Immigrant students and their teachers – exploring various constellations of acculturation orientations and their impact on school adjustment

ABSTRACT

The present study examined what impact various constellations of acculturation orientations of immigrant students and their teachers have on the students’ school adjustment. For this purpose, the study analysed the acculturation orientations of 1106 5th graders and their class teachers in Switzerland. Using multiple regression analysis, we found that immigrant students were more satisfied at school if their teachers had similar attitudes about cultural assimilation and cultural diversity and could, thus, be said to have congruent acculturation orientations. With respect to students’ academic self-concept, immigrant students with a minority orientation whose teachers value cultural diversity including the heritage culture of their students showed a lower academic self-concept despite matching acculturation orientations. We interpret this result as a dilemma of teachers between the endeavour to develop culturally sensitive attitudes and behaviours and the risk to create stereotype threat and its negative effects through addressing cultural differences. Furthermore, a mismatch between the students’ (bi-)national identification at school and the teacher’s perception of the student as an immigrant or a native-born student could be shown to have adverse effects on the academic self-concept in the group of students with older teachers (50+ years).     

How Turkish Private Hospitals use Social media: A Qualitative Study

The purpose of this study was to examine which social media tools are mostly used by private hospitals in Ankara and to reveal the advantages and disadvantages of social media tools at an institutional level. Based on the purposive sampling method, it was decided to reach only the people within the scope of the research and hence qualitative interviews was performed with 15 people. The interviewed people consisted of managers responsible for social media activities at the corporate level hospitals. The data obtained from in-depth interviews were analyzed by content analysis. Accordingly, it was found that the social media tools most commonly used by the surveyed participants was Facebook, Twitter, and Instagram respectively, primarily for sharing health-related information and the purpose of advertisement. It was concluded that 26.6% of social media activities at the corporate level are being conducted by the communication department and the social media is mostly being used to share health related information by the hospitals. The survey also found that hospitals had increased recognition as an advantage of social media, but difficulty in deleting comments related to the hospital was found to be a disadvantage. In this research, only the social media activities of the hospitals were discussed with the managers at the institutional level Thus, future studies that evaluate the views of managers, service providers, and service users about social media are required. But the results from this study are expected to provide hospital managers with important clues for planning social media activities.     

Multi-criteria Decision-Making for Greenways: The Case of Trabzon,Turkey

Greenways are multi-objective planning tools for sustainable development that take several criteria into account. In order to resolve the conflicts among objectives, greenway planning requires advanced strategies for decision-making processes and techniques. To aid in this, multi-criteria decision-making (MCDM) systems have significant capacity to analytically prioritize and select alternatives. This research develops a strategy for defining, prioritizing and selecting greenway alternatives with the support of Analytical Hierarchy Process (AHP) based on an empirical study in the urban region of Trabzon (Turkey). The study finds that AHP has promising capacity to analytically prioritize alternatives and rationally select the best alternative.     

The (social) innovation – subjective well-being nexus: subjective well-being impacts as an additional assessment metric of technological and social innovations

It cannot be assumed that technological innovations and social innovations are desirable per se. An innovation usually has positive as well as negative impacts on people, making its normative assessment a major research challenge. This has become increasingly recognized in the innovation literature. At the same time, subjective well-being (SWB) research has developed and matured largely independently of innovation studies. This paper puts forward a general conceptual framework of the (social) innovation – SWB nexus as a step towards the development of a unified normative “well-being theory of innovation” that applies to all types of innovation, e.g. social as well as technological innovation. Its main feature is the inclusion of multiple direct and indirect SWB impacts of processes as well as of outcomes. The framework can act as a focussing device that raises awareness of the many potential SWB impacts of innovation. SWB impacts should be used as an additional metric in the assessment of innovation, complementing other assessment approaches.     

Eonyergenzgeschwindigkeit der verteilung einer parametersehätzung für stationäre gauβsche folgen

This paper deals with speed of convergence to the normal distribution of the distribution of parameter estimates considered by Whittle and Walker for stationary Gaussian random sequences. The result obtained is based on an estimation of the speed of convergence for the distribution of an integrated periodogram.     

Robust ridge and robust Liu estimator for regression based on the LTS estimator

In the multiple linear regression analysis, the ridge regression estimator and the Liu estimator are often used to address multicollinearity. Besides multicollinearity, outliers are also a problem in the multiple linear regression analysis. We propose new biased estimators based on the least trimmed squares (LTS) ridge estimator and the LTS Liu estimator in the case of the presence of both outliers and multicollinearity. For this purpose, a simulation study is conducted in order to see the difference between the robust ridge estimator and the robust Liu estimator in terms of their effectiveness; the mean square error. In our simulations, the behavior of the new biased estimators is examined for types of outliers: X-space outlier, Y-space outlier, and X-and Y-space outlier. The results for a number of different illustrative cases are presented. This paper also provides the results for the robust ridge regression and robust Liu estimators based on a real-life data set combining the problem of multicollinearity and outliers.     

Stepwise Regression in Mixed Quantitative Linear Models with Autocorrelated Errors

ABSTRACT

In the stepwise procedure of selection of a fixed or a random explanatory variable in a mixed quantitative linear model with errors following a Gaussian stationary autocorrelated process, we have studied the efficiency of five estimators relative to Generalized Least Squares (GLS): Ordinary Least Squares (OLS), Maximum Likelihood (ML), Restricted Maximum Likelihood (REML), First Differences (FD), and First-Difference Ratios (FDR). We have also studied the validity and power of seven derived testing procedures, to assess the significance of the slope of the candidate explanatory variable x ₂ to enter the model in which there is already one regressor x ₁. In addition to five testing procedures of the literature, we considered the FDR t-test with n ? 3 df and the modified t-test with n? ? 3 df for partial correlations, where n? is Dutilleul's effective sample size. Efficiency, validity, and power were analyzed by Monte Carlo simulations, as functions of the nature, fixed vs. random (purely random or autocorrelated), of x ₁ and x ₂, the sample size and the autocorrelation of random terms in the regression model. We report extensive results for the autocorrelation structure of first-order autoregressive [AR(1)] type, and discuss results we obtained for other autocorrelation structures, such as spherical semivariogram, first-order moving average [MA(1)] and ARMA(1,1), but we could not present because of space constraints. Overall, we found that:

1. the efficiency of slope estimators and the validity of testing procedures depend primarily on the nature of x ₂, but not on that of x ₁;

2. FDR is the most inefficient slope estimator, regardless of the nature of x ₁ and x ₂;

3. REML is the most efficient of the slope estimators compared relative to GLS, provided the specified autocorrelation structure is correct and the sample size is large enough to ensure the convergence of its optimization algorithm;

4. the FDR t-test, the modified t-test and the REML t-test are the most valid of the testing procedures compared, despite the inefficiency of the FDR and OLS slope estimators for the former two;

5. the FDR t-test, however, suffers from a lack of power that varies with the nature of x ₁ and x ₂; and

6. the modified t-test for partial correlations, which does not require the specification of an autocorrelation structure, can be recommended when x ₁ is fixed or random and x ₂ is random, whether purely random or autocorrelated. Our results are illustrated by the environmental data that motivated our work.

     

Power of One-Sample Location Tests Under Distributions with Equal Lévy Distance

In this article, we study the power of one-sample location tests under classical distributions and two supermodels which include the normal distribution as a special case. The distributions of the supermodels are chosen in such a way that they have equal distance to the normal as the logistic, uniform, double exponential, and the Cauchy, respectively. As a measure of distance we use the Lévy metric. The tests considered are two parametric tests, the t-test and a trimmed t-test, and two nonparametric tests, the sign test and the Wilcoxon signed-rank tests. It turns out that the power of the tests, first of all, does not depend on the Lévy distance but on the special chosen supermodel.     

Checking Normality and Homoscedasticity in the General Linear Model Using Diagnostic Plots

Inference for the general linear model makes several assumptions, including independence of errors, normality, and homogeneity of variance. Departure from the latter two of these assumptions may indicate the need for data transformation or removal of outlying observations. Informal procedures such as diagnostic plots of residuals are frequently used to assess the validity of these assumptions or to identify possible outliers. A simulation-based approach is proposed, which facilitates the interpretation of various diagnostic plots by adding simultaneous tolerance bounds. Several tests exist for normality or homoscedasticity in simple random samples. These tests are often applied to residuals from a linear model fit. The resulting procedures are approximate in that correlation among residuals is ignored. The simulation-based approach accounts for the correlation structure of residuals in the linear model and allows simultaneously checking for possible outliers, non normality, and heteroscedasticity, and it does not rely on formal testing.

[Supplementary materials are available for this article. Go to the publisher's online edition of Communications in Statistics—Simulation and Computation® for the following three supplemental resource: a word file containing figures illustrating the mode of operation for the bisectional algorithm, QQ-plots, and a residual plot for the mussels data.]     

Minimum Variance Quadratic Unbiased Estimation for the Variance Components in Simple Linear Regression with Onefold Nested Error

The explicit forms of the minimum variance quadratic unbiased estimators (MIVQUEs) of the variance components are given for simple linear regression with onefold nested error. The resulting estimators are more efficient as the ratio of the initial variance components estimates increases and are asymptotically efficient as the ratio tends to infinity.