Analysis of data
•Download as PPTX, PDF•
2 likes•115 views
The short and sweet introduction to the analysis of research data for the GNM nursing students which involves the all components in brief.
Report
Share
Report
Share
Recommended
Processing of research data
This document outlines the key steps in processing research data: editing to gather accurate and complete data by finding and correcting errors; coding to organize data through assigning numerical values to categories to enable analysis; classification and distribution such as frequency, percentage, and cumulative distributions; and tabulation to present overall findings in a simplified way and facilitate comparison, trends, and further statistical computation.
Data processing and presentation
data processing and presentation
,
editing
,
model building
,
stages of data analysis/processing operations
,
coding
,
inferential analysis
,
classification
,
tabulation
,
analysis
,
descriptive analysis
,
fact finding
,
common research objectives for secondary data stu
,
data based marketin
Fundamentals of data presentation
To know about the fundamentals of data presentation visit My Assignment Services and get the session with PhD experts.
3 statistic
Statistics is the study of collecting, organizing, analyzing, and interpreting data. It involves planning data collection through surveys and experiments, and using descriptive statistics like means, frequencies, and percentages to summarize sample data numerically or graphically. Standard deviation is a measure of variability used to show how dispersed data points are from the average or mean value, with lower standard deviation indicating data is close to the mean and higher standard deviation showing data is more spread out.
Editing, coding and tabulation of data
Its a fully detailed topic about Editing , Coding, Tabulation o Data in research work.
The editing , coding , tabulation of data is been explained in this ppt.
Descriptive statistics
What are descriptive Statistics, Types of statistics and its implications.
graphs, tables, variation, mean mode median, central tendency,
Basics of statistics
This document provides an overview of basic statistics concepts. It defines data and information, and explains that processing data with meaningful value results in information. It describes statistics as measuring uncertainty, and discusses descriptive and inferential statistics. Descriptive statistics describe measurements, while inferential statistics make inferences about a population from a sample. It also outlines different types of data, variables, and characteristics of data like central tendency, variation, and distribution. Finally, it discusses types of statistical studies and graphs commonly used in statistics.
Research Methodology-Data Processing
Data processing involves 5 key steps: editing data, coding data, classifying data, tabulating data, and creating data diagrams. It transforms raw collected data into a usable format through these steps of cleaning, organizing, and analyzing the data. First, data is collected from sources and prepared by cleaning errors. It is then inputted and processed using algorithms before being output and interpreted in readable formats. Finally, the processed data is stored for future use and reports.
Recommended
Processing of research data
This document outlines the key steps in processing research data: editing to gather accurate and complete data by finding and correcting errors; coding to organize data through assigning numerical values to categories to enable analysis; classification and distribution such as frequency, percentage, and cumulative distributions; and tabulation to present overall findings in a simplified way and facilitate comparison, trends, and further statistical computation.
Data processing and presentation
data processing and presentation
,
editing
,
model building
,
stages of data analysis/processing operations
,
coding
,
inferential analysis
,
classification
,
tabulation
,
analysis
,
descriptive analysis
,
fact finding
,
common research objectives for secondary data stu
,
data based marketin
Fundamentals of data presentation
To know about the fundamentals of data presentation visit My Assignment Services and get the session with PhD experts.
3 statistic
Statistics is the study of collecting, organizing, analyzing, and interpreting data. It involves planning data collection through surveys and experiments, and using descriptive statistics like means, frequencies, and percentages to summarize sample data numerically or graphically. Standard deviation is a measure of variability used to show how dispersed data points are from the average or mean value, with lower standard deviation indicating data is close to the mean and higher standard deviation showing data is more spread out.
Editing, coding and tabulation of data
Its a fully detailed topic about Editing , Coding, Tabulation o Data in research work.
The editing , coding , tabulation of data is been explained in this ppt.
Descriptive statistics
What are descriptive Statistics, Types of statistics and its implications.
graphs, tables, variation, mean mode median, central tendency,
Basics of statistics
This document provides an overview of basic statistics concepts. It defines data and information, and explains that processing data with meaningful value results in information. It describes statistics as measuring uncertainty, and discusses descriptive and inferential statistics. Descriptive statistics describe measurements, while inferential statistics make inferences about a population from a sample. It also outlines different types of data, variables, and characteristics of data like central tendency, variation, and distribution. Finally, it discusses types of statistical studies and graphs commonly used in statistics.
Research Methodology-Data Processing
Data processing involves 5 key steps: editing data, coding data, classifying data, tabulating data, and creating data diagrams. It transforms raw collected data into a usable format through these steps of cleaning, organizing, and analyzing the data. First, data is collected from sources and prepared by cleaning errors. It is then inputted and processed using algorithms before being output and interpreted in readable formats. Finally, the processed data is stored for future use and reports.
Presentation1
This document discusses statistical analysis and its various types and uses. It describes statistical analysis as the critical evaluation of data to study relationships between variables and identify patterns. Both quantitative and qualitative methods are used, though social research often relies on quantitative analysis and statistical techniques. Statistical analysis summarizes large data sets, facilitates identifying causal factors, aids making reliable inferences from observational data, and allows estimation and generalization from sample surveys. The document outlines descriptive analysis, inferential analysis, and computerized analysis as the main types. It also discusses various statistical measures like measures of central tendency, dispersion, and relation.
Research methodology - Analysis of Data
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
analysis and presentation of data
The document discusses different methods of analyzing and presenting quantitative data. It describes two main types of analysis: qualitative analysis, which involves interpreting qualitative research data, and quantitative analysis, which involves presenting and interpreting numerical data through descriptive and inferential statistics. Descriptive statistics include measures of central tendency like mean, median and mode, as well as measures of variability like range and standard deviation. Inferential statistics are used to test hypotheses and generalize samples to populations. The document also discusses various methods of graphically presenting quantitative data through graphs like histograms, frequency polygons, frequency curves and Ogive curves.
Introduction to statistics
This document provides an introduction to descriptive statistics and measures of central tendency, including the mean, median, and mode. It discusses how the mean can be impacted by outliers, while the median is not. The standard deviation and variance are introduced as measures of dispersion that quantify how much values vary from the mean or from each other. Finally, the document discusses different ways of organizing and graphing data, including histograms, pie charts, line graphs, and scatter plots.
Quatitative Data Analysis
This document provides an overview of quantitative data analysis. It discusses data preparation, descriptive statistics such as measures of central tendency and dispersion, inferential statistics, and interpretation of results. The key steps in quantitative analysis are described as data preparation, describing the data through descriptive statistics, drawing inferences through inferential statistics, and interpreting the findings. Common statistical techniques like mean, median, mode, standard deviation, and correlation are also summarized.
Descriptive statistics
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
Statistics Introduction
This document provides an introduction to statistics. It defines statistics as techniques used to collect, organize, analyze and interpret quantitative data. There are two main kinds of statistics: descriptive statistics, which summarizes and describes data through graphical or computational methods; and inferential statistics, which makes inferences about populations based on samples. Key statistical concepts introduced include populations, samples, data types (continuous and discrete), methods of data presentation (graphs), and measures of central tendency (mean, median, mode) and dispersion (range).
Descriptive statistics ppt
This document discusses descriptive statistics and how to calculate them. It covers preparing data for analysis through coding and tabulation. It then defines four types of descriptive statistics: measures of central tendency like mean, median, and mode; measures of variability like range and standard deviation; measures of relative position like percentiles and z-scores; and measures of relationships like correlation coefficients. It provides formulas for calculating common descriptive statistics like the mean, standard deviation, and Pearson correlation.
Data processing
This document discusses various aspects of data processing and analysis in marketing research. It covers topics like data editing, coding, classification, tabulation and exploratory data analysis. Various statistical techniques for univariate, bivariate and multivariate analysis of data are also summarized. These include techniques like frequency analysis, t-tests, chi-square, ANOVA etc. for metric and non-metric data.
Presentation of data
This document discusses three methods of presenting data: textual or descriptive presentation, tabular presentation, and diagrammatic presentation. It focuses on tabular presentation, explaining that data is arranged in rows and columns and can be classified in four ways: qualitatively, quantitatively, temporally, or spatially. The components of a table are also listed.
Intro to statistics
This document provides an introduction and overview of key concepts in statistics. It defines statistics as the collection, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize and organize data, while inferential statistics allow researchers to make generalizations from a sample to a population. The document outlines common terminology in statistics, different types of data and scales of measurement, and how to present data through tables, graphs, and diagrams. Frequency distribution tables, bar diagrams, pie charts, and histograms are discussed as methods for graphical presentation of data.
Introduction to Statistics
This document provides an introduction to basic statistical concepts. It defines statistics as the study of numerical data and notes that while it uses mathematics, statistics arises from practical situations. The founder of modern statistics is identified as Ronald Fisher. Primary data is defined as data collected directly from sources, while secondary data is collected from existing sources. Key concepts explained include range, frequency, frequency tables, bar graphs, histograms, frequency polygons, and measures of central tendency like mean, median and mode. An example is provided to illustrate calculating these measures.
Basics of statistics
This is a ppt on Basic terminology of statistics based on my understanding after attending a workshop at NIMHANS.
Statistics ( central tendency / average)
This document discusses measures of central tendency, or averages, which are single values that represent a group of data values. It defines averages as values around which other data points congregate. The document outlines three types of averages: mathematical average, positional average, and commercial average. Positional averages include the median and mode. The median divides the data set into two equal parts, while the mode is the value that occurs most frequently. The document discusses the merits and demerits of each type of average.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Data Analysis
The document discusses various topics related to data analysis, including:
- Data analysis is defined as systematically organizing qualitative data to increase understanding of a phenomenon. It involves coding data and identifying patterns.
- Qualitative data comes in unstructured forms like interviews, observations, diaries and records. Analysis is more intuitive than quantitative analysis and focuses on values, meanings and experiences.
- Data can be measured on nominal, ordinal, interval or ratio scales depending on the properties they satisfy. Nominal data are categorical while ordinal data have a ranking order. Interval and ratio data have equal units of measurement.
- Common types of qualitative data analysis include content analysis, narrative analysis, discourse analysis, framework analysis and grounded theory
Business analytics
This ppt is all about Descriptive analysis , data summarization , its phases, through tables, using different scales i.e. Ordinal , nominal , interval, & ratio.
Frequency Distribution , Data View and variable view in spss ,
steps for analysis , data summarization through graphs/charts.
Usage of Histogram, bar chart and pie chart & box plot in SPSS .
Introduction to Statistics - Basic concepts
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
Data processing.pdf
This document discusses the process of data processing. It defines data processing as the intermediary stage between data collection and data interpretation. The key steps in data processing include identifying the data structure, editing the data, coding and classifying the data, transcribing the data, and tabulating the data. These steps prepare the raw data for meaningful analysis and interpretation to test research hypotheses. Proper data processing requires advance planning and defines the variables and relationships between them.
Introduction to Data Analysis for Nurse Researchers
The document provides an overview of data analysis for nursing students. It discusses the importance of statistical training for establishing cause-and-effect relationships and measuring health outcomes. The key steps of data analysis are described, including computing, editing, coding, selecting software, entering, cleaning and classifying data. Both descriptive and inferential statistical methods are covered. Descriptive statistics summarize and describe data through measures like frequency, percentage, mean, median and mode. Inferential statistics allow drawing conclusions about populations from samples using parametric or nonparametric tests. Qualitative data analysis involves coding, identifying themes in the data, and interpreting patterns.
Chapter 11 Data Analysis Classification and Tabulation
Data analysis involves classifying and tabulating data to identify relationships and make inferences. There are two main types of data analysis: qualitative analysis which handles categorical data, and quantitative analysis which uses statistical methods on numerical data. The goals of data analysis are to understand the data, answer research questions, identify patterns, and make predictions. Key aspects of data analysis include variables, attributes, parametric vs non-parametric statistics, classification methods, and tabulation which organizes data into tables.
Nursing Data Analysis.pptx
This document outlines the process of data analysis, which involves collecting, processing, cleaning, analyzing, and communicating data. The goal is to discover useful information and patterns in the data. Data analysis consists of several iterative phases: specifying data requirements, collecting data, processing and organizing data, cleaning data, analyzing data through various statistical techniques, and communicating the results. Findings are presented objectively using descriptive statistics and tables/figures. The findings, their meaning, how reliability/validity were maintained, and comparisons to previous studies are discussed. Conclusions address if the study problem/purpose were achieved. Implications and recommendations for further research are also provided.
More Related Content
What's hot
Presentation1
This document discusses statistical analysis and its various types and uses. It describes statistical analysis as the critical evaluation of data to study relationships between variables and identify patterns. Both quantitative and qualitative methods are used, though social research often relies on quantitative analysis and statistical techniques. Statistical analysis summarizes large data sets, facilitates identifying causal factors, aids making reliable inferences from observational data, and allows estimation and generalization from sample surveys. The document outlines descriptive analysis, inferential analysis, and computerized analysis as the main types. It also discusses various statistical measures like measures of central tendency, dispersion, and relation.
Research methodology - Analysis of Data
Processing & Analysis of Data, Data editing, Benefits of data editing, Data coding, Classification of data, CLASSIFICATION ACCORDING THE ATTRIBUTES, CLASSIFICATION ON THE BASIS OF INTERVAL, TABULATION of data, Types of tables, Graphing of data, Bar chart, Pie chart, Line graph, histogram, Polygon / ogive, Analysis of Data, Descriptive Analysis, Uni-Variate Analysis, Bivariate Analysis, Multi-Variate Analysis, Causal Analysis, Inferential Analysis, PARAMETRIC TESTS, Non parametric Test,
analysis and presentation of data
The document discusses different methods of analyzing and presenting quantitative data. It describes two main types of analysis: qualitative analysis, which involves interpreting qualitative research data, and quantitative analysis, which involves presenting and interpreting numerical data through descriptive and inferential statistics. Descriptive statistics include measures of central tendency like mean, median and mode, as well as measures of variability like range and standard deviation. Inferential statistics are used to test hypotheses and generalize samples to populations. The document also discusses various methods of graphically presenting quantitative data through graphs like histograms, frequency polygons, frequency curves and Ogive curves.
Introduction to statistics
This document provides an introduction to descriptive statistics and measures of central tendency, including the mean, median, and mode. It discusses how the mean can be impacted by outliers, while the median is not. The standard deviation and variance are introduced as measures of dispersion that quantify how much values vary from the mean or from each other. Finally, the document discusses different ways of organizing and graphing data, including histograms, pie charts, line graphs, and scatter plots.
Quatitative Data Analysis
This document provides an overview of quantitative data analysis. It discusses data preparation, descriptive statistics such as measures of central tendency and dispersion, inferential statistics, and interpretation of results. The key steps in quantitative analysis are described as data preparation, describing the data through descriptive statistics, drawing inferences through inferential statistics, and interpreting the findings. Common statistical techniques like mean, median, mode, standard deviation, and correlation are also summarized.
Descriptive statistics
Descriptive statistics are used to describe and summarize the basic features of data through measures of central tendency like the mean, median, and mode, and measures of variability like range, variance and standard deviation. The mean is the average value and is best for continuous, non-skewed data. The median is less affected by outliers and is best for skewed or ordinal data. The mode is the most frequent value and is used for categorical data. Measures of variability describe how spread out the data is, with higher values indicating more dispersion.
Statistics Introduction
This document provides an introduction to statistics. It defines statistics as techniques used to collect, organize, analyze and interpret quantitative data. There are two main kinds of statistics: descriptive statistics, which summarizes and describes data through graphical or computational methods; and inferential statistics, which makes inferences about populations based on samples. Key statistical concepts introduced include populations, samples, data types (continuous and discrete), methods of data presentation (graphs), and measures of central tendency (mean, median, mode) and dispersion (range).
Descriptive statistics ppt
This document discusses descriptive statistics and how to calculate them. It covers preparing data for analysis through coding and tabulation. It then defines four types of descriptive statistics: measures of central tendency like mean, median, and mode; measures of variability like range and standard deviation; measures of relative position like percentiles and z-scores; and measures of relationships like correlation coefficients. It provides formulas for calculating common descriptive statistics like the mean, standard deviation, and Pearson correlation.
Data processing
This document discusses various aspects of data processing and analysis in marketing research. It covers topics like data editing, coding, classification, tabulation and exploratory data analysis. Various statistical techniques for univariate, bivariate and multivariate analysis of data are also summarized. These include techniques like frequency analysis, t-tests, chi-square, ANOVA etc. for metric and non-metric data.
Presentation of data
This document discusses three methods of presenting data: textual or descriptive presentation, tabular presentation, and diagrammatic presentation. It focuses on tabular presentation, explaining that data is arranged in rows and columns and can be classified in four ways: qualitatively, quantitatively, temporally, or spatially. The components of a table are also listed.
Intro to statistics
This document provides an introduction and overview of key concepts in statistics. It defines statistics as the collection, analysis, and interpretation of numerical data. Descriptive statistics are used to summarize and organize data, while inferential statistics allow researchers to make generalizations from a sample to a population. The document outlines common terminology in statistics, different types of data and scales of measurement, and how to present data through tables, graphs, and diagrams. Frequency distribution tables, bar diagrams, pie charts, and histograms are discussed as methods for graphical presentation of data.
Introduction to Statistics
This document provides an introduction to basic statistical concepts. It defines statistics as the study of numerical data and notes that while it uses mathematics, statistics arises from practical situations. The founder of modern statistics is identified as Ronald Fisher. Primary data is defined as data collected directly from sources, while secondary data is collected from existing sources. Key concepts explained include range, frequency, frequency tables, bar graphs, histograms, frequency polygons, and measures of central tendency like mean, median and mode. An example is provided to illustrate calculating these measures.
Basics of statistics
This is a ppt on Basic terminology of statistics based on my understanding after attending a workshop at NIMHANS.
Statistics ( central tendency / average)
This document discusses measures of central tendency, or averages, which are single values that represent a group of data values. It defines averages as values around which other data points congregate. The document outlines three types of averages: mathematical average, positional average, and commercial average. Positional averages include the median and mode. The median divides the data set into two equal parts, while the mode is the value that occurs most frequently. The document discusses the merits and demerits of each type of average.
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
The present article helps the USA, the UK and the Australian students pursuing their business and marketing postgraduate degree to identify right topic in the area of marketing in business. These topics are researched in-depth at the University of Columbia, brandies, Coventry, Idaho, and many more. Stats work offers UK Dissertation stats work Topics Services in business. When you Order stats work Dissertation Services at Tutors India, we promise you the following – Plagiarism free, Always on Time, outstanding customer support, written to Standard, Unlimited Revisions support and High-quality Subject Matter Experts.
Contact Us:
Website: www.statswork.com
Email: info@statswork.com
UnitedKingdom: +44-1143520021
India: +91-4448137070
WhatsApp: +91-8754446690
Data Analysis
The document discusses various topics related to data analysis, including:
- Data analysis is defined as systematically organizing qualitative data to increase understanding of a phenomenon. It involves coding data and identifying patterns.
- Qualitative data comes in unstructured forms like interviews, observations, diaries and records. Analysis is more intuitive than quantitative analysis and focuses on values, meanings and experiences.
- Data can be measured on nominal, ordinal, interval or ratio scales depending on the properties they satisfy. Nominal data are categorical while ordinal data have a ranking order. Interval and ratio data have equal units of measurement.
- Common types of qualitative data analysis include content analysis, narrative analysis, discourse analysis, framework analysis and grounded theory
Business analytics
This ppt is all about Descriptive analysis , data summarization , its phases, through tables, using different scales i.e. Ordinal , nominal , interval, & ratio.
Frequency Distribution , Data View and variable view in spss ,
steps for analysis , data summarization through graphs/charts.
Usage of Histogram, bar chart and pie chart & box plot in SPSS .
Introduction to Statistics - Basic concepts
Introduction to Statistics - Basic concepts
- How to be a good doctor - A step in Health promotion
- By Ibrahim A. Abdelhaleem - Zagazig Medical Research Society (ZMRS)
What's hot (18)
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
Statistical Data Analysis | Data Analysis | Statistics Services | Data Collec...
Similar to Analysis of data
Data processing.pdf
This document discusses the process of data processing. It defines data processing as the intermediary stage between data collection and data interpretation. The key steps in data processing include identifying the data structure, editing the data, coding and classifying the data, transcribing the data, and tabulating the data. These steps prepare the raw data for meaningful analysis and interpretation to test research hypotheses. Proper data processing requires advance planning and defines the variables and relationships between them.
Introduction to Data Analysis for Nurse Researchers
The document provides an overview of data analysis for nursing students. It discusses the importance of statistical training for establishing cause-and-effect relationships and measuring health outcomes. The key steps of data analysis are described, including computing, editing, coding, selecting software, entering, cleaning and classifying data. Both descriptive and inferential statistical methods are covered. Descriptive statistics summarize and describe data through measures like frequency, percentage, mean, median and mode. Inferential statistics allow drawing conclusions about populations from samples using parametric or nonparametric tests. Qualitative data analysis involves coding, identifying themes in the data, and interpreting patterns.
Chapter 11 Data Analysis Classification and Tabulation
Data analysis involves classifying and tabulating data to identify relationships and make inferences. There are two main types of data analysis: qualitative analysis which handles categorical data, and quantitative analysis which uses statistical methods on numerical data. The goals of data analysis are to understand the data, answer research questions, identify patterns, and make predictions. Key aspects of data analysis include variables, attributes, parametric vs non-parametric statistics, classification methods, and tabulation which organizes data into tables.
Nursing Data Analysis.pptx
This document outlines the process of data analysis, which involves collecting, processing, cleaning, analyzing, and communicating data. The goal is to discover useful information and patterns in the data. Data analysis consists of several iterative phases: specifying data requirements, collecting data, processing and organizing data, cleaning data, analyzing data through various statistical techniques, and communicating the results. Findings are presented objectively using descriptive statistics and tables/figures. The findings, their meaning, how reliability/validity were maintained, and comparisons to previous studies are discussed. Conclusions address if the study problem/purpose were achieved. Implications and recommendations for further research are also provided.
lecture-8.pdf
This document discusses the analysis of quantitative and qualitative data in research. It describes the key steps in analyzing quantitative data, including data preparation, compilation, editing, coding, classification, tabulation, descriptive statistics, inferential statistics, and interpreting results. For qualitative data analysis, it outlines ordering and reducing data, summarizing data using various techniques, drawing conclusions, reporting findings, and establishing validity.
statistical analysis
This document provides an overview of statistical analysis for nursing research. It defines key terms like statistics, data analysis, and population. It outlines the specific objectives of understanding statistical analysis and applying it to nursing research skillfully. It also describes the various types of statistical analysis including descriptive statistics, inferential statistics, parametric and nonparametric tests. Finally, it discusses the steps in statistical analysis, available computer programs, uses of statistical analysis in different fields including nursing, and advantages and disadvantages of statistical analysis.
ANALYSIS OF DATA (2).pptx
The document discusses processing and analyzing data. It explains that data must be processed after collection by editing, coding, classifying, and tabulating it to prepare it for analysis. It then describes various methods of qualitative and quantitative data analysis, including content analysis, narrative analysis, and hypothesis testing. Finally, it discusses measures used to analyze data, such as central tendency (mean, median, mode), measures of dispersion (range, variance, standard deviation), and skewness.
Analysis of Data.pptx
This document defines and explains the key components of data analysis: compilation, tabulation, classification, presentation, and interpretation. It provides details on each component, such as that compilation refers to statistical procedures for producing intermediate and final data outputs, tabulation organizes numeric data in rows and columns for comparison, and classification arranges data into groups based on common characteristics. The document also describes different methods of data presentation, including textual, tabular, and graphical formats. Finally, it states that interpretation involves reviewing analyzed data and making inferences to arrive at relevant conclusions.
Unit 4 editing and coding (2)
This document discusses data analysis and various techniques used in data analysis such as data editing, coding, classification, tabulation, and statistical analysis. It describes different types of statistical tests like z-test, t-test, chi-square test, and their uses. It also discusses various types of tables, diagrams, and graphical representations that are used to present statistical data in a meaningful way. Key types of diagrams mentioned include bar charts, pie charts, histograms and scatter plots. Rules for properly constructing tables and graphs are also provided.
Statistical analysis, presentation on Data Analysis in Research.
This document summarizes statistical data analysis. It discusses the meaning of data analysis as inspecting, cleansing, transforming and modeling data to discover useful information for decision making. The objectives and steps of data analysis are defined as defining objectives, preparing data, descriptive analysis, confirmatory analysis, interpretation and reporting. Quantitative analysis involves measures like mean and standard deviation while qualitative analysis examines interviews and documents for common patterns. Benefits to business include informed decision making, identifying trends, cost efficiency and strategic planning. Methods of data interpretation are collecting clean data, choosing qualitative or quantitative analysis methods, observing qualitative data and using statistical measures for quantitative data.
Data analysis plan in medicine and nurse.pptx
This document outlines elements of developing a data analysis plan, including defining data analysis, common analysis methods, and key components of an effective plan. It discusses:
1) Quantitative and qualitative analysis methods like descriptive statistics and thematic analysis.
2) Key elements of a plan such as research questions, variables, proposed analyses, and presenting findings.
3) Developing a plan by identifying questions and variables, choosing analyses, and stating how results will be reported.
1. Data Process.pptx
This document discusses various methods of processing raw data collected during research. It defines key terms like editing, coding, classification, and tabulation. Editing involves examining raw data to detect and correct errors. Coding assigns numerals or symbols to categorize responses for analysis. Classification groups data into common categories based on attributes or class intervals. Tabulation summarizes data in an organized table format for further analysis. Proper data processing through these methods is important for obtaining reliable results from statistical analysis.
Data processing and analysis
This document discusses the processes of data analysis and data processing. It defines data analysis as discovering useful information through inspecting, cleansing, transforming and modeling data, while data processing refers to rearranging data that has already been analyzed. The key steps in data processing are: 1) identifying data structures, 2) editing data for completeness and accuracy, 3) coding data numerically or alphabetically, 4) classifying data into groups, 5) transcribing data manually or via computer, 6) tabulating data into frequency distributions, contingency tables or other table types, and 7) summarizing data using manual or computerized data sheets, compilation sheets, matrices, figures or tables.
Statistics.pptx
Statistics can be categorized into descriptive and inferential types. Descriptive statistics summarize data from samples using measures like mean and standard deviation, while inferential statistics interpret descriptive statistics to draw conclusions. There are four levels of measurement scales: nominal for categories without ordering; ordinal for ordered categories; interval for equal intervals but arbitrary zero; and ratio for absolute zero. Proper use of statistics and scales allows for accurate data analysis across various fields.
analysis of data.pptx
Analysis and interpretation of data is the final step of the research process. It involves properly compiling, editing, coding, and presenting the raw data using tables, figures, and text. Accurate and objective interpretation allows readers to understand the findings coherently. Errors in data can occur randomly, systematically due to faulty instruments, bias, or confounding from external variables. Researchers should employ standardized equipment, inclusive criteria, large sample sizes, and randomization to avoid errors. Data analysis involves organizing data to answer research questions, test hypotheses, and assess relationships between variables through descriptive and inferential statistics.
LESSON 4_UNGROUPED.pptx.pdf
- This document discusses descriptive statistics for ungrouped data, including how to organize raw data into a frequency distribution and present it graphically. It also defines measures of central tendency like mean, median and mode, as well as measures of dispersion like range, variance and standard deviation. Several examples and exercises are provided to illustrate calculating these common statistical measures for ungrouped data sets.
Lecture 1- data preparation.pptx
This document provides an overview of data processing and report writing for business research methods. It discusses various steps in data processing including data preparation, coding, tabulation, cleaning, and adjusting. Data preparation involves checking questionnaires for completeness and editing data to ensure accuracy. Coding assigns symbols to responses to categorize data. Tabulation summarizes raw data in a logical format. Graphical representations like bar charts and pie charts can visualize categorized data. Data cleaning checks for consistency and treats missing values. Data adjusting may involve weighting samples, modifying variables, or transforming scales. The overall goal is to prepare raw data for meaningful analysis.
RM UNIT 6.pptx
This document discusses quantitative data analysis. It defines quantitative data as numerical data that can be statistically analyzed. There are different types of quantitative data like counts, measurements, sensory calculations, and projections. Data coding is explained as the process of assigning codes to raw data to organize and summarize it for analysis. Visual aids like tables, bar charts, pie charts, scatter plots, and line graphs are described as ways to present quantitative data visually to identify patterns and relationships. Statistics can then be used to analyze the coded and visualized quantitative data.
Rm presentation
This presentation discusses data analysis and its process. It defines data analysis as analyzing data to determine its usefulness for research. The analysis involves various methods and techniques. The process of data analysis includes editing, coding, classification, and tabulation of the data. Some common methods of data analysis mentioned are measures of central tendency, measures of dispersion, ANOVA, regression, correlation, and time series analysis.
Rm presentation
This presentation discusses data analysis and its process. It defines data analysis as analyzing data to determine its usefulness for research. The analysis involves various methods and techniques. The process of data analysis includes editing, coding, classification, and tabulation of the data. Some common methods of data analysis mentioned are measures of central tendency, measures of dispersion, ANOVA, regression, correlation, and time series analysis.
Similar to Analysis of data (20)
Introduction to Data Analysis for Nurse Researchers
Introduction to Data Analysis for Nurse Researchers
Chapter 11 Data Analysis Classification and Tabulation
Chapter 11 Data Analysis Classification and Tabulation
Statistical analysis, presentation on Data Analysis in Research.
Statistical analysis, presentation on Data Analysis in Research.
More from Sushil Humane
MHN-Questions.pptx
This document contains 20 multiple choice questions related to psychiatric nursing. The questions cover topics like appropriate responses in different patient situations, explanations for behaviors observed in nurses or patients, identifying appropriate treatment plans and interventions, and recognizing signs that treatment goals have been met. Each question is followed by 4 possible answers, with only one indicated as being correct.
Patient Safety.pptx
This document discusses fall prevention in patients. It defines falls and near falls. The objectives are to maintain patient safety and reduce injury risk by determining how falls occur and implementing prevention programs. Falls can be caused by individual factors like medical conditions, drugs, or aging, and environmental factors. Fall injuries are classified by level of severity from none to death. Risk assessment tools are used and precautions implemented based on a patient's risk level from low to high. Education of patients and families is also important for fall prevention.
Tuberculosis treatment.pptx
tuberculosis treatment in detail
based on Bsc nursing and MSc nursing course syllabus with updated guidelines
Social groups and processes.pptx
The document discusses various concepts related to social groups and processes. It defines social groups as collections of human beings brought together through social relationships and common goals. Social groups are classified in various ways such as primary and secondary groups, in-groups and out-groups. Social processes that occur within groups include cooperation, competition, conflict, accommodation, and interaction. Cooperation involves working together for common interests while competition is a struggle over limited resources. Conflict is a challenge between individuals or groups, and accommodation is the adjustment of hostile parties. Social interaction is how individuals influence each other through group involvement and relationships.
Population.pptx
The document discusses various topics related to population studies including demography, factors that influence population size such as births, deaths and migration. It covers Malthusian theories of population growth, population explosion in India, and government programs to control population growth and promote family welfare. Key points include how population grows geometrically while resources increase arithmetically per Malthus, causes and impacts of population explosion in India, and national schemes aimed at reducing birth rates and stabilizing population.
Assertive Training.pptx
Assertive communication allows people to interact by expressing their needs simply and directly. It involves communicating in a way that is clear, consistent, and courteous without disrespecting others or violating their rights. Researchers have linked assertiveness to improved outcomes in education, job satisfaction, and relationships. Assertive communication styles can help minimize conflict and control anger while having needs better met. Learning assertiveness involves practicing direct but respectful communication through techniques like thought-stopping to replace negative thoughts. Assertiveness is important for nurses to communicate effectively while managing a busy schedule and high stress environment.
Nursing career guidance.pptx
This document provides an overview of nursing career options in India. It classifies nursing careers into three main categories: clinical nursing (60%), nursing education (30%), and other sectors (10%). Some of the clinical nursing roles discussed include staff nurse, nursing officer, nurse specialist, and nurse practitioner. Nursing education roles include positions at nursing colleges, schools, and private organizations. Other nursing career paths include roles in the army, public health, emergency response, industries, and schools. The document provides eligibility requirements to become a nurse and discusses the nursing degree/diploma required. It also outlines the exam process and strategies for cracking nursing exams. Finally, it briefly discusses opportunities for nursing careers and education abroad.
Dialysis.pptx
Hemodialysis involves removing waste and excess fluid from the blood of patients with kidney failure. It is done 3 times a week, with each session lasting 2-4 hours. Blood passes through a dialyzer containing semipermeable membranes where waste diffuses out of the blood into the dialysate solution. Ultrafiltration also removes fluid. Vascular access via fistula or graft is required. Nursing responsibilities include monitoring for hypotension and ensuring proper access care and dietary education. Peritoneal dialysis uses the peritoneal membrane and involves exchanging dialysate fluid in the abdomen via catheter.
Mental Hygiene.pptx
This document discusses concepts related to mental health and hygiene. It defines mental hygiene as dealing with promoting mental health and preventing/treating mental illness. Mental health is defined as a state of well-being where individuals can cope with stress and be productive. The document outlines strategies for maintaining good mental health, such as forgiving others, accepting yourself, and finding meaningful activities. It also discusses warning signs of poor mental health like mood changes, sadness, and substance abuse. Finally, it proposes strategies for adjusting to oneself and one's environment like understanding others, satisfying needs, and maintaining physical health.
GENITO URINARY DISORDERS-2.pptx
Brief Information regarding the disorders of the genitourinary system. This presentation involves the disorders of the urinary system including Chronic Kidney Disease, Congenital problems related to the urinary system, and renal cancers.
Genito-urinary system disorders-1.pptx
Brief description of genitourinary system-related disorders with their nursing management. This presentation involves glomerulonephritis, nephrotic syndrome, acute renal failure, and renal calculi.
Individual differences
Individual difference is a unit of post-basic BSc nursing syllabus. You can find relatable information about this topic. for a better understanding kindly refer to books. This presentation slides are for teachers use only
Therapeutic nurse patient relationship
The document discusses therapeutic communication and the therapeutic nurse-patient relationship. It defines therapeutic communication as an interpersonal interaction between the nurse and patient that focuses on meeting the patient's specific needs. The principles of therapeutic communication include maintaining focus on the patient, using self-disclosure appropriately, and avoiding social relationships with patients. Effective therapeutic communication techniques include listening, clarification, reflection, and informing. The phases of developing a therapeutic relationship are the pre-interaction, orientation, working, and termination phases. Maintaining proper boundaries and addressing resistance, transference, and countertransference are important for overcoming therapeutic impasses.
Substance Abuse
Substance abuse refers to disorders arising from the abuse of alcohol, drugs, and other chemicals. It is classified as F1 in ICD-10. Addiction involves physiological and psychological dependence on a substance, while abuse refers to impaired health. Dependence involves tolerance and withdrawal symptoms. Alcohol dependence is a chronic condition characterized by excessive and compulsive drinking that impairs functioning. It commonly leads to physical and psychological dependence as well as health, social, and legal problems. Relapse is the return to substance abuse after a period of abstinence.
Reference letter for tenant
Reference letter is used as a baseline for the proof of residence of tenant. It can be used for opening bank account, for getting the proof document, for passport or any other areas where temporary address proof is needed.
Memory and forgetting
The presentation is prepared according to the syllabus of Basic BSc nursing given by INC. for the better understanding and knowledge please refer the books
Learning
The presentation is prepared according to the syllabus of Basic BSc nursing given by INC. for the better understanding and knowledge please refer the books. the learning is the information gaining process where the individual interact with the environment and gain knowledge.
Personality
This presentation is prepared according to the syllabus of Basic BSc nursing students given by INC. for the better learning and knowledge please refer the books.
Attitude
The presentation is prepared according to the syllabus of INC for the Basic BSc nursing. presentation is a brief information for the students so for better knowledge please refer the books.
Attention and perception
1. Attention is the concentration of awareness on some phenomenon to the exclusion of other stimuli and determines consciousness. It can be voluntary, requiring conscious effort, or involuntary, arising without effort.
2. Factors that influence attention include the nature, intensity, size, contrast, location, repetition, motion, and form of stimuli as well as an individual's interests, motives, mental set, past experiences, emotions, and habits.
3. The span of attention refers to how long stimuli can be focused on before a break is needed. Visual attention span is brief while auditory span is slightly longer, especially with rhythm. Sustained attention maintains continuous concentration on a subject.
More from Sushil Humane (20)
Recently uploaded
Light House Retreats: Plant Medicine Retreat Europe
Our aim is to organise conscious gatherings and retreats for open and inquisitive minds and souls, with and without the assistance of sacred plants.
Integrating Ayurveda into Parkinson’s Management: A Holistic Approach
Explore the benefits of combining Ayurveda with conventional Parkinson's treatments. Learn how a holistic approach can manage symptoms, enhance well-being, and balance body energies. Discover the steps to safely integrate Ayurvedic practices into your Parkinson’s care plan, including expert guidance on diet, herbal remedies, and lifestyle modifications.
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Preoperative Management of Patients on GLP-1 Receptor Agonists like Ozempic and Semiglutide
ASA GUIDELINE
NYSORA Guideline
2 Case Reports of Gastric Ultrasound
How STIs Influence the Development of Pelvic Inflammatory Disease.pptx
STIs may cause PID. For the both disease, herbal medicine Fuyan Pill can be a solution.
micro teaching on communication m.sc nursing.pdf
Microteaching is a unique model of practice teaching. It is a viable instrument for the. desired change in the teaching behavior or the behavior potential which, in specified types of real. classroom situations, tends to facilitate the achievement of specified types of objectives.
Hemodialysis: Chapter 4, Dialysate Circuit - Dr.Gawad
- Video recording of this lecture in English language: https://youtu.be/kqbnxVAZs-0
- Video recording of this lecture in Arabic language: https://youtu.be/SINlygW1Mpc
- Link to download the book free: https://nephrotube.blogspot.com/p/nephrotube-nephrology-books.html
- Link to NephroTube website: www.NephroTube.com
- Link to NephroTube social media accounts: https://nephrotube.blogspot.com/p/join-nephrotube-on-social-media.html
Efficacy of Avartana Sneha in Ayurveda
Avartana Sneha is a unique method of Preparation of Sneha Kalpana in Ayurveda, mainly it is indicated for the Vataja rogas.
ANATOMY AND PHYSIOLOGY OF URINARY SYSTEM.pptx
Valuable Content of Human Anatomy and Physiology of Urinary system as per PCI Syllabus for Pharmacy and PharmD Students.
Journal Article Review on Rasamanikya
Rasamanikya is a excellent preparation in the field of Rasashastra, it is used in various Kushtha Roga, Shwasa, Vicharchika, Bhagandara, Vatarakta, and Phiranga Roga. In this article Preparation& Comparative analytical profile for both Formulationon i.e Rasamanikya prepared by Kushmanda swarasa & Churnodhaka Shodita Haratala. The study aims to provide insights into the comparative efficacy and analytical aspects of these formulations for enhanced therapeutic outcomes.
Best Ayurvedic medicine for Gas and Indigestion
Here is the updated list of Top Best Ayurvedic medicine for Gas and Indigestion and those are Gas-O-Go Syp for Dyspepsia | Lavizyme Syrup for Acidity | Yumzyme Hepatoprotective Capsules etc
ARTHROLOGY PPT NCISM SYLLABUS AYURVEDA STUDENTS
PPT RELATED TO ARTHROLOGY ACCORDING TO NCISM AYURVEDA
Knee anatomy and clinical tests 2024.pdf
This includes all relevant anatomy and clinical tests compiled from standard textbooks, Campbell,netter etc..It is comprehensive and best suited for orthopaedicians and orthopaedic residents.
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
Novas diretrizes da OMS para os cuidados perinatais de mais qualidadeProf. Marcus Renato de Carvalho
Recomendações da OMS sobre cuidados maternos e neonatais para uma experiência pós-natal positiva.
Em consonância com os ODS – Objetivos do Desenvolvimento Sustentável e a Estratégia Global para a Saúde das Mulheres, Crianças e Adolescentes, e aplicando uma abordagem baseada nos direitos humanos, os esforços de cuidados pós-natais devem expandir-se para além da cobertura e da simples sobrevivência, de modo a incluir cuidados de qualidade.
Estas diretrizes visam melhorar a qualidade dos cuidados pós-natais essenciais e de rotina prestados às mulheres e aos recém-nascidos, com o objetivo final de melhorar a saúde e o bem-estar materno e neonatal.
Uma “experiência pós-natal positiva” é um resultado importante para todas as mulheres que dão à luz e para os seus recém-nascidos, estabelecendo as bases para a melhoria da saúde e do bem-estar a curto e longo prazo. Uma experiência pós-natal positiva é definida como aquela em que as mulheres, pessoas que gestam, os recém-nascidos, os casais, os pais, os cuidadores e as famílias recebem informação consistente, garantia e apoio de profissionais de saúde motivados; e onde um sistema de saúde flexível e com recursos reconheça as necessidades das mulheres e dos bebês e respeite o seu contexto cultural.
Estas diretrizes consolidadas apresentam algumas recomendações novas e já bem fundamentadas sobre cuidados pós-natais de rotina para mulheres e neonatos que recebem cuidados no pós-parto em unidades de saúde ou na comunidade, independentemente dos recursos disponíveis.
É fornecido um conjunto abrangente de recomendações para cuidados durante o período puerperal, com ênfase nos cuidados essenciais que todas as mulheres e recém-nascidos devem receber, e com a devida atenção à qualidade dos cuidados; isto é, a entrega e a experiência do cuidado recebido. Estas diretrizes atualizam e ampliam as recomendações da OMS de 2014 sobre cuidados pós-natais da mãe e do recém-nascido e complementam as atuais diretrizes da OMS sobre a gestão de complicações pós-natais.
O estabelecimento da amamentação e o manejo das principais intercorrências é contemplada.
Recomendamos muito.
Vamos discutir essas recomendações no nosso curso de pós-graduação em Aleitamento no Instituto Ciclos.
Esta publicação só está disponível em inglês até o momento.
Prof. Marcus Renato de Carvalho
www.agostodourado.com
Adhd Medication Shortage Uk - trinexpharmacy.com
The UK is currently facing a Adhd Medication Shortage Uk, which has left many patients and their families grappling with uncertainty and frustration. ADHD, or Attention Deficit Hyperactivity Disorder, is a chronic condition that requires consistent medication to manage effectively. This shortage has highlighted the critical role these medications play in the daily lives of those affected by ADHD. Contact : +1 (747) 209 – 3649 E-mail : sales@trinexpharmacy.com
Recently uploaded (20)
Thyroid Gland- Gross Anatomy by Dr. Rabia Inam Gandapore.pptx
Thyroid Gland- Gross Anatomy by Dr. Rabia Inam Gandapore.pptx
Light House Retreats: Plant Medicine Retreat Europe
Light House Retreats: Plant Medicine Retreat Europe
Integrating Ayurveda into Parkinson’s Management: A Holistic Approach
Integrating Ayurveda into Parkinson’s Management: A Holistic Approach
CHEMOTHERAPY_RDP_CHAPTER 6_Anti Malarial Drugs.pdf
CHEMOTHERAPY_RDP_CHAPTER 6_Anti Malarial Drugs.pdf
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
Ozempic: Preoperative Management of Patients on GLP-1 Receptor Agonists
How STIs Influence the Development of Pelvic Inflammatory Disease.pptx
How STIs Influence the Development of Pelvic Inflammatory Disease.pptx
Hemodialysis: Chapter 4, Dialysate Circuit - Dr.Gawad
Hemodialysis: Chapter 4, Dialysate Circuit - Dr.Gawad
Vestibulocochlear Nerve by Dr. Rabia Inam Gandapore.pptx
Vestibulocochlear Nerve by Dr. Rabia Inam Gandapore.pptx
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
Novas diretrizes da OMS para os cuidados perinatais de mais qualidade
Analysis of data
- 1. ANALYSIS OF DATA Mr. Sushil Humane MSN, RN
- 2. Introduction • Analysis is the process of organization and synthesizing the data so as to answer research questions and test hypothesis
- 3. Analysis of quantitative data • Data preparation • Describing the data • Drawing the inference of data • Interpretation of data
- 4. Data preparation • Cleaning and organizing data for analysis: checking for correctness, entering into computer, transforming and documenting. • It involves the following steps • Compilation: gathering together all the collected data in order • Editing: Checked for accuracy, utility and completeness. • Coding: convert data in symbolic form for computer assisted analysis. • Classification: Geographic, chronological, Qualitative and Quantitively classification.
- 5. Data preparation • Tabulation: orderly arrangement of data in rows and columns • Tabulation forms: frequency distribution, contingency table, multiple response tables, miscellaneous table.
- 6. Describing the data • Used to organize and summaries the data to draw meaningful interpretation.it involves • Measure to condense data: frequency and percentage distribution through tabulation and graphic presentation. • Measure of central tendency: • Measure of dispersion • Measure of relationship: correlation coefficient
- 7. References • Suresh k. sharma. nursing research and statistics. 2nd edition. • Polit, D.F. & Beck, C.T. (2017). Nursing research: Generating and assessing evidence for nursing practice.