This document provides an overview of qualitative data analysis. It discusses that qualitative data analysis involves coding, categorizing, comparing and interpreting collected data to find meanings and implications. The researcher's perspective influences the analysis. It also describes techniques for qualitative data analysis like becoming familiar with the data, providing in-depth descriptions, and categorizing data into themes. Ensuring credibility involves considering factors like the researcher's observations and biases. The document also contrasts qualitative data analysis with quantitative analysis.
Qualitative data analysis involves analyzing words, observations, images and symbols to answer research questions. There are several common methods for analyzing qualitative data, including content analysis, narrative analysis, framework analysis, and discourse analysis. These methods involve coding the data by categorizing words and phrases into themes, then identifying patterns and relationships between themes to summarize the findings.
The document discusses various methods for analyzing and interpreting data. It describes descriptive analysis which helps summarize data patterns. Statistical analysis techniques like clustering, regression, and cohorts are explained. Inferential analysis makes judgments about differences between groups. Qualitative and quantitative methods are outlined for interpreting data through coding and establishing relationships. The purpose of data analysis and interpretation is to answer research questions and determine trends to support decision making.
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
This document provides an introduction to descriptive statistics and measures of condensation. It defines key concepts including data, variables, descriptive versus inferential statistics, and different types of data such as nominal, ordinal, discrete, and continuous. It also discusses frequency distribution and different ways of presenting data through tables, charts and graphs. The goal of descriptive statistics and measures of condensation is to summarize and organize large datasets in a concise and meaningful way.
Introduction of statistics and probabilityBencentapleras
This document discusses key concepts in statistics including collecting, organizing, and analyzing quantitative and qualitative data. It defines common statistical terminology like nominal, ordinal, interval, and ratio scales of measurement. Descriptive and inferential statistics are compared, where descriptive statistics summarize data and inferential statistics are used to make generalizations from a sample to a population. Common descriptive measures like mean, median, and mode are also defined.
The document discusses data analysis and interpretation. It describes the different scales of measurement used in data analysis including nominal, ordinal, interval, and ratio scales. It also discusses various methods used for interpreting qualitative and quantitative data, such as using statistical techniques like mean and standard deviation for quantitative data. Finally, it covers different visualization techniques used in data interpretation like bar graphs, pie charts, tables, and line graphs.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
Qualitative data analysis involves analyzing words, observations, images and symbols to answer research questions. There are several common methods for analyzing qualitative data, including content analysis, narrative analysis, framework analysis, and discourse analysis. These methods involve coding the data by categorizing words and phrases into themes, then identifying patterns and relationships between themes to summarize the findings.
The document discusses various methods for analyzing and interpreting data. It describes descriptive analysis which helps summarize data patterns. Statistical analysis techniques like clustering, regression, and cohorts are explained. Inferential analysis makes judgments about differences between groups. Qualitative and quantitative methods are outlined for interpreting data through coding and establishing relationships. The purpose of data analysis and interpretation is to answer research questions and determine trends to support decision making.
Need a nonplagiarised paper and a form completed by 1006015 before.docxlea6nklmattu
Need a nonplagiarised paper and a form completed by 10/06/015 before 7:00pm. I have attached the documents along the rubics that must be followed.
Coyne and Messina Articles, Part 2 Statistical Assessment
Details:
1) Write a paper of 1,000-1,250 words regarding the statistical significance of outcomes as presented in Messina's, et al. article "The Relationship between Patient Satisfaction and Inpatient Admissions Across Teaching and Nonteaching Hospitals."
2) Assess the appropriateness of the statistics used by referring to the chart presented in the Module 4 lecture and the resource "Statistical Assessment."
3) Discuss the value of statistical significance vs. pragmatic usefulness.
4) Prepare this assignment according to the APA guidelines found in the APA Style Guide located in the Student Success Center. An abstract is not required.
5) This assignment uses a grading rubric. Instructors will be using the rubric to grade the assignment; therefore, students should review the rubric prior to beginning the assignment to become familiar with the assignment criteria and expectations for successful completion of the assignment.
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
·
The research question itself
·
The sample size
·
The type of data you have collected
·
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generaliza.
This document provides an introduction to descriptive statistics and measures of condensation. It defines key concepts including data, variables, descriptive versus inferential statistics, and different types of data such as nominal, ordinal, discrete, and continuous. It also discusses frequency distribution and different ways of presenting data through tables, charts and graphs. The goal of descriptive statistics and measures of condensation is to summarize and organize large datasets in a concise and meaningful way.
Introduction of statistics and probabilityBencentapleras
This document discusses key concepts in statistics including collecting, organizing, and analyzing quantitative and qualitative data. It defines common statistical terminology like nominal, ordinal, interval, and ratio scales of measurement. Descriptive and inferential statistics are compared, where descriptive statistics summarize data and inferential statistics are used to make generalizations from a sample to a population. Common descriptive measures like mean, median, and mode are also defined.
The document discusses data analysis and interpretation. It describes the different scales of measurement used in data analysis including nominal, ordinal, interval, and ratio scales. It also discusses various methods used for interpreting qualitative and quantitative data, such as using statistical techniques like mean and standard deviation for quantitative data. Finally, it covers different visualization techniques used in data interpretation like bar graphs, pie charts, tables, and line graphs.
The document summarizes key concepts from Chapter 1 of the textbook "Elementary Statistics" including:
- The difference between a population and a sample, and how statistics uses samples to make inferences about populations.
- The different types of data: quantitative, categorical, discrete vs. continuous data.
- The different levels of measurement for data: nominal, ordinal, interval, and ratio.
- The importance of critical thinking when analyzing data and statistics, including considering context, sources, sampling methods, and avoiding misleading graphs, samples, conclusions, or survey questions.
This document provides an overview of key concepts from Chapter 1 of the textbook "Elementary Statistics". It defines important statistical terms like population, sample, parameter, and statistic. It also distinguishes between different types of data and levels of measurement. Additionally, it discusses the importance of collecting sample data through appropriate random sampling methods. Critical thinking in statistics is emphasized, highlighting factors like the context, source, and sampling method of data when evaluating statistical claims. Different ways of collecting data through studies and experiments are also introduced.
This document provides information on using SPSS for educational research. It discusses descriptive statistics, common statistical issues in research, procedures for creating a SPSS data file and conducting descriptive analyses. It also explains how to perform t-tests, analysis of variance (ANOVA), frequencies analysis and other statistical tests in SPSS. The document is intended as a guide for researchers on applying various statistical analyses in SPSS.
Data analysis involves understanding known facts or assumptions to draw conclusions about research questions. There are two main types of data analysis: qualitative and quantitative. Qualitative analysis examines subjective data like thoughts, feelings, and attitudes expressed in words, collected through interviews and observations. Quantitative analysis deals with numerical data, using statistical techniques to summarize relationships between variables. Both types of analysis require coding, organizing, and interpreting large amounts of data to understand the relevant information.
This document provides an overview of data analysis using SPSS. It discusses key concepts like variables, measurement scales, data types, statistical terminology, and the steps involved in data analysis using SPSS. The document defines nominal, ordinal, interval and ratio scales of measurement. It also describes the nature of data as categorical or metric, and the types of categorical and metric data. Furthermore, it outlines tasks like data preparation, coding, cleaning and the appropriate use of statistical tools for analysis in SPSS.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis
Practical applications and analysis in Research Methodology Hafsa Ranjha
Lara expresses concern about the low numbers of Latinos attending and graduating from higher education. She identifies four factors that may influence Latino students' college success based on research: family, religion, social support, and motivation. She ties these factors to theories of child development and ethnic identity. For her dissertation, Lara uses a mixed-methods explanatory design with two phases - a quantitative questionnaire followed by qualitative interviews - to examine how these factors influence college success among Mexican American students. She analyzes and interprets the data using statistical tools and software.
This document differentiates between qualitative and quantitative research. Qualitative research relies on non-numerical personal accounts and observations to understand how people think, while quantitative research uses measurable data and statistical analysis to test relationships. Some key differences are that qualitative research uses interviews, documents and observations to collect open-ended data, while quantitative relies on experiments, surveys and databases to collect standardized data suitable for numerical analysis. Both approaches have benefits and limitations for addressing different types of research questions.
This document discusses data in research methodology. It covers different types of data including qualitative and quantitative data. It describes various methods of collecting primary data such as observation, interviews, questionnaires, and schedules. It also discusses methods of collecting secondary data from published and unpublished sources. The importance of data collection in research is explained as well as factors to consider such as the object and scope of inquiry. Measurement scales including nominal, ordinal, interval, and ratio scales are outlined.
The document contains an outline of the table of contents for a textbook on general statistics. It covers topics such as preliminary concepts, data collection and presentation, measures of central tendency, measures of dispersion and skewness, and permutations and combinations. Sample chapters discuss introduction to statistics, variables and data, methods of presenting data through tables, graphs and diagrams, computing the mean, median and mode, and other statistical measures.
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
Biostatistics involves using statistical methods and techniques to analyze medical data. This includes collecting accurate data from patients regarding clinical characteristics, outcomes, and more. The data must then be prepared, coded, and cleaned before being analyzed. Statistical Package for Social Sciences (SPSS) is a commonly used software that allows importing data files and performing both descriptive and inferential statistical analyses. Descriptive analyses may include graphs, frequency tables, measures of central tendency, and variability to summarize categorical and continuous variables both individually and in relation to each other.
This document discusses various techniques for analyzing quantitative and qualitative data. It describes editing, coding, classification, and tabulation as methods for processing qualitative data. For quantitative data, it covers univariate analyses like measures of central tendency and dispersion. It also discusses bivariate analyses like correlation and regression, as well as multivariate techniques including multidimensional analysis, factor analysis, and cluster analysis. The goal of data analysis is to discover useful information and support decision making.
This document provides an introduction to statistics and biostatistics. It discusses what statistics and biostatistics are, their uses, and what they cover. Specifically, it explains that biostatistics applies statistical methods to biological and medical data. It also discusses different types of data, variables, coding data, and strategies for describing data, including tables, diagrams, frequency distributions, and numerical measures. Graphs and charts discussed include bar charts, pie charts, histograms, scatter plots, box plots, and stem-and-leaf plots. The document provides examples and illustrations of these concepts and techniques.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- Descriptive statistics focus on central tendency, variability, and distribution of data. Inferential statistics allow statisticians to draw conclusions about populations based on samples and determine the reliability of those conclusions.
- Statistics rely on variables, which are characteristics or attributes that can be measured and analyzed. Variables can be qualitative like gender or quantitative like mileage, and quantitative variables can be discrete like test scores or continuous like height.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- The two major areas of statistics are descriptive statistics, which summarizes data, and inferential statistics, which uses descriptive statistics to make generalizations and predictions.
- Mean, median, and mode describe central tendency, with mean being the average, median being the middle number, and mode being the most frequent value.
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
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
This document discusses different approaches to analyzing qualitative and quantitative data from research. It addresses questions like what types of data are common, how to find meanings and patterns, and how to display results effectively. The document provides an overview of quantitative data analysis methods like statistical tests and summarizing data in tables and charts. It also discusses qualitative data analysis, including reducing and organizing text data, coding, conceptualizing, and interpreting meanings. The goal is to help researchers choose appropriate analysis methods based on their research questions, methodological approach, and type of data collected.
Explore the key differences between silicone sponge rubber and foam rubber in this comprehensive presentation. Learn about their unique properties, manufacturing processes, and applications across various industries. Discover how each material performs in terms of temperature resistance, chemical resistance, and cost-effectiveness. Gain insights from real-world case studies and make informed decisions for your projects.
This document provides information on using SPSS for educational research. It discusses descriptive statistics, common statistical issues in research, procedures for creating a SPSS data file and conducting descriptive analyses. It also explains how to perform t-tests, analysis of variance (ANOVA), frequencies analysis and other statistical tests in SPSS. The document is intended as a guide for researchers on applying various statistical analyses in SPSS.
Data analysis involves understanding known facts or assumptions to draw conclusions about research questions. There are two main types of data analysis: qualitative and quantitative. Qualitative analysis examines subjective data like thoughts, feelings, and attitudes expressed in words, collected through interviews and observations. Quantitative analysis deals with numerical data, using statistical techniques to summarize relationships between variables. Both types of analysis require coding, organizing, and interpreting large amounts of data to understand the relevant information.
This document provides an overview of data analysis using SPSS. It discusses key concepts like variables, measurement scales, data types, statistical terminology, and the steps involved in data analysis using SPSS. The document defines nominal, ordinal, interval and ratio scales of measurement. It also describes the nature of data as categorical or metric, and the types of categorical and metric data. Furthermore, it outlines tasks like data preparation, coding, cleaning and the appropriate use of statistical tools for analysis in SPSS.
This document summarizes key concepts from an introduction to statistics textbook. It covers types of data (quantitative, qualitative, levels of measurement), sampling (population, sample, randomization), experimental design (observational studies, experiments, controlling variables), and potential misuses of statistics (bad samples, misleading graphs, distorted percentages). The goal is to illustrate how common sense is needed to properly interpret data and statistics.
Data Presentation & Analysis Meaning, Stages of data analysis, Quantitative & Qualitative data analysis methods, Descriptive & inferential methods of data analysis
Practical applications and analysis in Research Methodology Hafsa Ranjha
Lara expresses concern about the low numbers of Latinos attending and graduating from higher education. She identifies four factors that may influence Latino students' college success based on research: family, religion, social support, and motivation. She ties these factors to theories of child development and ethnic identity. For her dissertation, Lara uses a mixed-methods explanatory design with two phases - a quantitative questionnaire followed by qualitative interviews - to examine how these factors influence college success among Mexican American students. She analyzes and interprets the data using statistical tools and software.
This document differentiates between qualitative and quantitative research. Qualitative research relies on non-numerical personal accounts and observations to understand how people think, while quantitative research uses measurable data and statistical analysis to test relationships. Some key differences are that qualitative research uses interviews, documents and observations to collect open-ended data, while quantitative relies on experiments, surveys and databases to collect standardized data suitable for numerical analysis. Both approaches have benefits and limitations for addressing different types of research questions.
This document discusses data in research methodology. It covers different types of data including qualitative and quantitative data. It describes various methods of collecting primary data such as observation, interviews, questionnaires, and schedules. It also discusses methods of collecting secondary data from published and unpublished sources. The importance of data collection in research is explained as well as factors to consider such as the object and scope of inquiry. Measurement scales including nominal, ordinal, interval, and ratio scales are outlined.
The document contains an outline of the table of contents for a textbook on general statistics. It covers topics such as preliminary concepts, data collection and presentation, measures of central tendency, measures of dispersion and skewness, and permutations and combinations. Sample chapters discuss introduction to statistics, variables and data, methods of presenting data through tables, graphs and diagrams, computing the mean, median and mode, and other statistical measures.
Statistics What you Need to KnowIntroductionOften, when peop.docxdessiechisomjj4
Statistics: What you Need to Know
Introduction
Often, when people begin a statistics course, they worry about doing advanced mathematics or their math phobias kick in. Understanding that statistics as addressed in this course is not a math course at all is important. The only math you will do is addition, subtraction, multiplication, and division. In these days of computer capability, you generally don't even have to do that much, since Excel is set up to do basic statistics for you. The key elements for the student in this course is to understand the various types of statistics, what their requirements are, what they do, and how you can use and interpret the results. Referring back to the basic components of a valid research study, which statistic a researcher uses depends on several things:
The research question itself
The sample size
The type of data you have collected
The type of statistic called for by the design
All quantitative studies require a data set. Qualitative studies may use a data set or may use observations with no numerical data at all. For the purposes of the next modules, our focus will be on quantitative studies.
Types of Statistics
There are several types of statistics available to the researcher. Descriptive statistics provide a basic description of the data set. This includes the measures of central tendency: means, medians, and modes, and the measures of dispersion, including variances and standard deviations. Descriptive statistics also include the sample size, or "N", and the frequency with which each data point occurs in the data set.
Inferential statistics allow the researcher to make predictions, estimations, and generalizations about the data set, the sample, and the population from which the sample was drawn. They allow you to draw inferences, generalizations, and possibilities regarding the relationship between the independent variable and the dependent variable to indicate how those inferences answer the research question. Researchers can make predictions and estimations about how the results will fit the overall population. Statistics can also be described in terms of the types of data they can analyze. Non-parametric statistics can be used with nominal or ordinal data, while parametric statistics can be used with interval and ratio data types.
Types of Data
There are four types of data that a researcher may collect.
Nominal Data Sets
The Nominal data set includes simple classifications of data into categories which are all of equal weight and value. Examples of categories that are equal to each other include gender (male, female), state of birth (Arizona, Wyoming, etc.), membership in a group (yes, no). Each of these categories is equivalent to the other, without value judgments.
Ordinal Data Sets
Ordinal data sets also have data classified into categories, but these categories have some form or order or ranking attached, often of some sort of value / val.
Biostatistics involves using statistical methods and techniques to analyze medical data. This includes collecting accurate data from patients regarding clinical characteristics, outcomes, and more. The data must then be prepared, coded, and cleaned before being analyzed. Statistical Package for Social Sciences (SPSS) is a commonly used software that allows importing data files and performing both descriptive and inferential statistical analyses. Descriptive analyses may include graphs, frequency tables, measures of central tendency, and variability to summarize categorical and continuous variables both individually and in relation to each other.
This document discusses various techniques for analyzing quantitative and qualitative data. It describes editing, coding, classification, and tabulation as methods for processing qualitative data. For quantitative data, it covers univariate analyses like measures of central tendency and dispersion. It also discusses bivariate analyses like correlation and regression, as well as multivariate techniques including multidimensional analysis, factor analysis, and cluster analysis. The goal of data analysis is to discover useful information and support decision making.
This document provides an introduction to statistics and biostatistics. It discusses what statistics and biostatistics are, their uses, and what they cover. Specifically, it explains that biostatistics applies statistical methods to biological and medical data. It also discusses different types of data, variables, coding data, and strategies for describing data, including tables, diagrams, frequency distributions, and numerical measures. Graphs and charts discussed include bar charts, pie charts, histograms, scatter plots, box plots, and stem-and-leaf plots. The document provides examples and illustrations of these concepts and techniques.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- Descriptive statistics focus on central tendency, variability, and distribution of data. Inferential statistics allow statisticians to draw conclusions about populations based on samples and determine the reliability of those conclusions.
- Statistics rely on variables, which are characteristics or attributes that can be measured and analyzed. Variables can be qualitative like gender or quantitative like mileage, and quantitative variables can be discrete like test scores or continuous like height.
- Descriptive statistics describe the properties of sample and population data through metrics like mean, median, mode, variance, and standard deviation. Inferential statistics use those properties to test hypotheses and draw conclusions about large groups.
- The two major areas of statistics are descriptive statistics, which summarizes data, and inferential statistics, which uses descriptive statistics to make generalizations and predictions.
- Mean, median, and mode describe central tendency, with mean being the average, median being the middle number, and mode being the most frequent value.
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
Assignment 2 RA Annotated BibliographyIn your final paper for .docxjosephinepaterson7611
This document provides information about descriptive statistics and how to calculate various descriptive statistics measures. It defines four types of measurement data: nominal, ordinal, interval, and ratio data. It then explains how to calculate and interpret the mean, median, mode, variability measures including range, variance and standard deviation. Examples are provided to demonstrate calculating these descriptive statistics on sets of sample data. The document emphasizes that descriptive statistics alone cannot be used to draw conclusions, but rather just describe patterns in the data.
The document discusses different types of data that can be collected in statistics including categorical vs. quantitative data, discrete vs. continuous data, and different levels of measurement for data including nominal, ordinal, interval, and ratio scales. It also discusses key concepts such as parameters, statistics, populations, and samples. Potential pitfalls in statistical analysis are outlined such as misleading conclusions, nonresponse bias, and issues with survey question wording and order.
This document discusses different approaches to analyzing qualitative and quantitative data from research. It addresses questions like what types of data are common, how to find meanings and patterns, and how to display results effectively. The document provides an overview of quantitative data analysis methods like statistical tests and summarizing data in tables and charts. It also discusses qualitative data analysis, including reducing and organizing text data, coding, conceptualizing, and interpreting meanings. The goal is to help researchers choose appropriate analysis methods based on their research questions, methodological approach, and type of data collected.
Explore the key differences between silicone sponge rubber and foam rubber in this comprehensive presentation. Learn about their unique properties, manufacturing processes, and applications across various industries. Discover how each material performs in terms of temperature resistance, chemical resistance, and cost-effectiveness. Gain insights from real-world case studies and make informed decisions for your projects.
1. Chapter Six: Data Analysis & Interpretation
Once the data are collected, the next logical step of the
research process is data analysis.
Data Analysis is the process of systematically applying
statistical and/or logical techniques to describe and
illustrate, condense and recap, and evaluate the data.
Data Analysis is the process of evaluating data using
analytical and logical reasoning to examine each
component of the data collected.
Data analysis is the science of examining raw data with
the purpose of drawing conclusions about that
information.
2. Con’t
Data Analysis is an attempt by the researcher
to summarize collected data where as data
Interpretation is an attempt to find
meaning/Implication of the result.
Depending on the nature of data collected,
the analysis can be either qualitative or
quantitative analysis.
3. Qualitative Data Analysis
Qualitative research is used to describe behaviors,
actions, feelings, perceptions, and interaction among
people.
It assumes that respondents or people observed have
unique views of their personal experiences or the
surrounding environment.
It is used to help us understand lifestyles and cultural
values, actions, and symbols and it heavily rely on
verbal description.
The main instrument of data collection, interpretation
and written explanation is the researcher him
/herself.
4. Qualitative Data Analysis
Qualitative data analysis is mostly used to:
Develop an understanding of people or groups
that we know very little about.
Develop new theories that are relevant to
women, people of color, and other groups in
society that might have been excluded from
previous studies.
For example, qualitative research can be used to
understand the lives of Women, Low income
communities, people with HIV Aids and so on
5. Qualitative Data Analysis
Qualitative researches are usually more tentative
than quantitative ones, mainly because it is expected
that a qualitative study will evolve (change) in focus
once the researcher is in the research setting.
The Data Collection process is not an end in itself.
The culminating activities of qualitative inquiry are
not data collection but analysis, interpretation, and
presentation of findings.
Qualitative Data Collection and Analysis is more
common in exploratory type of research that looks at
a situation about which little is known.
6. Qualitative Data Analysis
The credibility of qualitative analysis depends on two distinct
but related inquiry elements:
1 Rigorous techniques and methods for gathering high-quality
data that is carefully analysed, with attention to issues of
validity, reliability, and triangulation;
2 The credibility of the researcher, which is dependent on
training, experience, track record, status, and presentation of
self; the extent to which the researcher influences responses is
significant in qualitative research and analysis.
Analysis and Interpretation depends on the perspective
of the researcher. Why?
The qualitative data analysis involves coding, categorisation,
abstraction, comparison, integration, interpretation,
explanation, description.
7. Qualitative Data analysis
In terms of research tradition qualitative data analysis is
different from quantitative in that data analysis in
qualitative research is undertaken during and after data
collection. That means analysis not left until the end of
data collection.
As they collect data the researcher must ask
Why do the participants act as they do?
What does this focus mean?
What else do I want to know?
What new ideas have emerged?
Is this new information?
8. Qualitative Data Analysis
Data Analysis After Collection
One way is to follow three important steps
1. Become familiar with the data through reading
2. Exam the data in depth to provide detailed
descriptions of the setting, participants, and
activities.
3. Categorizing and coding pieces of data and
grouping them into themes. coding qualitative
data helps to reduce data to a manageable form.
9. Qualitative Data Analysis
Useful techniques for qualitative data analysis & interpretation
Extend the analysis by raising questions
Connect findings to personal experiences
Seek the advice of “critical” friends.
Contextualize findings in the research
Turn to theory
The researcher shall answer these four questions while
analyzing and interpreting qualitative data
What is important in the data?
Why is it important?
What can be learned from it?
So what?
10. Ensuring Credibility of Qualitative
Data Analysis and Interpretation
To ensure Credibility of the analysis and interpretation of
qualitative data the following shall be adequately answered.
Are the data based on one’s own observation?
Is there corroboration by other’s of the observation?
In what circumstances was an observation made or reported?
How reliable are those providing the data?
What motivations might have influenced a participant’s
report?
What biases might have influenced how an observation was
made or reported?
11. Exercise
Focus Groups discussion: Form a group of 4
people. One person should be the
interviewer. Another should take notes.
Address the following three questions:
1. What are the best things about Queens’ College?
2. What are the worst things about Queens’ College?
3. How do you think Queens’ College could be
improved?
12. Quantitative Data Analysis
Before we can do any kind of analysis, we
need to quantify our data.
“Quantification” is the process of
converting data to a numeric format.
13. Quantitative Data Analysis
Quantitative data analysis Qualitative data analysis
Data reduced to numeric values Data reduced to codes
Primarily rely on research procedures Primarily rely on researcher himself.
Preference for statistical summary
results
Preference for narrative summary
results
Preference for developing hypothesis
at the outset and statistically testing it
Preference to set hypothesis as study
develops and preference for
logical/narrative description.
Preference for random sampling Preference for expert informant
purposive sampling
14. Data Analysis Tools
Statistical Package for Social Sciences (SPSS)
STATA
Eviews
15. Statistical Package for Social Sciences (SPSS)
SPSS-format data files are organized by cases
(rows) and variables (columns). There are two
viewes-varaible view and data view
• In Variable View, each row is a variable, and each
column is an attribute that is associated with that
variable-it contains ten columns
Name Type Width Decimal Label Value missing Column Align Measure
16. SPSS
In Data view: columns represent variables,
and rows represent cases (observations).
Variables are used to represent the different
types of data that you have compiled.
The response to each question on a survey is
equivalent to a variable.
Variables come in many different types,
including numbers, strings, currency, and
dates
17. SPSS
Data can be entered into the Data Editor,
which may be useful for small data files or for
making minor edits to larger data files
Click the Variable View tab at the bottom of
the Data Editor window.
You need to define the variables that will be
used.
Fore example variables like age, marital
status, and income
18. SPSS
For instance:
In the first row of the first column, type age.
In the second row, type marital.
In the third row, type income.
These variables are automatically given a Numeric
data type. You can also choose other data type
depending on the type of variable
Click the Data View tab to continue entering the data
for the variables.
The names that you entered in Variable View are
now the headings for the first three columns in Data
View.
19. SPSS
Begin entering data in the first row, starting
at the first column-In the age column, type 55,
In the marital column, type 1, In the income
column, type 2000.
Move the cursor to the second row of the first
column to add the next subject's data
In the age column, type 53, In the marital
column, type 0, In the income column, type
3000.
20. SPSS
Currently, the age and marital columns display
decimal points, even though their values are
intended to be integers. To hide the decimal points in
these variables:
►Click the Variable View tab at the bottom of the
Data Editor window.
In the Decimals column of the age row, type 0 to
hide the decimal.
In the Decimals column of the marital row, type 0 to
hide the decimal
21. SPSS
Non-numeric data, such as strings of text, can also be
entered into the Data Editor.
Click the Variable View tab at the bottom of the Data
Editor window.
In the first cell of the first empty row, type sex for the
variable name.
Click the Type cell next to your entry.
Click the button on the right side of the Type cell to
open the Variable Type dialog box
Select String to specify the variable type-Click OK to
save your selection and return to the Data Editor.
22. SPSS
In addition to defining data types, you can also
define descriptive variable labels and value labels for
variable names and data values
These descriptive labels are used in statistical reports
and charts.
Labels are meant to provide descriptions of
variables. These descriptions are often longer
versions of variable names.
These labels are used in your output to identify the
different variables
23. SPSS
In the Label column of the age row, type
Respondent's Age.
In the Label column of the marital row, type Marital
Status
In the Label column of the income row, type
Household Income.
In the Label column of the sex row, type Gender.
The Type column displays the current data type for
each variable. The most common data types are
numeric and string, but many other formats are
supported.
24. SPSS
In the current data file, the income variable is
defined as a numeric type.
Click the Type cell for the income row, and
then click the button on the right side of the
cell to open the Variable Type dialog box and
select Dollar.
The formatting options for the currently
selected data type are displayed.
Click OK to save your changes.
25. SPSS
Value labels provide a method for mapping
your variable values to a string label. In this
example, there are two acceptable values for
the marital variable.
A value of 0 means that the subject is single,
and a value of 1 means that he or she is
married
Click the Values cell for the marital row, and
then click the button on the right side of the
cell to open the Value Labels dialog box.
26. SPSS
The value is the actual numeric value.
The value label is the string label that is applied to
the specified numeric value.
Type 0 in the Value field.
Type Single in the Label field.
Click Add to add this label to the list.
Type 1 in the Value field, and type Married in the
Label field.
Click Add, and then click OK to save your changes
and return to the Data Editor
These labels can also be displayed in Data View,
which can make your data more readable.
27. SPSS
Click the Data View tab at the bottom of
the Data Editor window.
From the menus choose:
View
Value Labels
28. SPSS
Missing or invalid data are generally too
common to ignore. Survey respondents may
refuse to answer certain questions, may not
know the answer, or may answer in an
unexpected format
If you don't filter or identify these data, your
analysis may not provide accurate results.