9 resultados para Day-of-the-Week Effect

em Digital Commons at Florida International University


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This study investigated time-use of elementary music teachers and elementary classroom teachers to determine: (1) whether there was a relationship between grade level, time of day, and day of the week and teachers' time-use in teaching, monitoring, and non-curricular, and (2) whether ethnicity, training, and years of experience affect teacher time-use. Sixty-nine music teachers and 55 classroom teachers participated. ^ A MANOVA was used to examine the hypothesized relationship. ANOVA results were significant for time spent teaching, monitoring, and non-curricular. An independent t test revealed a significance difference (t (302) = 5.20, p < .001) between the two groups of teachers. A significant difference was found for teaching, t (302) = 5.20, p < .001: music teachers spent more time actively teaching than did classroom teachers. There was a significant difference for monitoring (t (302) = 13.62, p < .001): classroom teachers allocated more time to monitoring than did music teachers. A significant difference was also found for non-curricular (t (302) = 7.03, p < .001): music teachers spent more time in this category of activities than did classroom teachers. ^ Analyses of the activities subsumed under the major categories indicated significant differences between elementary music teachers and elementary classroom teachers, overall, in subject matter (p < .001), discussion (p < .05), school-wide activities (p < .001), seatwork (p < .001), giving directions (p < .001), changing activities (p < .001), lunch (p < .05), planning (p < .001) and interruption (p < .001). Analyses of the relationship and ethnicity, training, degree, experience indicated significant difference for main effect, ethnicity (F(2, 116) = 4.22, p < .017). Time-use for black non-Hispanic teachers was higher than time-use for those who were Hispanic and white non-Hispanic. ^ Analyses of time-use by grade showed no increase for either group as grade level increased. A statistically significant Wilks Lambda ( F (1,294) = .917 p < .013) was found for the independent variable day of the week. ANOVA indicated that elementary classroom teachers monitored more on Thursdays and Fridays: music teachers allocated more time to non-curricular activities on Fridays. ^

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This study investigated time-use of elementary music teachers and elementary classroom teachers to determine: (1) whether there was a relationship between grade level, time of day, and day of the week and teachers' time-use in teaching, monitoring, and non-curricular, and (2) whether ethnicity, training, and years of experience affect teacher time-use. Sixty-nine music teachers and 55 classroom teachers participated. A MANOVA was used to examine the hypothesized relationship. ANOVA results were significant for time spent teaching, monitoring, and non-curricular. An independent t test revealed a significance difference (t (302) = 5.20, p Analyses of the activities subsumed under the major categories indicated significant differences between elementary music teachers and elementary classroom teachers, overall, in subject matter ( p teachers was higher than time-use for those who were Hispanic and white non-Hispanic. Analyses of time-use by grade showed no increase for either group as grade level increased. A statistically significant Wilks Lambda ( F (1,294) = .917 p < .013 ) was found for the independent variable day of the week. ANOVA indicated that elementary classroom teachers monitored more on Thursdays and Fridays: music teachers allocated more time to non-curricular activities on Fridays.

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This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as “histogram binning” inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. ^ Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. ^ The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. ^ These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. ^ In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation. ^

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This dissertation develops a new mathematical approach that overcomes the effect of a data processing phenomenon known as "histogram binning" inherent to flow cytometry data. A real-time procedure is introduced to prove the effectiveness and fast implementation of such an approach on real-world data. The histogram binning effect is a dilemma posed by two seemingly antagonistic developments: (1) flow cytometry data in its histogram form is extended in its dynamic range to improve its analysis and interpretation, and (2) the inevitable dynamic range extension introduces an unwelcome side effect, the binning effect, which skews the statistics of the data, undermining as a consequence the accuracy of the analysis and the eventual interpretation of the data. Researchers in the field contended with such a dilemma for many years, resorting either to hardware approaches that are rather costly with inherent calibration and noise effects; or have developed software techniques based on filtering the binning effect but without successfully preserving the statistical content of the original data. The mathematical approach introduced in this dissertation is so appealing that a patent application has been filed. The contribution of this dissertation is an incremental scientific innovation based on a mathematical framework that will allow researchers in the field of flow cytometry to improve the interpretation of data knowing that its statistical meaning has been faithfully preserved for its optimized analysis. Furthermore, with the same mathematical foundation, proof of the origin of such an inherent artifact is provided. These results are unique in that new mathematical derivations are established to define and solve the critical problem of the binning effect faced at the experimental assessment level, providing a data platform that preserves its statistical content. In addition, a novel method for accumulating the log-transformed data was developed. This new method uses the properties of the transformation of statistical distributions to accumulate the output histogram in a non-integer and multi-channel fashion. Although the mathematics of this new mapping technique seem intricate, the concise nature of the derivations allow for an implementation procedure that lends itself to a real-time implementation using lookup tables, a task that is also introduced in this dissertation.

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This study examined the effects of computer assisted instruction (CAI) 1 hour per week for 18 weeks on changes in computational scores and attitudes of developmental mathematics students at schools with predominantly Black enrollment. Comparisons were made between students using CAI with differing software--PLATO, CSR or both together--and students using traditional instruction (TI) only.^ This study was conducted in the Dade County Public School System from February through June 1991, at two senior high schools. The dependent variables, the State Student Assessment Test (SSAT), and the School Subjects Attitude Scales (SSAS), measured students' computational scores and attitudes toward mathematics in 3 categories: interest, usefulness, and difficulty, respectively.^ Univariate analyses of variance were performed on the least squares mean differences from pretest to posttest for testing main effects and interactions. A t-test measured significant main effects and interactions. Results were interpreted at the.01 level of significance.^ Null hypotheses 1, 2, and 3 compared versions of CAI with the control group, for changes in mathematical computation scores measured with the SSAT. It could not be concluded that changes in standardized mathematics test scores of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were significantly higher than changes in test scores for students receiving TI only.^ Null hypotheses 4, 5, and 6 tested the effects of CAI for attitudes toward mathematics for experimental groups against control groups measured with the SSAS. Changes in attitudes toward mathematics of students using CAI with differing software 1 hour per week for 18 class hours combined with TI were not significantly higher than attitude changes for students receiving TI only.^ Teacher effect on students' computational scores was a more influential variable than CAI. No interaction was found between gender and learning method on standardized mathematics test scores (null hypothesis 7). ^

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Significant improvements have been made in estimating gross primary production (GPP), ecosystem respiration (R), and net ecosystem production (NEP) from diel, “free-water” changes in dissolved oxygen (DO). Here we evaluate some of the assumptions and uncertainties that are still embedded in the technique and provide guidelines on how to estimate reliable metabolic rates from high-frequency sonde data. True whole-system estimates are often not obtained because measurements reflect an unknown zone of influence which varies over space and time. A minimum logging frequency of 30 min was sufficient to capture metabolism at the daily time scale. Higher sampling frequencies capture additional pattern in the DO data, primarily related to physical mixing. Causes behind the often large daily variability are discussed and evaluated for an oligotrophic and a eutrophic lake. Despite a 3-fold higher day-to-day variability in absolute GPP rates in the eutrophic lake, both lakes required at least 3 sonde days per week for GPP estimates to be within 20% of the weekly average. A sensitivity analysis evaluated uncertainties associated with DO measurements, piston velocity (k), and the assumption that daytime R equals nighttime R. In low productivity lakes, uncertainty in DO measurements and piston velocity strongly impacts R but has no effect on GPP or NEP. Lack of accounting for higher R during the day underestimates R and GPP but has no effect on NEP. We finally provide suggestions for future research to improve the technique.

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After developing field sampling protocols and making a series of consultations with investigators involved in research in CSSS habitat, we determined that vegetationhydrology interactions within this landscape are best sampled at a combination of scales. At the finer scale, we decided to sample at 100 m intervals along transects that cross the range of habitats present, and at the coarser scale, to conduct an extensive survey of vegetation at sites of known sparrow density dispersed throughout the range of the CSSS. We initiated sampling in the first week of January 2003 and continued it through the last week of May. During this period, we established 6 transects, one in each CSSS subpopulation, completed topographic survey along the Transects A, C, D, and F, and sampled herb and shrub stratum vegetation, soil depth and periphyton along Transects A, and at 179 census points. We also conducted topographic surveys and completed vegetation and soil depth sampling along two of five transects used by ENP researchers for monitoring long-term vegetation change in Taylor Slough. We analyzed the data by summarizing the compositional and structural measures and by using cluster analysis, ordination, weighted averaging regression, and weighted averaging calibration. The mean elevation of transects decreased from north to south, and Transect F had greater variation than other transects. We identified eight vegetation assemblages that can be grouped into two broad categories, ‘wet prairie’ and ‘marsh’. In the 2003 survey, wet prairies were most dominant in the northeastern sub-populations, and had shorter inferred-hydroperiod, higher species richness and shallower soils than marshes, which were common in Subpopulations A, D, and the southernmost regions of Sub-population B. Most of the sites at which birds were observed during 2001 or 2002 had an inferred-hydroperiod of 120-150 days, while no birds were observed at sites with an inferred-hydroperiod less than 120 days or more than 300 days. Management-induced water level changes in Taylor Slought during the 1980’s and 1990’s appeared to elicit parallel changes in vegetation. The results described in detail in the following pages serve as a basis for evaluating and modifying, if necessary, the sampling design and analytical techniques to be used in the next three years of the project.

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Following on our previous year’s work on ‘Effect of hydrologic restoration on the habitat of the Cape Sable seaside sparrow (CSSS)’, we presented first year results at the Cape Sable seaside sparrow – fire planning workshop at Everglades National Park in December 2003. Later, with almost the same set of crews as in the previous year, we started field work in the first week of January and continued till May 26, 2004. Protocols for sampling topography and vegetation in 2004 were identical to the previous year. In the early season, we completed topographic surveys along two remaining transects, B and E (~16.5 km), and vegetation surveys along three transects, D, E and F (~10.8 km), leaving only the vegetation sampling on transects B and C to be completed in 2005. During April and May, vegetation sampling was completed at 230 census sites, making the total of 409 CSSS census sites for which we have complete vegetation data. We updated data sets from both 2003 and 2004, and analyzed them together using cluster analysis, ordination, weighted-averaging regression and analysis of variance, as we had in 2003. Additionally, we used logistic regression to examine the effect of vegetation structural parameters on the recent occurrence of CSSS. We also analyzed vegetation observations recorded by the sparrow census team in 1981 and annually between 1992 and 2004 to assess historical patterns of vegetation change in CSSS habitat.

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The major activities in Year 3 on ‘Effect of hydrologic restoration on the habitat of the Cape Sable seaside sparrow (CSSS)’ included presentations, field work, data analysis, and report preparation. During this period, we made 4 presentations, two at the CSSS – fire planning workshops at Everglades National Park (ENP), one at the Society of Wetland Scientists’ meeting in Charleston, SC, and a fourth at the Marl Prairie/CSSS performance measure workshop at ENP. We started field work in the third week of January and continued till June 3, 2005. Early in the field season, we completed vegetation surveys along two transects, B and C (~15.1 km). During April and May, vegetation sampling was completed at 199 census sites, bringing to 608 the total number of CSSS census sites with quantitative vegetation data. We updated data sets from all three years, 2003-05, and analyzed them using cluster analysis and ordination as in previous two years. However, instead of weighted averaging, we used weighted-averaging partial least square regression (WA-PLS) model, as this method is considered an improvement over WA for inferring values of environmental variables from biological species composition. We also validated the predictive power of the WA-PLS regression model by applying it to a sub-set of 100 census sites for which hydroperiods were “known” from two sources, i.e., from elevations calculated from concurrent water depth measurements onsite and at nearby water level recorders, and from USGS digital elevation data. Additionally, we collected biomass samples at 88 census sites, and determined live and dead aboveground plant biomass. Using vegetation structure and biomass data from those sites, we developed a regression model that we used to predict aboveground biomass at all transects and census sites. Finally, biomass data was analyzed in relation to hydroperiod and fire frequency.