BALANCED MESSAGING FOR EXPERIENCED LAPTOP CONSUMERS

This research aims to obtain empirical evidence about the impact of “balanced messaging” (a marketing communicationpractice where both strengths and weaknesses of a product are communicated to consumers) in marketing communication on consumers’ brand loyalty. With three laptop brands being the subjects and university students in Indonesia being the sample, surveys with an experimental design were conducted. Participants (n = 90) were randomly grouped into those being exposed to product’s strengths only (Group A), product’s weaknesses (Group B), and product’s strengths and weaknesses (Group C) – with 30 participants in each group. Using ANOVA, the results indicate that balanced messaging can be as constructive as imbalanced messaging, which focuses merely on strengths in the context of brand loyalty. With several limitations, this preliminary study is expected to benefit marketers to create fairer marketing communication.

Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

Time Series Methods and Repeated Sample Surveys

<p>When applied to a sequence of repeated surveys, the traditional sample survey estimators of means or totals for one time period only, fail to take advantage of any time series structure. Such structure may result from correlation between successive responses for resampled individuals, or from time series properties in the parameters of interest. Historically, the initial published papers on time series improvement of repeated sample survey estimates allowed only the first possibility, treating the sum over the population of the individual responses as fixed; individual responses were seen as having stochastic properties only with respect to the sampling scheme. The alternative and later development allowed that both individual responses and their sum have stochastic properties with respect to a superpopulation from which the population of individual responses are drawn. Superpopulations allowed the application of mainstream time series techniques, including signal extraction and stochastic least squares, to repeated sample survey data. These developments in their historical perspective are the topic of Chapter 1. Superpopulation models may also be applied to sample surveys from a single time period, and superpopulation and design properties of the one period linear non-homogeneous sample survey estimator form the topic of Chapter 2; this estimator is sufficiently general to subsume almost all single period non-informative sample survey estimators, and Chapter 2 allows systematisation of a wide range of previously disparate results. This linear estimator may also be extended beyond one time period to include the known estimators for repeated surveys, and this topic, together with a consideration of the effects of data agqregation on non-stochastic and stochastic least squares, is the subject of Chapter 3. Given the central role of the general linear model, and the time series nature of repeated surveys, projection and parameter updating formulae for linear models should form an integral part of repeated survey analysis. The correlation of sample survey errors however, invalidates the formulae appropriate to the known iid error case, and Chapters 4 and 5 develop the general formulae to allow correlated error structure. Chapter 4 considers parameter vectors of fixed length, as for example, for polynomial models, and provides formulae for estimating the length of the parameter vector, and for calculating independent recursive residuals and cusums when further data are added to the model. Chapter 5 considers updating and projection formulae in a wider context, and allows that the parameter vector may be stochastic or non-stochastic and that its length may increase with additional data; it consequently provides a general extension of the Kalman filter to the case of coloured noise over time. The paucity of suitable data has limited data analysis to that contained in Chapter 6, where a simulation study and an analysis of medical data gauge the efficacy of polynomial models in time with multiple observations per time point and autocorrelated errors. The formulae of Chapter 4 allow testing for the constancy of the regression relationships over time. The appendix details SAS computer programs for fitting the polynomial models of Chapter 6.</p>

First Record of Sericomyrmex mayri for Paraguay and Increasing the Range of Distribution of 17 Ant Species in the Central Department

Ants have been studied in Paraguay, South America, over the last two centuries, nevertheless new species can still be discovered with simple sample surveys. Most species collected in the country belong to one or few locations, therefore knowledge about species distribution is limited. A total of 2,040 ants have been collected, belonging to 7 subfamilies and representing 44 species. All of these species, except Sericomyrmex mayri, were documented for Paraguay and 17 species were first documented in the Central Department. Those 17 species are: Camponotus sanctaefidei, Crematogaster acuta, C. arata, Cyphomyrmex laevigatus, C. lectus, C. minutus, Forelius pusillus, Linepithema neotropicum, L. pulex, Mycetomoellerius fiebrigi, Nylanderia docilis, Pheidole cyrtostela, Pogonomyrmex tenuipubens, Solenopsis megergates, S.richteri, Strumigenys hindenburgi, and Wasmannia lutzi. These species belong to 12 genera and 3 subfamilies. The new recorded species is described and illustrated with photographs of the collected specimens as well as a short description of taxonomy, ecology, and distribution. A list of the new species to the Central Department is also provided. The aim of this study is to increase the knowledge of ant species in Paraguay and their distribution.

How to make estimates with compensation for nonresponse in statistical analysis of census data

The paper deals with the problem of solving the nonresponse problem in a realized census. The purpose is to modify the method of poststratification using weights to compensate for nonresponse, which is known in sample surveys, for its application in censuses. The suggested approach offers more accurate estimates because of compensation for nonresponse and the possibility to formulate broader conclusions based on the census data. The approach is advised in all surveys in which the costs of realization the survey by the census, are practically the same as for sample survey and the list of all units of the population is available.