Significance of Data analysis
Data analysis, as described across various fields, consistently involves examining and interpreting data to find patterns, trends, and relationships. This process often utilizes statistical methods and software to draw conclusions, support decision-making, and assess the impact of interventions or treatments. Specific techniques, like regression analysis, thematic analysis, and various statistical tests, are employed to extract meaningful insights and understand the study's findings.
Synonyms: Data assessment, Data evaluation, Data interpretation, Information analysis
In Dutch: Gegevensanalyse; In Finnish: Tietojen analysointi; In Spanish: Análisis de datos
The below excerpts are indicatory and do represent direct quotations or translations. It is your responsibility to fact check each reference.
Buddhist concept of 'Data analysis'
In Buddhism, data analysis involves scrutinizing information to find patterns and connections. This process assists in making informed decisions and solving problems. It's a method for understanding a specific situation more deeply, derived from the given text.
(1) This process involves examining and interpreting information to identify patterns, trends, and relationships, ultimately aiding in informed decision-making and problem-solving within a specific context, derived from the provided textual content.[1]
Hindu concept of 'Data analysis'
In Hinduism, data analysis involves examining collected information to draw conclusions, using statistical methods like t-tests. It includes inspecting, cleaning, and modeling data to identify patterns and understand relationships, supporting research objectives and treatment efficacy assessments.
(1) Data analysis is used to understand how HIS is useful for front office services and is conducted according to the objectives of the study, as the text describes.[2] (2) This term refers to the process of analyzing collected data and categorizing findings into etiology, classification, and management.[3] (3) This is the process of examining data to find patterns, and it is used in the study to draw conclusions about the relationship between variables.[4] (4) This is the process of examining the collected information to draw conclusions, such as the analysis performed using SPSS software.[5] (5) This is the process of examining the collected data, including visual inspection for errors and statistical tests like the Shapiro-Wilk's test, paired samples t-test, and ANCOVA.[6]
(1) The process of analyzing the data extracted using a Microsoft Excel sheet, with the average value among the three values considered, and data expressed as mean ± SD, with comparisons done using paired and unpaired t-tests.[7] (2) This is the process of using statistical methods to examine and interpret the data collected in a study, such as the IBM SPSS Statistics software used in this research.[8] (3) This indicates a crucial step in the research process, involving the systematic examination of collected information to identify patterns and draw conclusions.[9] (4) Data analysis is a critical aspect of the research, encompassing the collection, evaluation, and the drawing of conclusions based on the gathered information.[10] (5) This describes the statistical methods used to analyze the data collected from the study, providing a scientific basis for the findings.[11]
(1) Data analysis involved modifying methods to ensure the merit and completeness of the questionnaires and the originality of the oral interviews.[12] (2) This section explains how the data collected from the study was analyzed using statistical methods and software.[13] (3) This is the process of evaluating the data collected before and after treatment using statistical methods like paired and unpaired t-tests to determine the significance of the findings.[14]
(1) Data analysis involves the process of inspecting, cleaning, transforming, and modeling data to discover useful information, draw conclusions, and support decision-making, according to the provided text.[15] (2) The process of inspecting, cleansing, and modeling data to discover useful information.[16]
The concept of Data analysis in scientific sources
Data analysis, as described, is a systematic process of examining collected information. It involves identifying patterns, trends, and relationships through various statistical methods and software, like SPSS, Stata, and Excel. The goal is to interpret data, draw conclusions, and inform study findings.
(1) Data analysis involved a qualitative approach where responses were inductively coded by researchers to identify themes and categories related to social accountability.[17] (2) Mixed methods were used to analyze the data, including qualitative data analyzed using the open coding method and quantitative data analyzed using quasistatistics, as stated in the text.[18] (3) The process of examining and interpreting information was done by T.S.M., E.R. and S.N. as a part of the research study.[19] (4) This was performed using IBM SPSS 25 to analyze the data, including descriptive statistics like frequencies, percentages, means, and standard deviation.[20] (5) This is the process of interpreting the data, cleaned and analyzed using the International Business Manual (IBM) Statistical Package for Social Science (SPSS) version 27.[21]
(1) Data analysis involved using Tesch’s method to analyze the transcribed responses of participants, identifying themes and subthemes to understand the experiences of home-based care workers.[22] (2) The process of examining the collected information, including scoring questionnaire items and determining the percentages of correct or incorrect responses, as described in the text.[23] (3) This describes the process of systematically examining the collected data to identify patterns and draw conclusions.[24] (4) This is the process of organizing, summarizing, and interpreting the collected data to draw conclusions about the research questions, using statistical methods.[25] (5) This refers to the systematic process of examining the collected data, using techniques like constant comparative technique and coding, to identify patterns and themes.[26]
(1) JMP participated in the study design, field activities, analysis of data, and drafted the paper, while others participated in the study design and analysis, coordinated its implementation and revised subsequent drafts of the manuscript.[27] (2) This is the process of examining data to draw conclusions, and the study used both quantitative and qualitative methods of data analysis to understand healthcare workers' perceptions.[28] (3) This is the process of examining and interpreting the data collected in a study, using statistical methods to draw conclusions and identify patterns.[29] (4) This is the process of examining and interpreting the collected information, using statistical methods to identify patterns, relationships, and draw conclusions from the research data.[30] (5) This is the process of examining the collected data, using statistical software to summarize and analyze the distribution of categorical variables and HIVST outcomes.[31]
(1) Data analysis was performed using SPSS version 19.0 software to examine bivariate associations using the Chi-square test, and binary logistic regression was utilized to study associations.[32] (2) Data analysis is the process of examining the collected data, using statistical methods to identify patterns, relationships, and significant findings related to stress and coping.[33] (3) This used descriptive statistics to summarize the patient's characteristics, resource use, cost per component, and the total cost of the illness.[34] (4) Data analysis is the process of interpreting data collected during a study, and it was performed by TMT and SAD.[35] (5) This involves the application of statistical methods to examine the collected data, including descriptive statistics and regression analyses, to determine factors associated with the cost.[36]
(1) This process was conducted to understand the ideas that emerged during the interviews, and the thematic analysis focused on identifying patterns of expressions from the participants.[37] (2) This was done using Statistical Package for the Social Sciences (SPSS), and total scores and the mean score for the RMDQ were calculated during the process.[38] (3) This is the process used to analyze the collected data, and it was conducted under the supervision of a statistician using a specific software version.[39] (4) This is the process of examining and interpreting data to draw conclusions, and the study highlights the need for enhanced competencies in this area.[40] (5) This is the process of examining and interpreting the collected information, a time-consuming part of the ink footprint method used in the study.[41]
(1) The process of examining extracted data, entered into a spreadsheet, cleaned, and imported into a statistical software package for analysis.[42] (2) This was performed by L.C., who also assisted in conceptualising the study, writing the protocol, and performed the data collection, as stated in the text.[43] (3) The data were analyzed manually using the thematic content analysis guided by the five stages of thematic analysis as illustrated by Pope et al.[44] (4) The process of examining data to draw conclusions, with all answers recorded on the paper survey tools and later entered into Excel for side-by-side analysis of each question for all four sites.[45] (5) This was conducted using statistical software, and the prevalence of blood and body fluid exposures was calculated.[46]
(1) The process of evaluating data to determine individual baseline fGCM concentrations, periods of elevated fGCM concentrations, and the baseline stability of each EIA.[47] (2) This refers to the process of examining the collected data to determine the prevalence of mastitis and associated factors, using methods like logistic regression, according to the text.[48] (3) Data analysis was performed using MEGA 6.06 software, and the p72 and p54 nucleotide sequences were aligned using Clustal W for phylogenetic reconstruction.[49] (4) The data were entered into a Microsoft Excel spreadsheet and coded appropriately, and SPSS version 16 was used for this.[50] (5) This is the process of examining and interpreting the collected data, utilizing descriptive statistics and analysis of variance to understand the impact of trypanosomosis risk on dairy cattle production.[51]
(1) This is the process of examining and interpreting data.[52] (2) This is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, inform conclusions, and support decision-making, which was performed in the study.[53] (3) The process of recording and analyzing data on livestock diseases, plant names, preparation methods, and administration routes, using statistical tools to achieve the study's objectives.[54] (4) The process of examining and interpreting information, which involves performing calculations and drawing conclusions to summarize the findings presented in the research.[55] (5) This refers to the process of interpreting and making sense of the data, which was done using specific software.[56]
(1) This is a process, and the author thanks Dr. Clare Craig for edits and data analysis, and Alex Starling for comments and suggestions, according to the provided text.[57] (2) The process of examining and interpreting data, where descriptive statistics and paired t-tests were used to characterize the study population and examine changes in outcomes over time.[58] (3) The process of examining data to draw conclusions, which includes calculating the coefficient of determination from the reported data, as discussed in the study.[59] (4) Data analysis was performed by certain authors, including C.A.P.C., C.Z., and G.S., as part of their contributions to the research.[60] (5) This is the process of examining the collected data to draw conclusions about the effects of the intervention.[61]
(1) The statistical methods employed to interpret the experimental data, including calculating means, standard errors, and performing comparisons between groups.[62] (2) Statistical methods, including one-way analysis of variance (ANOVA) and student's t-test, used to analyze the experimental data and determine significance.[63] (3) Data analysis in this study involved calculating the Use Value (UV) and Informant Consensus Factor (Fic) to assess the importance and homogeneity of plant use in traditional medicine.[64] (4) Data analysis involved statistical comparison between experimental groups using methods like One-Way Analysis of Variance (ANOVA) to determine the significance of the observed results.[65] (5) The process of evaluating and interpreting the collected data using statistical methods to draw conclusions about the effects of the treatments.[66]
(1) The process of examining and interpreting collected data to draw conclusions.[67] (2) Gathered data is subjected to advanced methods like statistical analysis and machine learning to find patterns and trends that guide decision-making.[68]
(1) The process of inspecting, cleansing, transforming, and modeling data to discover useful information and support decision-making.[69] (2) Data analysis was conducted by the principal investigator, A.T., as part of their responsibilities for the project.[70] (3) The process of examining and interpreting data using statistical methods to identify patterns, relationships, and trends, providing insights into research questions.[71] (4) This was done using the Statistical Package for Social Sciences, where the questionnaire responses were entered into the database and used to determine the percentage of university students with accurate and inaccurate beliefs.[72] (5) This was conducted using the Statistical Package for Social Sciences, Windows Version 13, setting the level of probability at a 5% level of significance for the study.[73]
(1) The statistical methods employed to interpret the experimental data, including calculation of means, standard errors, and significance testing.[74] (2) Data analysis involved using One-Way ANOVA followed by Tukey's test to determine statistical significance between different experimental groups.[75] (3) Statistical methods, including mean, standard deviation, and ANOVA with Duncan's test, used to interpret the experimental results.[76] (4) The process of analyzing collected data using statistical software like SAS version to compare reporting systems and calculate rates of testing and resistance.[77] (5) The process of inspecting, cleaning, transforming, and modeling data to discover useful information.[78]