¶ … data collection and analysis legitimize the goals and strategies educators create for change and improvement?
Given today's emphasis on standardized testing in the era of No Child Left Behind (NCLB), using data-driven analysis to legitimize various educational strategies is essential. "Daily life in districts and schools requires educators to effectively navigate a sea of data: diagnostic and norm-referenced standardized assessment data, reading assessment data, state and local assessment data, in combination with other data related to instructional programs and demographic, attendance, and dropout trends" (Ronka et al. 2008). Ideally, educators can use data such as student assessments to tailor the learning experience in a more effective fashion and incorporate formative assessments within the classroom to ensure that lesson plans are responsive and flexible to student needs. On a macro level, districts can use data tracking to see what types of teaching methods are effective or ineffective. Although teachers are always getting feedback in terms of student reactions, often this can be tainted by inevitable personal impressions and biases. Data, properly...
2008). The level of refinement of this data allowed teachers to more specifically zone in on what strategies were effective and which were not. "With the assistance of the data coach, school principals developed a dissemination plan that identified what data would be available and when, who would get the data, and how staff members might use it" (Ronka et al. 2008).
Teachers can be naturally resistant to being forced to change teaching strategies without evidence that the changes work and if they feel that the data used to support those changes is not representative or fair, they…
Over 250 respondents reported working 40 hours, with the next highest frequency being under 100. Number of Siblings The histogram for the number of siblings shows a negatively skewed data set, with more participants reporting fewer siblings. However, the range in this variable was quite high, ranging from 0 to 22 siblings. The mean response was 3.71 siblings, the median response was 3 siblings and the mode of the sample was
Self-reflection For a successful completion of any program, data analysis and results dissemination is a crucial part of the processes. Data analysis is the processes of project reporting that involves inspection, cleansing, transformation, and modeling of the data collected with the aim of establishing information that is useful in suggesting the possible conclusions and in providing insights to support the decisions made (Ott & Longnecker, 2015). Dissemination on the other hand
Overall, it appears that the relationships between these variables are somewhat similar between men and women, although there are slight differences, most keenly pointed out in the ANOVA results. Correlations Respondent's Sex Age of Respondent Highest Year of School Completed Total Family Income Job Satisfaction Male Age of Respondent Pearson Correlation 1 -.240** -.065 -.125** Sig. (2-tailed) .000 .103 .005 N Highest Year of School Completed Pearson Correlation -.240** 1 .419** -.042 Sig. (2-tailed) .000 .000 .350 N Total Family Income Pearson Correlation -.065 .419** 1 -.114* Sig. (2-tailed) .103 .000 .012 N Job Satisfaction Pearson Correlation -.125** -.042 -.114* 1 Sig. (2-tailed) .005 .350 .012 N Female Age of Respondent Pearson Correlation 1 -.275** -.115** -.123** Sig. (2-tailed) .000 .001 .002 N Highest Year of School Completed Pearson Correlation -.275** 1 .459** -.093* Sig. (2-tailed) .000 .000 .018 N Total Family Income Pearson Correlation -.115** .459** 1 -.196** Sig.
SPSS Data Analysis Does the number of average study hours per week during the semester accurately predict final exam grades? Independent variable: average number of study hours per week. Hours is continuous data because it can take on any value below 168 hours, which is the number of hours in a week. Even though the data is reported in integer form the 'hours' data is continuous. Hours data is quantitative, since it can be
Total numbers of students in Utica Junior High for seventh and eight grades, the number of students actually tested for proficiency levels, the state student population at each grade level and numbers tested, and for truly accurate analysis a host of other demographic information would need to be taken into account (Giddens, 2006). Given the information actually provided, it would not only be practically impossible but statistically meaningless to
Software Tools for Qualitative Research Data Analysis Software Packages Exploring Options for Research Software Tools As a quick reference tool, I created a table to compare primary features of three data analysis software packages: NVivo 10, Atlas.ti 7; and HyperRESEARCH (which also includes HyperTRANSCRIBE as a separate complementary option). This information is shown in Table 1, which is provided at the end of this work. After reviewing the software packages for data analysis, I