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This report summarizes the attitudinal portion of a nation-wide survey on the computerization of Canadian universities. Six different questionnaires, each of which contained the same questions on attitudes, were mailed to faculty, deans, admissions, officers, registrars, computer center directors, and other administrators at 63 Canadian universities. The overall return rate for the survey was 28.6%, thus restricting the conclusions that can be drawn from the results. Questions on the 30-item survey were grouped according to instruction, administration, equity, and social impact. Independent variables that were explored included the size of the university, the level of education, and the age of the respondent. Results of the survey showed that, within the university, there were few significant differences in attitudes between variable groups. Overall, there was a positive feeling about computers within the university which cut across all groups. There were differences, however, in the level of positive attitudes among the subjects, with the strongest differences in attitudes occurring between the faculty and the administrative staff. There was a strong tendency for senior administrators to be the most enthusiastic of all, while the faculty were more circumspect about the changes to teaching that the computer will supposedly bring. Data are presented in five tables and four figures as well as a narrative report. The appendices contain a copy of the attitude survey questions, and a table showing the breakdown of the attitude survey data by question. (DB)

As an extension of B. Thompson's 1998 invited address to the American Educational Research Association, this paper cites two additional common faux pas in research methodology and explores some research issues for the future. These two errors in methodology are the use of univariate analyses in the presence of multiple outcome variables (with the converse use of univariate analyses in post hoc explorations of detected multivariate effects) and the conversion of intervally scaled predictor variables into nominally scaled data in the service of the "of variance" (OVA) analyses. Among the research issues to receive further attention in the future is the appropriate use of statistical significance tests. The use of the descriptive bootstrap and the various types of effect size from which the researcher should select when characterizing quantitative results are also discussed. The paper concludes with an exploration of the conditions necessary and sufficient for the realization of improved practices in educational research. Three appendixes contain the Statistical Package for the Social Sciences for Windows syntax used to analyze data for three tables. (Contains 16 tables, 15 figures, and 173 references.) (SLD)

Most researchers using analysis of variance (ANOVA) use a fixed-effects model. However, a random- or mixed-effects model may be a more appropriate fit for many research designs. One benefit of the random- and mixed-effects models is that they yield more generalizable results. This paper focuses on the similarities and differences between the various ANOVA models and the factors that should be considered when determining which model to use. An explanation of the "Rules of Thumb" for deriving the correct formulas for computing "F" statistics is also given. (Contains 2 tables and 15 references.) (SLD)

Using a quasi-experimental analysis of variance (ANOVA) design, this project examined the effects of the use of accommodations with students of limited English proficiency (LEP) and non-LEP students and whether the use of accommodations affected the validity of test score interpretations. Major accommodations examined were extra time, and extra time with extended oral presentation. Samples of fourth (n=292) and seventh grade students (n=159) were tested using the Terranova multiple assessment mathematics test, as well as a math skills test and the LAS reading comprehension test. Descriptive findings showed that LEP students scored lower than non-LEP students on mathematics tests and teacher-reported skill levels. Major predictors of mathematics achievement were LAS reading proficiency level (a proxy of LEP status), whether students received an accommodation, and teacher rating of reading skill. ANOVA analyses were conducted to compare the mean scores of students in accommodated tests versus those with no accommodation. These showed the accommodation effect was significant, with those students in the extra time condition showing the highest scores. A discriminant analysis showed that the best predictors of membership in different English fluency groups were Spanish fluency, time in the United States, reading grade, and mathematics test score. LEP students were more likely than non-LEP students to be misclassified into a fluency group. Examination of students' writing samples showed clear differences in mathematics and language achievement, depending on the student's language proficiency level. The project's results provide information on whether accommodations provide valid inferences for special needs children as well as non-LEP children. (Contains 1 figure, 16 tables, and 25 references.) (SLD)

Notes that the availability of statistical software packages has led to a sharp increase in use of complex research designs and complex statistical analyses in communication research. Reports a series of Monte Carlo simulations which demonstrate that this complexity may come at a heavier cost than many communication researchers realize. Warns communication researchers about problems associated with design and statistical complexity. Suggests solutions. (SG)

Students in an introductory statistics course are often preoccupied with learning the computational routines of specific summary statistics and thereby fail to develop an understanding of the meaning of those statistics or their conceptual basis. To help students develop a better understanding of the meaning of three frequently used statistics, this document presents HyperCard programs and a Lotus 1-2-3 spreadsheet for use by teachers in classroom simulation demonstrations of Pearson's correlation coefficients, t-test of two independent population means, and one-way analysis of variance. The instructional contents and typical computer screen outputs for each demonstration are discussed in association with their respective HyperCard programs, which are included in the appendix. (JJK)

Non-PR, IFPRI1; MP-17, FCND, This paper examines attrition in the KwaZulu-Natal Income Dynamics Study (1993–1998) and assesses the extent of attrition bias for a specific empirical example. The analysis shows that 1993 first round nonresponse is largely unrelated to observable characteristics of the communities other than indicators of migration activity. Multivariate regressions are then used to describe the characteristics of the households attriting in 1998, revealing the importance of distinguishing between two types of attriting households, those that moved and those that apparently moved but left no trace. For example, increased household size reduced the probability of either type of attrition, whereas measures of higher quality of fieldwork in the 1993 survey only reduced the probability that a household left no trace. While observable differences between attritors and non-attritors indicate attrition is nonrandom, it does not necessarily follow that estimated relationships based on the non-attriting sample suffer from attrition bias. To more directly explore attrition bias, which is by its nature model specific, this analysis estimates household-level expenditure functions correcting for attrition bias using standard Heckman selection procedures and a quality of 1993 interview variablesas identifying instruments. There is positive selection, and although many of the other parameter estimates are quite similar, a Hausman test rejects the equality of coefficients between the corrected and uncorrected models. Therefore, this study concludes, at least for this simple case, that attrition does appear to bias the “behavioral” coefficients.