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 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 data which is the combination of all of the information warehouse data to create the central data mart. --------------------------------------------------------------------------------
Examples of Data Analysis in a sentence
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Such disclosure of confidential information to the Data Analysis Contractors shall be made on a confidential basis, and the recipients are bound by confidentiality obligations to Alberta Innovates.
Alberta Innovates is entitled to disclose any information about the Applicant and the Project to trusted third party service providers who are on contract to provide data analysis services to Alberta Innovates ("Data Analysis Contractors"), Including confidential information, and Including this Investment Agreement, the Application, all Reports, Required Reporting Metrics (if any), and any Survey responses.
More Definitions of Data Analysis
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 betweentreatments (low NO3−-N versus high NO3−-N) was performedusing the unpaired t-test (˛ = 0.05). Data analysis was generated using SigmaPlot software, Version 11.0 (Systat Software Inc., San Jose, CA, USA).
Data Analysis. Means and standard deviations were calculated for each of the 10 Likert-scale survey items for the 16 students in the accelerated program. The percentage of students who agreed with each item was calculated as the total percentage of students who rated the item with a 4 (slightly agree), 5 (agree), or 6 (strongly agree). A review of the responses to the 10 open ended survey questions revealed considerable redundancy and overlap among the answers. Therefore, the investigator pooled all of the responses to the ten questions and conducted a single content analysis, using an empiric-analytic inductive technique to categorize the data [25]. The content analysis technique included 1) creating a computer printout listing all participant responses for a given question, 2) segmenting each of the responses into individual thematic units, 3) reviewing all responses, 4) creating and naming categories or clusters of themes based on similarities among the data, and 5) sorting the thematic units into the categories.
Data Analysis. Means was applied to obtain the norm in describing the skill preferences as perceived by employers, the extent of skill acquisition as perceived by employees and the level of skill competence as perceived by both employers and employees for every skill category and skill area. Also, it was utilized to describe the employees’ task performance as perceived by both groups of respondents. Furthermore, Pearson product moment coefficient of correlation (r) was computed to determine the correlation of all employability skills categories and areas in two factors namely (a) skill acquisition and (b) skill competence of employees to task performance. For the purpose of interpreting the strength of relationship between factors of employability skills and task performance, Lodico, Spaulding and Voegtle’s (2006, p. 233) categorization of the size of r was used as follows: 0-0.19 (Weak Relationship), 0.20-0.34 (Slight Relationship), 0.35-0.64 (Moderately Strong Relationship), 0.65-0.84 (Strong Relationship), and 0.85 or greater (Very Strong Relationship). All statistical tests were set at .05 level of significance.
Data Analysis. Means and standard deviations of continuous variables were calculated. The accuracy of body composition measures (BMI, BFP, FMI, and FFMI) to discriminate injured from non-injured participants was evaluated using Receiver Operating Characteristic (ROC) curve analysis. The ROC curve is a plot of the sensitivity (proportion of positives that are correctly identified as such) versus specificity (proportion of negatives that are correctly identified as such) at various cut-off points. A comparison of the area under the curves (AUC) among composition measures was also used to assess their overall performance as prognostic tools of musculoskeletal injuries. The cut-off point for each composition measure was defined as the co-ordinate that had the closest value to 1 for the difference between sensitivity and specificity values. P was based on two-tailed tests and P<0.05 wasconsidered significant. All statistical analyses were conducted using MedCalc software, version 12.4.0, (MedCalc, Ostend, Belgium).
Data Analysis. Means were used for better understanding of the effects of intervention. Means for URICA stage of change outcomes , BMI outcome, and program satisfaction were computed and pre-intervention and post-intervention means were compared. Qualitative data was then reviewed for themes, suggestions, and participant feelings of overall program success.
Data Analysis. Means and standard deviations on the intelligence quotients and subscales were calculated for the three groups, shown in Table 2. Generalized linear mixed models for the three training candidate groups on the intelligence quotients and subscales were run. Generalized linear mixed models were chosen for these analyses to account for the unequal sample sizes and unequal variances among the three training candidate groups.Bonferroni post hoc t-tests with an adjustment for multiple comparisons were run to identify differences among groups. A statistical significance level of p ≤ 0.05 was established a priori for the post hoc t-tests. A two-tailed t-test was not considered clinically significant (i.e., two groups were not considered meaningfully different) unless the comparison was statistically significant at p ≤ 0.05. Small (0.20 – 0.40), moderate (0.41 – 0.79), and large (0.80 and above) Hedges’ g effect sizes were identified. Other comparisons with p < 0.15 were noted to take into account differences between groups that may be underrepresented because of the small sample size for Group 2 (RPA training candidates who completed UPT; n = 36). Analyses were run toidentify the minimum sample size required for each of these comparisons to meet the p ≤ 0.05 a priori requirements. Table 2. Pilot Training Candidate Group Means and Standard Deviations for MAB-II Quotients and SubscalesGroup 1(n = 411)Mean (SD)Group 2 UPT (n = 36)Mean (SD)Group 3(n = 7,248)Mean (SD)Note: General population mean (SD) is 100 (15) for FSIQ, VIQ, and PIQ and 50 (10) for subscales. Group 1: non-rated RPA training candidates, Group 2: RPA training candidates who completed UPT, and Group 3: manned airframe training candidates.
Data Analysis. Means and standard deviations were calculated using measured data on clay content, soil pH and CEC. Analysis of variance was performed on measured values of pH, CEC and clay content of the soil profiles to determine whether the application of different volumes of treated sewage on the soil parameters was significant at 95% confidence level. Correlation analysis was used to test the strength of association of soil pH versus depth, CEC versus depthcriticaland clay content versus depth. The significance of the association was determined by comparing r2 values with the Pearson r2 value of 0.87 (p≤0.05). For statistical analysis and development of dose-response relationships, measured data on bio- available metal concentrations and concentration of metals in grasses was first tested for normality and then transformed to log10 values. To assess Pb and Cd accumulation in the soil profile, correlation analysis was used to relate soil depth and log10 (metal concentration). Analysis of variance was used to test the significance of treatment on (1) bio-available Pb and Cd and on (2) levels of the metals in grass. To develop the best-fit models for uptake of Pb and Cd under field conditions two approaches were used to analyse the data obtained. In the first approach, the data for each of the 3 sample sets of bio-available metal levels and grass metal levels was used to draw up a model for each harvest and test its strength. This was done to assess whether any of the models of individual harvests could be representative of multiple harvests. In the second approach, dose-response models of average bio-available soil levels and average levels of the metals star grass throughout the life of the experiment were drawn up, to assess whether they could represent multiple harvesting of grass. The assumption was that in the field, a grass crop is planted and animals continue to feed on the crop until the old crop is removed and a new crop is planted. Therefore the regular grazing of animals could be regarded as being synonymous with regular harvesting of the grass crop. To develop models representative of this situation, it was decided to analyse each harvest and each soil-sampling event as a replicate of the average situation that prevails under field conditions over a long time. The best-fit models were tested for strength by comparing the computed correlation coefficients and the critical t values from the t-test for comparison of regression coefficients.