Data Analysis Methods Clause Samples
The Data Analysis Methods clause defines the specific techniques, tools, and procedures that will be used to process and interpret data within the scope of an agreement or project. It typically outlines the types of analyses to be performed, such as statistical modeling, qualitative coding, or data visualization, and may specify standards or software to be used. By clearly establishing how data will be analyzed, this clause ensures consistency, transparency, and reliability in the results, reducing misunderstandings and disputes over methodology.
Data Analysis Methods. A significant number of the values contained in the East Boulder Mine water quality dataset are reported as less than analytical detection limits. The following methods are used to assign a numeric value to a particular data point to allow for inclusion in statistical analysis and/or comparison to the AMP TTLF. These methods make it possible to describe a parameter as having an overall mean value reported less than the detection limit. The presence of data represented by values less than detection limits require determination of a number to be used in place of a “less than” value. For statistical purposes data reported as less than detection limits will be assigned a numeric value using a set of rules. If half or fewer data points in a set for a particular parameter are less than the detection limit, then the full detection limit will be used for statistical values. If more than half of the data points in a set for a particular parameter are less than the detection limit, then half the detection limit will be used for statistical values. For multiple detection limits, if detection limits differ by more than 10 times, then only the lower detection limit data will be used and higher detection limit data will be discarded. If detection limits are less than or equal to 10 times the lowest limit, then both sets of detection limit data will be retained. If this method is applied in the GNA water quality database or AMP monthly reports, the data will be emphasized with italics.
Data Analysis Methods. Contractor must describe the analytical methods to be used to test for an association between respondents’ estimated exposure and health outcomes, including but not limited to the long- and short-term health and quality of life outcomes measured in the survey. Multivariable analyses must include relevant covariates, including but not limited to demographics, socioeconomic factors, length of residence in the area, behavioral risk factors, and COVID-19 related factors. All outcomes must be stratified by race/ethnicity and socioeconomic status when applicable. Analyses also must examine issues of environmental justice, including but not limited to identifying disproportionate impacts by race and/or socioeconomic status. Interim and final reports on the methods, results, and implications of the analysis findings must be presented to Public Health. All datasets, code books, modeling analysis codes and procedures and any other data files used in the analysis submitted to Public Health.
Data Analysis Methods. The present study used SPSS to analyse the research data. As ▇▇▇▇▇ (2010) explained, before looking at correlations between variables, first of all, it is important to consider individual variables. Therefore, the present study first focused on the descriptive information, that is, how many boys and girls, where they come from as well as their ethnicity background. The next step was to investigate the relationship between two variables and to conduct bivariate analysis. Thus, the given research investigated the students’ likeliness to bully peers or to be bullied and what were the differences depending on gender, language of instruction, ethnicity, academic performance and residency.
Data Analysis Methods. The names of all participants were coded to ensure the anonymity of respondents (Respondent 1 – Respondent 6). The data was saved on the personal computer of the researcher protected by a password. The researcher reviewed each data document separately and noted meaningful segments or topics in each document by underlining the words, phrases or sentences. When these topics were identified in all data documents, the researcher made a table that comprised all the topics on the same sheet. Each column of the table represented a certain data document. Thus, the table had six columns. Afterward, the researcher highlighted similar topics with the same colors across the different columns. Similar topics were coded and categorised in relation to emerging themes. The researcher did not use any computer data analysis program, the data was coded manually even though the process took considerable time (▇▇▇▇▇▇▇▇, 2013).
Data Analysis Methods. The collected data was exported to an excel document to create a data file for SPSS statistical analysis. I used IBM SPSS Statistics Data Editor (Version 23; 2015) software to analyze collected data. In order to identify the highest and lowest-rated items in the survey, descriptive statistics procedure was applied to compute frequencies. Furthermore, to identify what was the overall attitude of teachers towards the motivational environment at the school, descriptive analysis was employed. SPSS software allowed me to apply a descriptive analysis to summarize the data. It included the identification of mean, mode and the frequency of responses. Regarding qualitative data analysis, the interview transcripts were analyzed using manual coding. The ‘grounded theory' principles were followed for data analysis and the concepts were developed from actual data (as cited in Givon & Court, 2010). The data analysis was driven by the inductive approach since themes that were mentioned several times were selected to be coded, and a set of concepts that were clearly related to the research question which identified core motives along with other categories having a relationship to that main category were developed.
Data Analysis Methods