measurement Model Components: Rationale Of Two-Index Strategy Revisited

In previous research (Hu & Bentler, 1998, 1999), 2 conclusions were drawn: standardized root mean squared residual (SRMR) was the most sensitive to misspecified factor covariances, and a group of other fit indexes were most sensitive to misspecified factor loadings. Based on these findings, a 2-index strategy - that is, SRMR coupled with another index - was proposed in model fit assessment to detect potential misspecification in both the structural and measurement model parameters. Based on our reasoning and empirical work presented in this article, we conclude that SRMR is not necessarily most sensitive to misspecified factor covariances (structural model misspecification), the group of indexes (TLI, BL89, RNI, CFI, Gamma hat, Me, or RMSEA) are not necessarily more sensitive to misspecified factor loadings (measurement model misspecification), and the rationale for the 2-index presentation strategy appears to have questionable validity. Copyright © 2005, Lawrence Erlbaum Associates, Inc

Sensitivity Of Fit Indices To Model Misspecification And Model Types

The search for cut-off criteria of fit indices for model fit evaluation (e.g., Hu & Bender, 1999) assumes mat these fit indices are sensitive to model misspecification, but not to different types of models. If fit indices were sensitive to different types of models that are misspecified to the same degree, it would be very difficult to establish cut-off criteria that would be generally useful. The issue about SEM fit indices being sensitive to different types of models has not received sufficient attention, although there is some research suggesting that this might be the case (e.g., Kenny & McCoach, 2003). This study examines if fit indices are sensitive to different types of models while controlling for the severity of model misspecification. The findings show that most fit indices, including some very popular ones (e.g., RMSEA), may be sensitive to different types of models that have the same degree of specification error. The findings suggest that, for most fit indices, it would be difficult to establish cut-off criteria that would be generally useful in SEM applications. Copyright © 2007, Lawrence Erlbaum Associates, Inc

Using Θgoodness-Of-Fit Indexes In Assessing Mean Structure Invariance

In research concerning model invariance across populations, researchers have discussed the limitations of the conventional 2 difference test (2 test). There have been some research efforts in using goodness-of-fit indexes (i.e., goodness-of-fit indexes) for assessing multisample model invariance, and some specific recommendations have been made (Cheung Rensvold, 2002). Because goodness-of-fit indexes were designed to assess model fit in terms of covariance structure, it is not clear how they will perform when mean structure invariance is the research focus. This study extends the previous work (Cheung Rensvold, 2002), and evaluates how goodness-of-fit indexes perform in mean structure invariance analysis. By using a Monte Carlo simulation experiment, the performance of goodness-of-fit indexes in detecting population mean structure difference is evaluated. The findings suggest that, in general, goodness-of-fit indexes are so sensitive to model size that they are not generally useful in mean structure invariance analysis

Predicting Continued Use Of Online Teacher Professional Development And The Influence Of Social Presence And Sociability

This study examined how a well-established Technology Acceptance Model (TAM) could predict teachers\u27 intentions to continue using e-learning for professional development based on perceived ease of use and usefulness. Although studies have shown social interactions are important to teachers, no study has analyzed the mediating influence of social presence and sociability within e-learning professional development. Therefore, the original TAM was expanded to encompass user perceptions of social presence and sociability. Structural equation modeling was used to measure the mediating affects on their intention to continue using e-learning for their professional development. The results indicate that the expanded hypothesized model was a good predictor of continuance intention. Perceived ease of use, perceived usefulness and social presence were found to be significant determinants of teachers\u27 intent to continue using e-learning to meet their future professional development needs. The results have implications for educational leaders, designers and facilitators who want to promote teacher online professional development and embed e-learning conditions that will be readily embraced by classroom teachers. © 2011 The Authors. British Journal of Educational Technology © 2011 BERA

Clinical Decision-Making And Intuition: A Task Analysis Of 44 Experienced Counsellors

Background: Clinical decision-making and intuition are important concepts to counsellors. However, our understanding of clinical decision-making and intuition, that is the process whereby clinicians make sound therapeutic judgements, is not well understood and thus is an underrepresented area of research in counselling. Aim: The purpose of this study was to better understand the development of clinical decision-making and intuition and how it is utilised during therapeutic encounters. Methodology: This study used Q-Methodology to explore the responses of 44 experienced clinicians to a set of standardised clinical scenarios. Findings: The results suggested that experienced clinicians clustered into a single, common-factor response, which the researchers assert is the factor of intuition. Implications: The implications from the study\u27s findings include that (a) the study\u27s methodology shows promise for developing more advanced research designs that measure the influence of clinical decision-making and intuition on client outcomes and (b) the resulting single common factor suggests that experienced clinicians eventually transcend the confines of any single theoretical perspective

A Prelude To Strategic Management Of An Online Enterprise

Strategic management is expected to allow an organization to maximize given constraints and optimize limited resources in an effort to create a competitive advantage that leads to better results. For both for-profit and non-profit organizations, such strategic thinking helps the management make informed decisions and sustain long-term planning. To be customer centric, administrators of distance education may need to assess their constituents’ (e.g., students) needs and expectations prior to any strategic planning. In the present study the authors explore what viable factors may explain (a) student academic success, (b) student face-to-face interaction with the instructor, and (c) student separation of school life and personal life. Results and implications are discussed