Data Analysis. In the meeting, the analysis that has led the College President to conclude that a reduction- in-force in the FSA at that College may be necessary will be shared. The analysis will include but is not limited to the following: ● Relationship of the FSA to the mission, vision, values, and strategic plan of the College and district ● External requirement for the services provided by the FSA such as accreditation or intergovernmental agreements ● Annual instructional load (as applicable) ● Percentage of annual instructional load taught by Residential Faculty (as applicable) ● Fall 45th-day FTSE inclusive of dual enrollment ● Number of Residential Faculty teaching/working in the FSA ● Number of Residential Faculty whose primary FSA is the FSA being analyzed ● Revenue trends over five years for the FSA including but not limited to tuition and fees ● Expenditure trends over five years for the FSA including but not limited to personnel and capital ● Account balances for any fees accounts within the FSA ● Cost/benefit analysis of reducing all non-Residential Faculty plus one Residential Faculty within the FSA ● An explanation of the problem that reducing the number of faculty in the FSA would solve ● The list of potential Residential Faculty that are at risk of layoff as determined by the Vice Chancellor of Human Resources ● Other relevant information, as requested
Data Analysis. Alabama Power will ensure appropriate data analysis techniques are used in the collection of the data required for this study.
Data Analysis. All data were transcribed verbatim in Bengali, translated into English, and deidentified prior to analysis. All transcripts were uploaded into MAXQDA2020 (VERBI Software, 2019). Data comprised of 44 IDI transcripts and 12 FGD transcripts. While transcripts were being reviewed during analysis, it was noted that some participants within a control cluster reported having participated in another intervention that was similar to FAARM, run by the NGO BRAC. Because these participants received nutrition education and home garden training, any interviews and focus groups they participated in were removed from the data set, resulting in the removal of two FGDs and two IDIs. This resulted in a total of 42 IDIs and 10 FGDs. Due to availability of translated transcripts at the time of analysis, 40 IDIs and 10 FGDs were analyzed. Transcripts were read in detail and memoed to note recurring themes, interesting statements, and to keep analytic notes on ideas as they emerged. Memos were additionally used to develop inductive codes. Developing codes involved an iterative process of reviewing the memos, data, and literature. Once a preliminary codebook was developed, codes were applied across the data set over multiple readings. On later readings, new codes were added to the codebook as they emerged. The codebook was refined and fully developed, including definitions for each code, inclusion and exclusion criteria, as well as sample quotes from the data that exemplified that code. On a final reading, codes were reviewed and revised, and codes which were developed later in the coding process were applied to earlier interviews and discussions. Once all data had been coded, a grounded theory approach (Xxxxxx, 2017) was used to analyze data to explain the process by which FAARM may have influenced women’s empowerment on nutrition. Codes comprised of a variety of themes, ranging from self-efficacy and purchasing power, to produce sharing with community members, to decision making power and mobility shaming. First, the coded segments of data were compared across variables to identify patterns and commonalities between participants. Data were compared across the following variables: intervention status; gender; years of marriage as a proxy of age; number of children; nuclear vs joined household including in-laws; whether or not they currently have a home garden; and whether or not their household owned livestock. Variations between variables presented in the coded segments were no...
Data Analysis. This includes a detailed discussion of the method of data evaluation, including appropriate statistical methods that will allow for the effects of the Demonstration to be isolated from other initiatives occurring in the State. The level of analysis may be at the beneficiary, provider, and program level, as appropriate, and shall include population stratifications, for further depth. Sensitivity analyses may be used when appropriate. Qualitative analysis methods may also be described, if applicable.
Data Analysis. Microsoft Excel and SPSS-11 were used to perform the statistical analysis and to assess numeric trends. Intraclass Correlation (ICC) was used to measure the level of agreement among physicians and nurses. There are two approaches to ICC: consistency and absolute agreement. The difference between consistency and absolute agreement measures how the systematic variability due to raters or measures is treated. If that variability is considered irrelevant, it is not included in the denominator of the estimated ICCs, and measures of consistency are produced. If systematic differences among levels of ratings are considered relevant, rater variability contributes to the denominators of the ICC estimates, and measures of absolute agreement are produced. In the current study, we used the consistency approach due to the fact that it is more suitable to Kappa statistic in our later analysis. K statistic was employed to measure the level of agreement among the physicians themselves and among the nurses themselves (quadratic weighting). The K statistic is based on a formula developed by Fleiss [13], which provides a numerical measure of agreement among multiple raters. Xxxxx’x Kappa coefficient was used to test levels of agreement between the two nurses in each unit, Xxxxx’x Kappa is more suitable than Fleiss13 K statistic to examine inter-observer agreement between two raters. The Kappa statistic measures the observed amount of agreement adjusted for the amount of agreement expected by chance alone. A value of −1.00 indicates complete disagreement, a value of 0 indicates that the agreement is no better than chance, and a value of +1.00 indicates a perfect agreement. In addition, Chi square analysis was performed in order to examine the differences between the two units in the staff members’ ratings.
Data Analysis. Beginning with the 2013-2014 school year, and annually thereafter, the District will maintain data regarding the participation of students, by race and ELL status, in higher level learning opportunities. The District will additionally re-conduct the surveys described in 1.c) and 1.d) above to gather information regarding the efficacy of strategies it has implemented. The District will review the data to identify whether there remains a statistically significant disparity in the participation of underrepresented group students when compared to peers not in the underrepresented groups, in higher level learning opportunities. The District will also consider, on an annual basis, whether the strategies and plan it has implemented have proven effective, or need to be altered. If alterations are required, the District will enact such alterations within one year of identifying the need for that change.
Data Analysis. The transcripts were analysed using framework of thematic content analysis with the assistance of a qualitative software package NVivo 11. The analysis was carried out in a step wise manner as mentioned below. Since the thematic content analysis method sought to address research questions in the qualitative research study to investigate perceptions and viewpoints of study participants (perception and conceptualisation about service user involvement), this method was deemed an appropriate method of analysis.
Data Analysis. Analysis of qualitative data Analysing the data is important in order to “get the sense” of the information gathered (Xxxxxxxx, 2013). Since qualitative data is often too rich, especially if audio or visual information is analyzed, it is crucial to filter out the unnecessary parts of the interview and group the remaining data into relevant themes (Xxxxxxxx, 2013). After all interviews had been recorded, I collected the audio files into a separate folder and imported the folder into MAXQDA, a software package for qualitative and mixed methods research. Since analyzing and coding data by hand is a difficult task, many researchers, including me, rely on software solutions when analyzing qualitative data (Xxxxxxxx, 2013). First, I transcribed all the audio files using the built-in transcribing feature of MAXQDA. This helped me not only to have a text version of the interview, but also to have every single paragraph timestamped, which allowed me to listen to specific parts of the interview by clicking on the sentences in the text. After all interviews had been transcribed, I started the coding, “the process of organizing the data by bracketing chunks and writing a word representing a category in the margins” (Xxxxxxx & Xxxxxx, 2012 as cited in Xxxxxxxx, 2013, p.247). Since my interviews were semi-constructed, the questions I asked revolved around certain topics. This allowed me to use selective coding approach when coding the text segments. After I have coded all the interviews, MAXQDA allowed me to organize the coded segments into themes and topics, and to export the data into printable format. Since all interviews were conducted in Kazakh and Russian, I coded the original interviews and translated only the coded segments. Quantitative data The quantitative data was collected using Google Forms. This tool allows researchers to create a dynamic spreadsheet in Google Sheets and the data in the spreadsheet automatically updates each time a new response is submitted. The problem with Google Sheets, however is that the information is recorded “as it is”, which means that every respondent’s answer is recorded not as a value, but as the answer choice provided in the survey. This creates an extra obstacle if a researcher wants to start analyzing the data in a software package immediately. This problem, however, can be easily solved by using the built-in “find and replace” feature within Google Sheets. After all responses have been collected, I renamed the columns in t...
Data Analysis. 842. The Parties acknowledge that the Consultant for the ACLU Agreement is preparing a report, in consultation with an independent statistical expert, which assesses data regarding investigatory stops completed by CPD officers for the period between 2018 and 2020 (“Report”). With respect to the disparate impact compliance methodology for this Report, the City has agreed that the Consultant may (1) assume that a prima facie showing under ICRA based on disparate impact on the basis of race has been satisfied, and (2) forego that analysis. The Parties recognize that the methodology for this Report includes, but is not limited to, an analysis of the following:
Data Analysis. The three curriculum representations of Xxx xxx Xxxxx (1998) were used to analyse the different data. We used Atlas.ti (Scientific Software Development GmbH, Berlin, Germany) for the analysis of the interviews. The derived analytic scheme was used by the first two authors to code all interviews during several rounds until full agreement was reached. The other data sources were analysed separately by the two first authors. To determine the aim of the intended curriculum, we reviewed the study guides and analysed the data from the interviews with the teacher educators and the heads of department. In the study guides, we scrutinised all texts to search for references to (the development of) community competence. We included all sentences referring to the acquisition of community competence in the mission/vision statement, the learning aims, the course descriptions, and the assessment procedure. From the interviews, we used those parts in which the interviewees described what they considered to be the ideal way to educate student teachers in community competence. A distinction was made between their views on the importance of community competence for the profession and their views on the role of teacher education institutes. The implemented curriculum was analysed on the basis of interviews with teacher educators, group observations, and the logs of the electronic learning environments used by groups. As mentioned before, we may expect teacher educators not only to recognise the importance of community competence, but we also expect them to stimulate community competence development by organising collaborative activities, including activities focusing on reflection on and assessment of community competence development. Therefore, during our analysis we searched within the interviews for teacher educators’ comments about the way they stimulate community competence, and categorised these statements into the three main categories: collaborative activities, reflection and assessment. The collaborative activities are configured within different group arrangements: mentor groups, subject matter groups, reflection groups, and research groups. The activities within these types of groups, together with reflection and assessment, have an important role in the curriculum. Student teachers present their reflections in electronic portfolios, which are used by the teacher educators as a basis for assessment. Comments about the electronic learning environment were also consi...
