A sample of the slides available to support the teaching of the textbook Statistics for Geography and Environmental Science by Harris & Jarvis (2011). For further information see www.social-statistics.org
Statistics for Geography and Environmental Science:an introductory lecture c...Rich Harris
A sample of the instructor's resources to support the textbook Statistics for Geography and Environmental Science. Further information at www.social-statistics.org
This document provides a lecture note on statistics for physical sciences and engineering. It begins with an introduction to statistics and its importance in various fields such as physical sciences, engineering, and research. It then discusses descriptive and inferential statistics. The document also covers topics such as data collection methods, presentation of data through tables and diagrams, and some basic statistical definitions. Examples are provided to illustrate how to construct frequency tables from raw data. In summary, the document presents an overview of key statistical concepts and methods relevant for physical sciences and engineering.
Measures of Descriptive statistics and Inferential statistics MeganShaw38
The presentation will walk you through descriptive and inferential statistic measures, including a simple scenario, key measures and applications of descriptive and inferential statistic's.
This document discusses statistical methods and measurement scales. It describes three main statistical methods: descriptive statistics which summarize data, correlational methods which examine relationships between variables, and inferential statistics which make inferences about populations from samples. It also outlines four measurement scales: nominal for identity measures, ordinal for ranked order, interval for differences with equal units but no zero, and ratio which has an absolute zero where ratios have meaning.
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.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Class lecture notes # 2 (statistics for research)Harve Abella
The document discusses different types of variables and scales of measurement used in research. It defines qualitative and quantitative variables, and describes discrete and continuous quantitative variables. It also outlines four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples. The document emphasizes that statistics play a vital role in research design, validity/reliability testing, data organization and interpretation, and determining significance of findings.
Statistics for Geography and Environmental Science:an introductory lecture c...Rich Harris
A sample of the instructor's resources to support the textbook Statistics for Geography and Environmental Science. Further information at www.social-statistics.org
This document provides a lecture note on statistics for physical sciences and engineering. It begins with an introduction to statistics and its importance in various fields such as physical sciences, engineering, and research. It then discusses descriptive and inferential statistics. The document also covers topics such as data collection methods, presentation of data through tables and diagrams, and some basic statistical definitions. Examples are provided to illustrate how to construct frequency tables from raw data. In summary, the document presents an overview of key statistical concepts and methods relevant for physical sciences and engineering.
Measures of Descriptive statistics and Inferential statistics MeganShaw38
The presentation will walk you through descriptive and inferential statistic measures, including a simple scenario, key measures and applications of descriptive and inferential statistic's.
This document discusses statistical methods and measurement scales. It describes three main statistical methods: descriptive statistics which summarize data, correlational methods which examine relationships between variables, and inferential statistics which make inferences about populations from samples. It also outlines four measurement scales: nominal for identity measures, ordinal for ranked order, interval for differences with equal units but no zero, and ratio which has an absolute zero where ratios have meaning.
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.
This document provides an overview of descriptive statistics as taught in a statistics course (STS 102) at Crescent University, Nigeria. It covers topics like statistical data collection methods, presentation of data through tables and graphs, measures of central tendency and dispersion. The key objectives of descriptive statistics are to summarize and describe characteristics of data through measures, charts and diagrams. Inferential statistics is also introduced as a way to make inferences about populations based on samples.
Class lecture notes # 2 (statistics for research)Harve Abella
The document discusses different types of variables and scales of measurement used in research. It defines qualitative and quantitative variables, and describes discrete and continuous quantitative variables. It also outlines four scales of measurement - nominal, ordinal, interval, and ratio scales - and provides examples. The document emphasizes that statistics play a vital role in research design, validity/reliability testing, data organization and interpretation, and determining significance of findings.
This document discusses the different meanings and definitions of statistics. It explains that statistics has three different meanings: (1) plural sense referring to numerical facts and figures collected systematically, (2) singular sense referring to the science of collecting, analyzing, and presenting numerical data, and (3) plural of the word "statistic" referring to numerical quantities calculated from samples. The document also provides several definitions of statistics from different authors, describing it as the science of collecting, organizing, and interpreting quantitative data.
The document discusses quantitative research methods, including univariate, bivariate, and multivariate analysis. It defines key terms like frequency distribution, measures of central tendency, dispersion, continuous vs discrete variables, constructing bivariate tables, and sociological diagnostics. Univariate analysis examines one variable, bivariate looks at two variables simultaneously, and multivariate examines relationships between several variables. Quantitative analysis involves converting data to numerical formats and subjecting it to statistical analysis.
Introduction to statistics for social sciences 1Minal Jadeja
This document provides an introduction to statistics. It defines statistics as the collection, presentation, analysis, and interpretation of numerical data. Statistics can refer to either quantitative information or a method of dealing with quantitative or qualitative information. There are two main approaches in statistics - descriptive statistics, which deals with presenting data in tables or graphs to get a general picture of a sample, and inferential statistics, which involves techniques for making inferences about a whole population based on a sample. Some key uses and applications of statistics include showing how samples differ from normal distributions, facilitating comparisons, simplifying messages in data, helping to formulate and test hypotheses, and aiding in prediction and inference. However, there are also some limitations to consider with statistics, such
This document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
Statistics is important in chemistry for collecting, analyzing, and presenting quantitative data. It is used in analytical chemistry to detect, identify, and measure unknown chemical compositions using instrumentation techniques. Statistical methods like descriptive statistics are used to summarize sample data using measures like the mean and standard deviation, while inferential statistics draw conclusions from data subject to random variation. Statistics plays a vital role in chemistry research by guiding data collection, interpretation, and presentation to properly characterize and summarize results. It is especially useful for drawing reliable conclusions in chemistry research.
This document provides an overview of statistical methods in geography. It discusses key concepts such as data, geographic data, components and characteristics of geographic data, nature of geographic data, classification of geographic data, types of geographic data, sources of geographic data, methods of collecting geographic data including surveying and remote sensing, geographical data matrix, vector and raster data, variable types, significance of statistical methods in geography, and sources of data including primary, secondary, and tertiary sources. The document is a useful introduction to foundational concepts in statistics and geography.
This document discusses data reduction and analysis techniques used in research methodology. It covers topics like data processing operations including editing, coding, classification and tabulation. Classification can be according to attributes or class intervals. Tabulation involves summarizing raw data into statistical tables for analysis. Simple tabulation provides information on independent questions while complex tabulation shows interrelated data categories. Analysis estimates unknown population parameters and tests hypotheses.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting numerical data. The document notes that statistics can refer to both quantitative information and the methods used to analyze that information. It describes the key stages of a statistical analysis: data collection, organization, presentation, analysis, and interpretation. The document also discusses whether statistics is a science or an art and the important functions of statistics like providing definiteness, enabling comparison, and aiding in prediction.
1. The document discusses basic research methodology including definitions of research, categories of research such as empirical, theoretical, basic, and applied research.
2. It also covers scientific research steps, quantitative and qualitative data collection, and research design which involves formulating problems, setting objectives, designing studies, and interpreting results.
3. Key aspects of research methodology discussed include hypotheses formulation and testing, various study designs like experimental and observational, and determining appropriate sample sizes.
This document discusses statistics and data analysis as tools for researchers. It defines key concepts such as variables, constants, populations, samples, descriptive statistics, and inferential statistics. Descriptive statistics are used to describe samples, while inferential statistics allow generalizing about populations from samples. Random sampling is emphasized as it allows each population member an equal chance of being selected. The scientific method, involving hypotheses testing, is also discussed. Statistics and data analysis provide a systematic way to examine data and gain insights, though they do not prove definitive conclusions.
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.
This document discusses the process of data analysis, which includes editing, coding, classification, and tabulation of raw data collected during research. It explains that after data collection, the researcher must process and analyze the data. Key steps include editing the data for accuracy and completeness, coding the data by assigning numeric or alphabetic values to response categories, classifying the data into groups based on common attributes, and tabulating the data by organizing it into tables for further analysis and interpretation. Computer software can facilitate large-scale data processing and tabulation.
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement that determine appropriate analysis, tools to support analysis, and theoretical frameworks like grounded theory. The purpose of analysis is to obtain useful information by describing, comparing, and identifying relationships in data. Findings should be presented rigorously with careful claims supported by evidence.
Mixed methods research combines quantitative and qualitative data collection and analysis in a single study. It allows researchers to gain a deeper understanding of a phenomenon by using the strengths of both quantitative and qualitative research. There are three main types of mixed methods designs: qualitative-quantitative, quantitative-qualitative, and concurrent quantitative-qualitative. Mixed methods research provides an opportunity for quantitative and qualitative data to inform and enhance each other.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
Senior Assessment Tasks in Geography, Jo McDonald, Varsity College and Jackie...becnicholas
This document provides guidance on developing geography assessments, including essay questions, stimulus materials, and practical exercises. It emphasizes that assessments should:
- Focus on key geographic concepts and spatial relationships rather than memorization.
- Present manageable stimulus materials like maps, graphs, and limited text to apply learned concepts to new locations.
- Incorporate analysis of relationships and decision-making about provided options or scenarios.
- Include open-ended questions, paragraph responses, and manipulation of primary data through maps and graphs to demonstrate geographic skills.
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).
Data collection,tabulation,processing and analysisRobinsonRaja1
This document discusses data collection, tabulation, processing, and analysis. It begins by outlining the need for data collection to support scientific research and problem solving. It then describes various methods of data collection including warranty cards, audits, and mechanical devices. The document emphasizes the importance of processing and analyzing raw data to make it meaningful and test hypotheses. It outlines steps in processing like editing, coding, classification, and tabulation. Finally, it discusses various statistical analysis techniques including measures of central tendency, frequency distributions, correlation, regression, and parametric and non-parametric tests.
This document discusses the role and importance of statistics in scientific research. It begins by defining statistics as the science of learning from data and communicating uncertainty. Statistics are important for summarizing, analyzing, and drawing inferences from data in research studies. They also allow researchers to effectively present their findings and support their conclusions. The document then describes how statistics are used and are important in many fields of scientific research like biology, economics, physics, and more. It also provides examples of statistical terms commonly used in research studies and some common misuses of statistics.
The document discusses basics of statistics including key concepts like population, sample, parameters, and statistics. It provides definitions for population as the collection of all individuals or items under consideration, and sample as the part of the population selected for a study. Parameters describe unknown characteristics of the population, while statistics describe known characteristics of the sample and are used to infer parameters. The document also distinguishes between descriptive statistics, which summarize and organize data, and inferential statistics, which draw conclusions about populations from samples.
This document discusses the different meanings and definitions of statistics. It explains that statistics has three different meanings: (1) plural sense referring to numerical facts and figures collected systematically, (2) singular sense referring to the science of collecting, analyzing, and presenting numerical data, and (3) plural of the word "statistic" referring to numerical quantities calculated from samples. The document also provides several definitions of statistics from different authors, describing it as the science of collecting, organizing, and interpreting quantitative data.
The document discusses quantitative research methods, including univariate, bivariate, and multivariate analysis. It defines key terms like frequency distribution, measures of central tendency, dispersion, continuous vs discrete variables, constructing bivariate tables, and sociological diagnostics. Univariate analysis examines one variable, bivariate looks at two variables simultaneously, and multivariate examines relationships between several variables. Quantitative analysis involves converting data to numerical formats and subjecting it to statistical analysis.
Introduction to statistics for social sciences 1Minal Jadeja
This document provides an introduction to statistics. It defines statistics as the collection, presentation, analysis, and interpretation of numerical data. Statistics can refer to either quantitative information or a method of dealing with quantitative or qualitative information. There are two main approaches in statistics - descriptive statistics, which deals with presenting data in tables or graphs to get a general picture of a sample, and inferential statistics, which involves techniques for making inferences about a whole population based on a sample. Some key uses and applications of statistics include showing how samples differ from normal distributions, facilitating comparisons, simplifying messages in data, helping to formulate and test hypotheses, and aiding in prediction and inference. However, there are also some limitations to consider with statistics, such
This document provides an overview of key concepts in psychological statistics. It defines statistics as procedures for organizing, summarizing, and interpreting information using facts and figures. It discusses populations and samples, variables and data, parameters and statistics, descriptive and inferential statistics, sampling error, and experimental and nonexperimental methods. It also covers scales of measurement, frequency distributions, measures of central tendency and variability, and the importance of measurement in research.
Statistics is important in chemistry for collecting, analyzing, and presenting quantitative data. It is used in analytical chemistry to detect, identify, and measure unknown chemical compositions using instrumentation techniques. Statistical methods like descriptive statistics are used to summarize sample data using measures like the mean and standard deviation, while inferential statistics draw conclusions from data subject to random variation. Statistics plays a vital role in chemistry research by guiding data collection, interpretation, and presentation to properly characterize and summarize results. It is especially useful for drawing reliable conclusions in chemistry research.
This document provides an overview of statistical methods in geography. It discusses key concepts such as data, geographic data, components and characteristics of geographic data, nature of geographic data, classification of geographic data, types of geographic data, sources of geographic data, methods of collecting geographic data including surveying and remote sensing, geographical data matrix, vector and raster data, variable types, significance of statistical methods in geography, and sources of data including primary, secondary, and tertiary sources. The document is a useful introduction to foundational concepts in statistics and geography.
This document discusses data reduction and analysis techniques used in research methodology. It covers topics like data processing operations including editing, coding, classification and tabulation. Classification can be according to attributes or class intervals. Tabulation involves summarizing raw data into statistical tables for analysis. Simple tabulation provides information on independent questions while complex tabulation shows interrelated data categories. Analysis estimates unknown population parameters and tests hypotheses.
This document provides an introduction to business statistics. It defines statistics as the science of collecting, organizing, analyzing, and interpreting numerical data. The document notes that statistics can refer to both quantitative information and the methods used to analyze that information. It describes the key stages of a statistical analysis: data collection, organization, presentation, analysis, and interpretation. The document also discusses whether statistics is a science or an art and the important functions of statistics like providing definiteness, enabling comparison, and aiding in prediction.
1. The document discusses basic research methodology including definitions of research, categories of research such as empirical, theoretical, basic, and applied research.
2. It also covers scientific research steps, quantitative and qualitative data collection, and research design which involves formulating problems, setting objectives, designing studies, and interpreting results.
3. Key aspects of research methodology discussed include hypotheses formulation and testing, various study designs like experimental and observational, and determining appropriate sample sizes.
This document discusses statistics and data analysis as tools for researchers. It defines key concepts such as variables, constants, populations, samples, descriptive statistics, and inferential statistics. Descriptive statistics are used to describe samples, while inferential statistics allow generalizing about populations from samples. Random sampling is emphasized as it allows each population member an equal chance of being selected. The scientific method, involving hypotheses testing, is also discussed. Statistics and data analysis provide a systematic way to examine data and gain insights, though they do not prove definitive conclusions.
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.
This document discusses the process of data analysis, which includes editing, coding, classification, and tabulation of raw data collected during research. It explains that after data collection, the researcher must process and analyze the data. Key steps include editing the data for accuracy and completeness, coding the data by assigning numeric or alphabetic values to response categories, classifying the data into groups based on common attributes, and tabulating the data by organizing it into tables for further analysis and interpretation. Computer software can facilitate large-scale data processing and tabulation.
This document discusses data analysis and presentation. It covers qualitative and quantitative analysis methods, scales of measurement that determine appropriate analysis, tools to support analysis, and theoretical frameworks like grounded theory. The purpose of analysis is to obtain useful information by describing, comparing, and identifying relationships in data. Findings should be presented rigorously with careful claims supported by evidence.
Mixed methods research combines quantitative and qualitative data collection and analysis in a single study. It allows researchers to gain a deeper understanding of a phenomenon by using the strengths of both quantitative and qualitative research. There are three main types of mixed methods designs: qualitative-quantitative, quantitative-qualitative, and concurrent quantitative-qualitative. Mixed methods research provides an opportunity for quantitative and qualitative data to inform and enhance each other.
Statistics is the collection and analysis of data. There are two main branches: descriptive statistics, which organizes and summarizes data, and inferential statistics, which uses descriptive statistics to make predictions. Statistics starts with a question and uses data to provide information to help make decisions. It is widely used in business, health, education, research, social sciences, and natural resources.
Senior Assessment Tasks in Geography, Jo McDonald, Varsity College and Jackie...becnicholas
This document provides guidance on developing geography assessments, including essay questions, stimulus materials, and practical exercises. It emphasizes that assessments should:
- Focus on key geographic concepts and spatial relationships rather than memorization.
- Present manageable stimulus materials like maps, graphs, and limited text to apply learned concepts to new locations.
- Incorporate analysis of relationships and decision-making about provided options or scenarios.
- Include open-ended questions, paragraph responses, and manipulation of primary data through maps and graphs to demonstrate geographic skills.
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).
Data collection,tabulation,processing and analysisRobinsonRaja1
This document discusses data collection, tabulation, processing, and analysis. It begins by outlining the need for data collection to support scientific research and problem solving. It then describes various methods of data collection including warranty cards, audits, and mechanical devices. The document emphasizes the importance of processing and analyzing raw data to make it meaningful and test hypotheses. It outlines steps in processing like editing, coding, classification, and tabulation. Finally, it discusses various statistical analysis techniques including measures of central tendency, frequency distributions, correlation, regression, and parametric and non-parametric tests.
This document discusses the role and importance of statistics in scientific research. It begins by defining statistics as the science of learning from data and communicating uncertainty. Statistics are important for summarizing, analyzing, and drawing inferences from data in research studies. They also allow researchers to effectively present their findings and support their conclusions. The document then describes how statistics are used and are important in many fields of scientific research like biology, economics, physics, and more. It also provides examples of statistical terms commonly used in research studies and some common misuses of statistics.
The document discusses basics of statistics including key concepts like population, sample, parameters, and statistics. It provides definitions for population as the collection of all individuals or items under consideration, and sample as the part of the population selected for a study. Parameters describe unknown characteristics of the population, while statistics describe known characteristics of the sample and are used to infer parameters. The document also distinguishes between descriptive statistics, which summarize and organize data, and inferential statistics, which draw conclusions about populations from samples.
Part 2 of the course "Topics in Survey Methologogy and Survey Analysis" held in University of Helsinki in 2013, together with prof Seppo Laaksonen (Part 1) and prof Risto Lehtonen (Part 3). Part 2 included topics such as exploratory and confirmatory analysis, reliability, validity and measurement errors, data reduction with factor analysis, visualization of multidimensional data. The course was intended mainly for doctoral students from Social (and Behavioral) Sciences.
Characteristics of Quantitative ResearchGeorgePeligro
This document provides information about quantitative research, including its definition, characteristics, strengths, weaknesses, and different types. Quantitative research is defined as a systematic process of obtaining numerical data about the world. It has characteristics such as using structured research instruments, large sample sizes, clearly defined research questions, and numerical data presented statistically. The strengths include testing theories, generalizing findings, and establishing cause-and-effect relationships. Weaknesses can include missing local contexts and understandings. The main types of quantitative research discussed are experimental (true, quasi, pre-experimental) and non-experimental (descriptive, correlational, causal-comparative, comparative, evaluative).
MAC411(A) Analysis in Communication Researc.pptPreciousOsoOla
This document provides information on the course "Data Analysis in Communication Research" taught at Covenant University. The course aims to give students an in-depth understanding of applying basic statistical methods in mass communication. It will cover topics such as sampling designs, probability distributions, and methods for analyzing quantitative and qualitative data. Students will learn statistical techniques and data processing. They will conduct data analysis, interpretation and presentation through practical exercises and demonstrations. The course assessments include mid-semester exams, assignments, and an alpha semester exam.
Zuur et al 2010 methods in ecology and evolution a protocol for data explorat...Lisiane Zanella
This document provides a protocol for data exploration to avoid common statistical problems when analyzing ecological data. It discusses exploring data for outliers, heterogeneity, collinearity, dependence, and other issues. The protocol aims to identify potential problems before statistical analysis to reduce type I and II errors and ensure robust conclusions. Data exploration is presented as an essential first step, taking up to 50% of analysis time. Graphical tools are emphasized over tests for exploring data visually and identifying issues to address. The document provides examples and discusses handling outliers and other problems when they arise.
The document provides information on the course content and practical components of a statistics course.
The theory section covers topics like introduction to statistics, measures of central tendency and dispersion, probability, normal distribution, correlation, regression, tests of significance, analysis of variance, and sampling methods.
The practical section involves applying concepts like graphical representation of data, measures of central tendency and dispersion, moments, correlation and regression analysis, t-tests, chi-square tests, and analysis of variance to real data. Reference books on agricultural statistics are also listed.
This document provides an introduction to a course on statistical methods in nursing. It outlines the general objectives of understanding the nature and definition of statistics, its brief historical development, distinguishing samples from populations, types of variables, and the importance of statistics in research. It includes a pre-test to assess students' basic knowledge of statistical concepts before beginning the lessons.
This document summarizes the four levels or scales of measurement used in quantitative research: nominal, ordinal, interval, and ratio scales. Nominal scales simply categorize or name variables without order, while ordinal scales maintain a rank order but the intervals between ranks are unknown. Interval scales provide equal distances between ranks and ratio scales have all the properties of interval scales with the additional property of having a true zero point. Understanding the appropriate scale of measurement is important for determining which statistical analyses can be applied to research data.
This presents an overview about relevance and significance of statistics as a valid tool in enhancing quality of research. It also touches upon some misuse and abuse of statistics.
The document provides an overview of data analysis concepts and methods for qualitative and quantitative data. It discusses topics such as descriptive statistics, measures of central tendency and spread. It also covers inferential statistics concepts like ANOVA, ANCOVA, regression, and correlation. Both the advantages and disadvantages of qualitative data analysis are presented. The document is a presentation on research methodology focusing on data analysis.
In this study various techniques for exploratory spatial data analysis are reviewed : spatial autocorrelation, Moran's I statistic, hot spots analysis, spatial lag and spatial error models.
CHAPTER 10 MIXED METHODS PROCEDURESHow would you write a mixed mEstelaJeffery653
CHAPTER 10 MIXED METHODS PROCEDURES
How would you write a mixed methods procedure section for your proposal or study? Up until this point, we have considered collected quantitative data and qualitative data. We have not discussed “mixing” or combining the two forms of data in a study. We can start with the assumption that both forms of data provide different types of information (open-ended data in the case of qualitative and closed-ended data in the case of quantitative). If we further assume that each type of data collection has both limitations and strengths, we can consider how the strengths can be combined to develop a stronger understanding of the research problem or questions (and, as well, overcome the limitations of each). In a sense, more insight into a problem is to be gained from mixing or integration of the quantitative and qualitative data. This “mixing” or integrating of data, it can be argued, provides a stronger understanding of the problem or question than either by itself. Mixed methods research, therefore, is simply “mining” the databases more by integrating them. This idea is at the core of a new methodology called “mixed methods research.”
Conveying the nature of mixed methods research and its essential characteristics needs to begin a good mixed methods procedure. Start with the assumption that mixed methods is a methodology in research and that the readers need to be educated as to the basic intent and definition of the design, the reasons for choosing the procedure, and the value it will lend to a study. Then, decide on a mixed methods design to use. There are several from which to choose; consider the different possibilities and decide which one is best for your proposed study. With this choice in hand, discuss the data collection, the data analysis, and the data interpretation, discussion, and validation procedures within the context of the design. Finally, end with a discussion of potential ethical issues that need to be anticipated in the study, and suggest an outline for writing the final study. These are all standard methods procedures, and they are framed in this chapter as they apply to mixed methods research. Table 10.1 shows a checklist of the mixed methods procedures addressed in this chapter.
COMPONENTS OF MIXED METHODS PROCEDURES
Mixed methods research has evolved into a set of procedures that proposal developers and study designers can use in planning a mixed methods study. In 2003, the Handbook of Mixed Methods in the Social and Behavior Sciences (Tashakkori & Teddlie, 2003) was published (and later added to in a second edition, see Tashakkori & Teddlie, 2010), providing a comprehensive overview of this approach. Now several journals emphasize mixed methods research, such as the Journal of Mixed Methods Research, Quality and Quantity, Field Methods, and the International Journal of Multiple Research Approaches. Additional journals actively encourage this form of inquiry (e.g., International Journal of ...
Biostatistics Master’s Degree by Slidesgo.pptxSuharnoUsman1
This document provides definitions and explanations of key concepts in biostatistics. It begins by defining statistics as the study of collecting, organizing, analyzing, and interpreting numerical data. Descriptive statistics are used to summarize and describe samples of data through measures like the mean, median and mode. Inferential statistics are used to analyze data, draw conclusions, and test hypotheses about parameters. Biostatistics applies statistical techniques to solve problems in human health and biology. The roles of statistics in research include determining sample sizes, testing instruments, analyzing data, and testing hypotheses. Statistics are widely used in the health sector for tasks like determining the magnitude of health problems, measuring vital events, evaluating programs, and conducting research.
Application Of Sampling Methods For The Research DesignGina Rizzo
This document discusses sampling methods and sample size considerations for research design. It explains that sampling involves selecting a representative subset of a larger population for study. The sample size depends on factors like the population size, desired precision of results, level of analysis required, and practical constraints. Quantitative research typically uses larger sample sizes while qualitative research uses smaller, in-depth samples. Sample size impacts statistical precision, with larger samples decreasing sampling error. The document provides guidance on determining appropriate sample sizes for different types of studies.
Characteristic of a Quantitative Research PPT.pptxJHANMARKLOGENIO1
The document discusses quantitative research, including its definition, characteristics, strengths, and weaknesses. It notes that quantitative research seeks objective and accurate measurement through clearly defined research questions and structured instruments. Data is collected in numerical form from large sample sizes to allow for replication and generalization. Strengths include objectivity and the ability to analyze large amounts of data, while weaknesses include high costs and the inability to explore contextual factors.
This document discusses limitations and applications of statistics. It begins by covering limitations of statistics, such as it only dealing with quantitative data and groups/aggregates, and possible errors in statistical analysis. It then covers many fields that statistics can be applied to, such as actuarial science, biostatistics, econometrics, environmental statistics, epidemiology, and others. It concludes with sample multiple choice questions related to limitations and applications of statistics.
Similar to Sample of slides for Statistics for Geography and Environmental Science (20)
Quantitative Methods in Geography Making the Connections between Schools, Uni...Rich Harris
A report into the nature of and attitudes towards quantitative methods teaching in geography, with recommendations for how the benchmark statement might be changed.
White flight, ethnic cliffs and other unhelpful hyperbole?Rich Harris
In an (unguarded?) conversation with a journalist, I talked about a 'cliff-edge' measure of segregation where neighbouring places have very different proportions of their resident population classified as White British in the 2011 Census. The words, rephrased as 'ethnic cliffs' was soon coupled with talk of White Flight from British cities and has appeared in a number of national newspapers and magazines, alongside like 'self-segregation' and 'sundown segregation' (The Sunday Times and the Daily Mail). In this presentation I look at changes to the ethnic composition of census zones in England from 2001 to 2011 and ask whether such phrases are unhelpful hyperbole or simply vivid but accurate descriptors of "Britain's new problem" (Goodhart, 2013 writing in Prospect Magazine).
There has been long and wide-ranging debate in the social science literature about how best to conceptualise and to measure segregation (see, inter alia, Allen and Vignoles, 2007; Johnston and Jones, 2010; Harris, 2011). A popular measure is the dissimilarity index, usually attributed to Duncan and Duncan (1955). This is somewhat ironic because in another paper published in the same year, the same two authors were much more cautious about advocating any one index as preferable to others and were wise to the geographical limitations: "all of the segregation indexes have in common the assumption that segregation can be measured without regard to the spatial patterns of white and nonwhite residence in a city" (p.215). Whilst one response to this shortcoming has been the development of spatial measures of segregation (Wong, 1993; Reardon and O'Sullivan, 2004; Harris, 2012), a number of papers from the 1980s and 90s treated the measurement of segregation as a (spatial) optimisation problem (Jakubs 1981; Morgan 1983; Waldorf 1993). In this paper I revisit that optimisation literature, substituting geographical distances between places with ‘nearest-neighbour distances’ to determine the cost function. Applying this method to the 2011 Census data and to England, I consider claims of “white flight” that have appeared in the media.
Motion Charts, White Flight and Ethnic Cliffs?Rich Harris
The aim of this presentation is to investigate claims of decreased segregation yet also of ‘white flight’ from English cities during the period from 2001 to 2011. It does so supplementing a traditional measure of segregation, the dissimilarity index,
with measures comparing differences between adjoining small areas. Together these measures provide insight not only into the amount of segregation but also its spatial configuration within local authorities, including the degree to which different ethnic groups are clustered together of dispersed across the authorities. An analysis of change is then undertaken, asking whether the neighbouring small areas with greatest differences in their ethnic compositions in 2001 become more or less dissimilar by 2011, and whether those changes are caused by more population mixing or by the withdrawal of the White British population from those areas. Motion charts also are presented to warning against over-simplification and ‘one-size-fits-all’ explanations, stressing the individual trajectories of different local authorities.
Commentary: Ethno-demographic change in English local authorities, 1991-2011Rich Harris
A commentary on a graphic submitted to the journal Environment and Planning A as one of its featured graphics. That graphic aims to capture various dimensions of population change within English local authorities from 1991 to 2011: the proportional increase in the Asian population, the decrease in the White British population, generally decreasing Asian - White British segregation within authorities on average but with that average concealing some increases in spatial heterogeneity: increased differences between some neighbouring small areas (and also increased differences between local authorities). To see the graph, please visit http://www.social-statistics.org/?p=1064
Sermon given at the 10.30am service, Christ Church Downend, Sunday February 10th, 2013. The Bible reading is Luke 9: 28-36. More sermons and talks at http://www.social-statistics.org/?cat=22
Geographies of ethnicity by school in LondonRich Harris
Maps of the prevalence of various ethnic groups in London’s secondary schools according to their proportion of new entrants to the schools in September 2008.
A comment with new analysis on an Financial Times article talking about the possibility of White Flight from London revealed by the 2011 UK Census results.
Geographies of ethnicity in the 2011 Census of England and WalesRich Harris
The document discusses maps showing the geographic distribution of ethnic groups in England and Wales based on the 2011 Census. It notes that an unconventional map projection was used to give more space to London boroughs. It also explains that some locations were moved to avoid points overlapping and that borough boundaries are not perfectly accurate. The document provides information on how the map categorizes data into percentiles to emphasize areas with the highest ethnic group percentages.
Faith and Climate Change Scepticism: Competing Christian theologies of Enviro...Rich Harris
This document discusses competing Christian theologies regarding environmental stewardship and climate change skepticism. It outlines a "dominion" theology that views humans as having authority to exploit nature for their ends, which has been used to oppose environmental regulations. However, it also presents an alternative "stewardship" view based on caring for God's creation. The roles of various organizations in promoting these perspectives are examined, showing how theology and politics can intersect on environmental issues.
Neoconservatism, Nature and the American Christian RightRich Harris
The document discusses the relationship between Christianity, particularly Christian fundamentalism, and environmental policy in the United States. It provides historical context on how interpretations of passages from Genesis have been used to support both environmental domination and stewardship. It also outlines the rise of the "Wise Use" movement in opposition to environmental regulations and protections. This movement had ties to industry groups and found common cause with the Christian Right and Republican party to advocate for reduced environmental regulation during the Reagan and George W. Bush administrations. The document examines the complex interplay between religious ideology and politics in shaping American environmental policy.
Sleepwalking towards Johannesburg? Local
measures of ethnic segregation between
London’s secondary schools 2003 – 2008/9. Presented at the PLASC Users Group conference, March 6th, 2012
Using geographical micro-data to measure segregation at the scale of competin...Rich Harris
Segregation is a spatial outcome of spatial processes that needs to be measured spatially and at a scale meaningful to the study. This is the axiom from which local indices of segregation are developed and applied to the patterns of admission observed for cohorts of pupils entering London's state-funded secondary (high) schools in each of the years from 2003 to 2008. The indices - local indices of difference, isolation and of concentration – are used to measure social segregation not between arbitrary areas or regions but specifically for schools that overlap in regard to their admission spaces. This is made possible by the use of detailed and geographically referenced governmental micro-data that allow the pupil flows from elementary to high schools to be modeled and therefore "competing" schools to be identified. Using eligibility for free school meals as a measure of social segregation, sizable differences in the proportions of FSM eligible pupils recruited by apparently competing schools are found, with selective schools especially and also faith schools under-recruiting such pupils. Whilst there is some evidence that social segregation has decreased over the period, the trend is considered to be an artifact of using free school meals as a measure of disadvantage. As such the problem shifts from at what scale to measure between-school segregation to what actually is an appropriate measure to use.
Level 3 NCEA - NZ: A Nation In the Making 1872 - 1900 SML.pptHenry Hollis
The History of NZ 1870-1900.
Making of a Nation.
From the NZ Wars to Liberals,
Richard Seddon, George Grey,
Social Laboratory, New Zealand,
Confiscations, Kotahitanga, Kingitanga, Parliament, Suffrage, Repudiation, Economic Change, Agriculture, Gold Mining, Timber, Flax, Sheep, Dairying,
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Leveraging Generative AI to Drive Nonprofit InnovationTechSoup
In this webinar, participants learned how to utilize Generative AI to streamline operations and elevate member engagement. Amazon Web Service experts provided a customer specific use cases and dived into low/no-code tools that are quick and easy to deploy through Amazon Web Service (AWS.)
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
This document provides an overview of wound healing, its functions, stages, mechanisms, factors affecting it, and complications.
A wound is a break in the integrity of the skin or tissues, which may be associated with disruption of the structure and function.
Healing is the body’s response to injury in an attempt to restore normal structure and functions.
Healing can occur in two ways: Regeneration and Repair
There are 4 phases of wound healing: hemostasis, inflammation, proliferation, and remodeling. This document also describes the mechanism of wound healing. Factors that affect healing include infection, uncontrolled diabetes, poor nutrition, age, anemia, the presence of foreign bodies, etc.
Complications of wound healing like infection, hyperpigmentation of scar, contractures, and keloid formation.
How to Make a Field Mandatory in Odoo 17Celine George
In Odoo, making a field required can be done through both Python code and XML views. When you set the required attribute to True in Python code, it makes the field required across all views where it's used. Conversely, when you set the required attribute in XML views, it makes the field required only in the context of that particular view.
Sample of slides for Statistics for Geography and Environmental Science
1. Statistics for Geography and
Environmental Science:
an introductory lecture course
(sample)
By Richard Harris, with material
by Claire Jarvis
USA: http://amzn.to/rNBWd5
UK: http://amzn.to/tZ7fVu
5. The modules
Module1 makes the case for knowing
about statistics as a transferable skill
and to be equipped for social and
political debate.
Module 2 is about using descriptive
statistics and simple graphical
techniques to explore and make
sense of data.
Module 3 discusses the Normal
curve, the properties of which
provide the basis for inferential
6. The modules
Module 4 is about the principles of
research design and effective data
collection.
Module 6 discusses the role of
hypothesis testing.
Module 7 is about regression
analysis.
7. The modules
Module 8 moves to modelling point
patterns, ―hotspot analysis‖ and ways
of measuring patterns of spatial
autocorrelation in data.
Module 9 looks at spatial regression
models, geographically weighted
regression and multilevel modelling.
Each module is explored more fully
in the accompanying textbook,
Statistics for Geography and
Environmental Science.
8. Module 1
(Extracts from Chapter 1 of Statistics for Geography
and Environmental Science)
DATA, STATISTICS AND
GEOGRAPHY
9. Module overview
To convince you that studying
statistics is a good idea!
Our argument is that data collection
and analysis are central to the
functioning of contemporary society
so knowledge of quantitative
methods is a necessary skill to
contribute to social and scientific
debate.
10. About statistics
Statistics are a reflective practice: a
way of approaching research that
requires a clear and manageable
research question to be formulated, a
means to answer that question,
knowledge of the assumptions of
each test used, an understanding of
the consequences of violating those
assumptions, and awareness of the
researcher‘s own prejudices when
doing the research.
11. Some reasons to study statistics
Reasons for human geographers
– Data collection and analysis are central
to the functioning of society, to systems
of governance and science.
– Knowledge of statistics is an entry into
debate, informed critique and the
possibility of creating change.
12. Some reasons to study statistics
Reasons for GI scientists
– To address the uncertainties and
ambiguities of using data analytical.
– Because of the increased integration of
mapping capabilities, data visualizations
and (geo-) statistical analysis.
13. Some reasons to study statistics
Reasons for all students
– They provide a transferable skill set
using in other areas of research, study
and employment.
– There is a recognised shortage of
students with skills in quantitative
methods, especially within the social
sciences.
14. Types of statistic
Descriptive
– Used to provide a summary of a set of
measurements, e.g. the average.
Inferential
– Use the data at hand to convey information
about the population (‗the greater
something‘) from which the data are drawn.
Relational
– Consider whether greater or lesser values
in one set of data are related to greater or
lesser values in another.
15. Geographical data
These are records of what has
happened at some location on the
Earth‘s surface and where.
For many statistical tests the where
is largely ignored.
However, it is central to geostatistics
and to spatial statistics (as their
names suggest)
16. Some problems when analysing
geographical data
Standard statistical tests assume that
each ‗bit‘ of data (each observation)
has a value that is not influenced by
any other.
However, we may often expect there
to be geographical patterns in the
data.
– Spatial autocorrelation: geographical
patterns in the measurements
17. Some problems when analysing
geographical data
Determining what causes what in a
complex and dynamic natural or
social system is extremely tricky.
Two things may be associated (e.g.
greater income inequality and more
non-recycled waste) without the one
directly causing the other.
18. Some problems when analysing
geographical data
Data and structured forms of enquiry
can only tell us so much and may not
be appropriate to some types of
research for which a more
qualitative, participatory or less
representational approach may be
better.
19. Further reading
Chapter 1 of Statistics for
Geography and Environmental
Science by Richard Harris and Claire
Jarvis (Prentice Hall / Pearson, 2011)
Includes a review of the following
key concepts: types of statistics;
why error is unavoidable;
geographical data analysis; and
spatial autocorrelation and the first
law of geography.
20. Module 2
(Extracts from Chapter 2 of Statistics for Geography
and Environmental Science)
DESCRIPTIVE STATISTICS
21. Module overview
This module is about ―everyday
statistics‖, the sort that summarise
data and describe them in simple
ways.
They include the number of home
runs this season, average male
earnings, numbers unemployed,
outside temperature, average cost of
a barrel of oil, regional variations in
crime rates, pollution statistics,
measures of the economy and other
―facts and figures‖
22. Data and variables
Data
– A collection of observations:
measurements made of something.
A variable
– Another name for a collection of data.
Variable because it is unlikely that the
data are all the same.
Data types
– These include discrete, continuous,
and categorical data.
23. Simple ways of presenting data
Discrete data Continuous data
Frequency table Summary table
Bar chart (below) Histogram (below, with a rug plot)
25. Information to include
in a summary table
Measures of central tendency
(―averages‖)
– The mean and/or median
• The ―centre‖ of the data
Measures of spread and variation
– The range (minimum to maximum)
– The interquartile range (from ‗mid-
spread‘ of the data)
– The standard deviation,s
26. More about the standard deviation
Essentially a measure of average
variation around the mean.
It is also the square root of the
variance.
The variance is the sum of squares
divided by the degrees of freedom
27. Boxplots
Are useful for
showing the
median,
interquartile
range and range
of a set of data,
for indentifying
outliers and also
for comparing
variables.
28. Other ways of classifying numeric
data
Nominal, ordinal, interval and ratio
Counts and rates
Proportions and percentages
Parametric and non—parametric
Arithmetic and geometric
Primary and secondary
29. Further reading
Chapter 2 of Statistics for Geography
and Environmental Science by Richard
Harris and Claire Jarvis (Prentice Hall /
Pearson, 2011)
Includes a review of the following key
concepts: data and variables; discrete
and continuous data; the range;
histograms, rug plots, and stem and
leaf plots; measures of central
tendency; why averages can be
misleading; quantiles; the sum of
squares; degrees of freedom; the
standard deviation and the variance;
box plots; and five and six number
summaries