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 means data which is the combination of all of the information warehouse data to create the central data mart. --------------------------------------------------------------------------------
Data Analysis. Means and standard errors were determined for rainbow trout fillet analysis results while means and standard deviations were determined for water sample analysis results. Comparison of the grouped RAS mean geosmin concentrations in trout flesh between treatments (low NO3−-N versus high NO3−-N) was performed using the unpaired t-test (˛ = 0.05). Data analysis was generated using SigmaPlot software, Version 11.0 (Systat Software Inc., San Jose, CA, USA).
Examples of Data Analysis in a sentence
Signed by: NAME: AFFILIATION: EMAIL: AUTHOR CONTRIBUTION: Study Concept or Design Data Collection Data Analysis or Interpretation Writing the Paper Others ORCID: We agree to the terms as set out in the Agreement.
As described in sections 1111(b)(1), 1114 (b)(1)(A) and 1309(2) of the Elementary and Secondary Education Act (ESEA), the Comprehensive Needs Assessment (CNA) requirement is met by completing a School Data Analysis (SDA) and School Process Profile (SPP).
For R, the basic reference is The New S Language: A Programming Environment for Data Analysis and Graphics by Richard A.
Data Narrative for SchoolDescription of School Setting and Process for Data Analysis: Provide a very brief description of the school to set the context for readers (e.g., demographics).
Two worksheets (#1 Progress Monitoring of Prior Year’s Performance Targets and #2 Data Analysis) have been provided to organize the data referenced in the narrative.
More Definitions of Data Analysis
Data Analysis means analytical actions of the Client, including those performed at the request of its clients (on their behalf), in respect of data, aimed at generating various commercially valuable information, within the limits allowed by the Permitted purpose.
Data Analysis. Means, standard deviations and zero order correlations for all variables in the analysis are shown in Table 1 on page 49. The sample is composed of 337 undergraduate students enrolled in a variety of sociology classes. Forty percent of the sample were males (N=134) and 59% were females (N=198). The mean age of the sample was 20, and the sample was fairly evenly distributed among academic levels; 27% were seniors (N=91), followed by 25% juniors (N=84), 25% sophomores (N=83) and 20% freshman (N=66).Eighty-five percent of the respondents are single (N=287). Twenty-five percent of the respondents had a lifetime total of 2-3 sex partners (N=85), 19% had 4-6 sex partners (N=63), 16% had one sex partner (N=54), 14% never had a sex partner (N=48) and 14% had 10+ sex partners (N=46). There were no reported gays or lesbians and there were three reported bisexuals; all others reported being heterosexual. Sixty-six percent of the respondents reported never attending an AIDS meeting (N=224). Only 19% reported attending one AIDS meeting (N=65). The dependent variable of concern is condom use. Slightly more than 58% of the sample reported "definitely true" and "probably true" when asked if they always use condom during sexual activity. The full breakdown of the dependent variable is as follows: when asked if they always used a condom during sexual activity, 35% of the respondents answered "probably true" (N=119), 23% responded "definitely true" (N=79), 14% responded "probably false" (N=46) and 13% responded "definitely false" (N=44).The dependent variable "I always use condoms during sexual activity" can be found on page 54, number sixteen. The remaining analysis excludes all cases with incomplete data and consists of the 215 respondents with complete data. Scale ConstructionAll variables used to construct the scales had Likert-type response choices ranging from "definitely true" to "definitely false" with values of 1-5 respectively. The Self- Efficacy and HBM variables also contained a "Not Applicable (6)" response option. Scales were created to measure knowledge, self-efficacy and elements of the Health Belief Model.
Data Analysis. Shine will refine program-wide monthly Manage By Information Reports (MBI) & quarterly Manage By Outcomes Reports (MBO). The MBO will be used during Data Days where staff will be trained to analyze their classroom data to drive infonned decision making at the classroom level. Shine will work in partnership with the New Haven Public Schools' Head Start Director and designees to facilitate Data-Driven Leadership Meetings (investing in culture of data). • Strengthening the local Partnership for Compliance and Accountability: Shine will provide onboarding training for the new Superintendent and the site Principal which may include: • Head Start Requirements and Expectations • Head Start and Shine Terminology (MBI/PIR, etc.) • Key Reports for monitoring and compliance • Understanding HS staff responsibilities in accordance to the Head Start Program Performance Standards and HS Act • Develop system of reporting incidents and licensing concerns • Develop an Action Plan for resolving and strengthening systems (Incident reporting, Incident Filing/tracking, and Incident Analysis}
Data Analysis. Means, standard-deviations and frequencies were computed for all quantitative questions. For open questions, qualita- tive data were analyzed using a method of thematic content analysis developed by Miles and Huberman.[13]
Data Analysis. Means of the triplicates were calculated for the pH-stat test. For phases 2 and 3, homoscedasticity and normal distribution of the data was checked by the Hartley and Shapiro–Wilks tests. Once these assumptions were satisfied, one-way ANOVA and Tukey tests were carried out for comparisons amongst groups. The software SigmaPlot 11.0 (Systat Software Inc., Chicago Illinois, USA) was used for the calculations, with significance level of 5%. demineralization model. After the first 30 min of acid expo- sure, the surface loss of all groups was very low and below the detection limit of the method, with exception of the group XG. This same finding was found for the groups C+, CLP + LPP and
Data Analysis. Means and standard deviations on the domains and facet standardized T-scores were run for the three groups (see Table 1). Generalized linear mixed models were performed for the three groups of pilot training candidates on the 5 domains and 30 facets of the NEO-PI-R. Generalized linear mixed models were chosen for these analyses to account for the unequal sample sizes and unequal variances among the three groups.Bonferroni post hoc t-tests with an adjustment for multiple comparisons were conducted to identify between-group differences. A statistical significance level of p < .10 was established a priori for the post hoc t-tests. A two-tailed t-test was not considered meaningful unless (a) the comparison was statistically significant at p < .10, (b) Hedges’ g effect size was |0.38| or greater, and (c) power was 0.80 or greater. However, comparisons that were significant with a Hedges’ g effect size of |0.38| or greater, but with a power less than 0.80, were also identified. These comparisons were noted to take into account between-group differences that may be underrepresented because of the small sample size for Group 2 (n = 27).
Data Analysis. Means, graphs and standard error bars for teacher distribution and shortage were worked out using Microsoft excel. Analysis of variance, level of significance and standard error deviations (SED) were computed using SPSS 17.5 version.