The document discusses key concepts in research methods and statistics. It provides definitions and explanations of terms like variables, hypotheses, reliability, validity, experimental research, measures of central tendency and variability, levels of measurement, and the scientific method. The document is a study guide or review of important foundational concepts for conducting quantitative research.
This document describes a five-step approach to conducting constant comparative analysis of qualitative interview data:
1. Comparison within a single interview to develop categories, understand the core message, and check for consistency.
2. Comparison between interviews from the same group to expand codes, conceptualize themes, and identify patterns and typologies.
3. Comparison of interviews from different groups to triangulate data, understand different perspectives, and identify additional themes.
4. Comparison of interviews in pairs from couples to conceptualize relationship issues and understand both partner's perspectives.
5. Comparison of interviews from several couples to identify criteria for comparing couples, hypothesize about relationship patterns, and develop typologies.
The document defines and discusses various types of validity and reliability in research experiments and measurements. It summarizes the key threats to internal and external validity in experiments. It also defines correlation research and distinguishes between explanatory and predictive research designs. Finally, it defines ethnographic research, when it should be used, and outlines the five key steps in conducting an ethnographic study.
Triangulation refers to using multiple research methods, data sources, investigators, or theoretical perspectives to study a phenomenon and increase the validity and reliability of research findings. It involves looking at something from several different angles to gain a more complete understanding. While triangulation aims to overcome bias and validity issues, it is most effective when methods, data sources, and perspectives share the same underlying assumptions and are analyzed consistently within a single paradigm or theoretical framework. Simply combining qualitative and quantitative data or perspectives risks introducing inconsistencies without a shared foundation.
Correlational research investigates potential relationships between variables without manipulating them. Researchers seek associations between variables to help explain behaviors or predict outcomes. If a strong enough relationship exists between two variables, scores on one can be used to predict the other. Correlational techniques include multiple regression, coefficient of multiple correlation, and factor analysis. Researchers must consider threats to internal validity like subject characteristics, instrumentation, and data collector bias.
The document discusses content analysis and grounded theory. It provides definitions of content analysis from various scholars and outlines its essential purpose and process. The key steps in content analysis are defined as defining research questions, population/sample, context, units of analysis, codes, categories, coding, analysis and summarizing. Grounded theory is also discussed, including its origins, definitions, key elements of theoretical sampling, coding (open, axial, selective), constant comparison and core variables. A worked example of interpreting discussion in an infant classroom is provided to demonstrate grounded theory.
This document provides an overview of correlational research and inferential statistics. It defines correlation as measuring the relationship between two variables, and explains that correlational research allows determining if variables are related but not if there is a causal relationship. Key aspects covered include independent and dependent variables, the Pearson correlation coefficient for measuring strength and direction of relationships, and types of correlations. Scatter plots and examples are used to illustrate concepts. Hypothesis testing and different sampling methods are also summarized.
This document discusses various scales and measures used in research. It outlines Thurstone scales, Guttman scales, feeling thermometers, and ranking scales which are used to measure direction, intensity, commitment, and compare references. Composite measures are used to simplify data analysis. Quality control and reliability ensure unbiased questions and stable results. There are various types of reliability testing like split-half, test-retest, and inter-coder. Validity measures if a scale accurately measures what it intends through content, criterion, predictive, and construct validity testing.
This document discusses measurement and scaling techniques used in research. It begins by defining measurement and scaling, and describes four levels of measurement scales: nominal, ordinal, interval, and ratio scales. It then explains different scaling techniques, including comparative techniques like paired comparison scales and rank order scales, as well as non-comparative techniques like Likert scales. The document provides examples to illustrate each scaling technique and discusses how to select the appropriate technique for a given research problem.
This document describes a five-step approach to conducting constant comparative analysis of qualitative interview data:
1. Comparison within a single interview to develop categories, understand the core message, and check for consistency.
2. Comparison between interviews from the same group to expand codes, conceptualize themes, and identify patterns and typologies.
3. Comparison of interviews from different groups to triangulate data, understand different perspectives, and identify additional themes.
4. Comparison of interviews in pairs from couples to conceptualize relationship issues and understand both partner's perspectives.
5. Comparison of interviews from several couples to identify criteria for comparing couples, hypothesize about relationship patterns, and develop typologies.
The document defines and discusses various types of validity and reliability in research experiments and measurements. It summarizes the key threats to internal and external validity in experiments. It also defines correlation research and distinguishes between explanatory and predictive research designs. Finally, it defines ethnographic research, when it should be used, and outlines the five key steps in conducting an ethnographic study.
Triangulation refers to using multiple research methods, data sources, investigators, or theoretical perspectives to study a phenomenon and increase the validity and reliability of research findings. It involves looking at something from several different angles to gain a more complete understanding. While triangulation aims to overcome bias and validity issues, it is most effective when methods, data sources, and perspectives share the same underlying assumptions and are analyzed consistently within a single paradigm or theoretical framework. Simply combining qualitative and quantitative data or perspectives risks introducing inconsistencies without a shared foundation.
Correlational research investigates potential relationships between variables without manipulating them. Researchers seek associations between variables to help explain behaviors or predict outcomes. If a strong enough relationship exists between two variables, scores on one can be used to predict the other. Correlational techniques include multiple regression, coefficient of multiple correlation, and factor analysis. Researchers must consider threats to internal validity like subject characteristics, instrumentation, and data collector bias.
The document discusses content analysis and grounded theory. It provides definitions of content analysis from various scholars and outlines its essential purpose and process. The key steps in content analysis are defined as defining research questions, population/sample, context, units of analysis, codes, categories, coding, analysis and summarizing. Grounded theory is also discussed, including its origins, definitions, key elements of theoretical sampling, coding (open, axial, selective), constant comparison and core variables. A worked example of interpreting discussion in an infant classroom is provided to demonstrate grounded theory.
This document provides an overview of correlational research and inferential statistics. It defines correlation as measuring the relationship between two variables, and explains that correlational research allows determining if variables are related but not if there is a causal relationship. Key aspects covered include independent and dependent variables, the Pearson correlation coefficient for measuring strength and direction of relationships, and types of correlations. Scatter plots and examples are used to illustrate concepts. Hypothesis testing and different sampling methods are also summarized.
This document discusses various scales and measures used in research. It outlines Thurstone scales, Guttman scales, feeling thermometers, and ranking scales which are used to measure direction, intensity, commitment, and compare references. Composite measures are used to simplify data analysis. Quality control and reliability ensure unbiased questions and stable results. There are various types of reliability testing like split-half, test-retest, and inter-coder. Validity measures if a scale accurately measures what it intends through content, criterion, predictive, and construct validity testing.
This document discusses measurement and scaling techniques used in research. It begins by defining measurement and scaling, and describes four levels of measurement scales: nominal, ordinal, interval, and ratio scales. It then explains different scaling techniques, including comparative techniques like paired comparison scales and rank order scales, as well as non-comparative techniques like Likert scales. The document provides examples to illustrate each scaling technique and discusses how to select the appropriate technique for a given research problem.
There are two main types of composite measures - indexes and scales. Indexes use nominal level indicators that are given equal weight, while scales use continuous level indicators where each response contributes differently to the total score. Both can be weighted or unweighted. Constructing indexes and scales requires selecting valid items, examining relationships between items and constructs, and handling missing data. Common scale types include Thurstone, Likert, semantic differential, and Guttman scales. Typologies summarize variables into nominal categories but risk oversimplification. Validity and reliability are important concepts for measuring devices.
This document provides an overview of statistical analysis of questionnaire data. It discusses topics like questionnaire construction, data entry, reliability analysis using Cronbach's alpha, descriptive statistics for Likert scale items including frequencies, medians, interquartile ranges and box plots. It also covers composite scale analysis using means, standard deviations and comparisons between groups. An example is provided on assessing student satisfaction regarding teaching using 4 questionnaire items from 60 students. Results would be reported using tables and figures with interpretations.
This glossary provides definitions of many of the terms used in the guides to conducting qualitative and quantitative research. The definitions were developed by members of the research methods seminar taught by Mike Palmquist in the 1990s and 2000s at the Colorado State University.
The document discusses different scales of measurement used in research including nominal, ordinal, interval, and ratio scales. It also discusses key concepts in statistical analysis like validity, reliability, inferential statistics, the null hypothesis, t-tests, and analysis of variance (ANOVA). Specifically, it explains that t-tests are used to compare two means, the null hypothesis states there is no relationship between variables, and ANOVA computes the F-ratio to measure differences between groups.
The document discusses qualitative content analysis. It defines content analysis as the systematic classification and interpretation of text through coding and identifying themes. Content analysis allows researchers to understand social reality and explore meanings in a scientific manner. It can use inductive or deductive approaches to analyze data. Unique characteristics include flexibility in approaches and ability to extract manifest and latent meanings from text. Researchers use content analysis to describe message characteristics and identify themes. The process involves defining a research question, sampling material, developing a coding scheme of themes, coding the content, and analyzing results both qualitatively and quantitatively. Validity and reliability are also addressed.
eeMba ii rm unit-3.1 measurement & scaling aRai University
This document discusses various types of measurement scales used in business research including nominal, ordinal, interval, and ratio scales. It also describes commonly used scaling techniques such as rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales. The key aspects of valid and reliable measurement are highlighted, including selecting observable events, developing a mapping rule to assign numbers, and applying the rule consistently.
This document defines content analysis and discusses Kerlinger's definition. Kerlinger defines content analysis as a systematic, objective, and quantitative method for studying communication. It involves treating all content in an identical manner (systematic), allowing different researchers to obtain the same results (objective), and quantifying the analysis to aid precision (quantitative). The document also lists uses of content analysis, such as identifying what exists in media, comparing media to reality, testing hypotheses, and exploring media images of minority groups.
SOURCES OF ERROR AND SCALES OF MEASUREMENTashanrajpar
This document discusses different scales of measurement used in behavioral sciences research: nominal, ordinal, interval, and ratio scales. It provides examples of variables that fall under each scale. Nominal scales involve categorizing variables without rank. Ordinal scales involve ordering categories but without precise intervals between ranks. Interval scales have equal intervals between ranks and a true zero point. Ratio scales have all the properties of interval scales plus a meaningful zero point. The document also discusses sources of error in measurement, including issues related to respondents, measurers, and instruments.
This document discusses correlational research, which aims to describe relationships between variables and measure their strength. Correlational research observes how variables change together without manipulation. A correlation indicates the direction, form, and consistency of a relationship. Positive relationships involve variables changing in the same direction, while negative relationships involve opposite direction changes. Correlational designs can be explanatory, relating co-varying variables, or predictive, identifying predictors of an outcome variable. Correlational research allows investigating relationships but cannot determine causation due to low internal validity.
This document provides an overview of non-parametric statistics. It begins by explaining that non-parametric statistics do not specify conditions about a population's parameters and can be used when distributions are not normal or variables are measured nominally or ordinally. Some common non-parametric tests are then described for comparing independent groups (like the Mann-Whitney U test) or dependent groups (like the Wilcoxon matched-pairs test). The document concludes by contrasting parametric and non-parametric tests, noting that non-parametric tests are less powerful but also have fewer assumptions.
Commonly Used Statistics in Survey ResearchPat Barlow
This is a version of our "commonly used statistics" presentation that has been modified to address the commonly used statistics in survey research and analysis. It is intended to give an *overview* of the various uses of these tests as they apply to survey research questions rather than the point-and-click calculations involved in running the statistics.
This document provides an overview of quantitative data analysis, including different data types like nominal, ordinal, interval, and ratio scales. It also describes common statistical analyses like descriptive statistics to analyze frequencies, means, medians and modes, as well as inferential statistics for comparison tests, correlation, and regression. Specific comparison tests are outlined for within-groups, between-groups, and mixed subject designs depending on the number of groups being compared.
The document discusses measurement, scaling, and sampling in research. It defines measurement instruments and explains that validity and reliability are key indicators of quality. Scaling is discussed as a way to quantify qualities using numerical values. The document also defines sampling as selecting a subset of a population for observation, and explains probability and non-probability sampling methods.
This document discusses conceptualization, operationalization, and measurement in statistics. It defines conceptualization as communicating concepts about external realities through concepts, constructs, dimensions, and indicators. Operationalization is selecting observable phenomena to represent abstract concepts through attributes, variables, and levels of measurement including nominal, ordinal, interval, and ratio. Measurement involves assigning data to these levels based on their qualities and relationships.
This document discusses measurement and different types of scales used in measurement. It defines measurement as assigning numbers to objects or events according to rules. There are four main types of scales - nominal, ordinal, interval, and ratio scales. Nominal scales simply classify items into categories while ordinal scales rank items. Interval scales have equal intervals but no true zero point, and ratio scales have a true zero point and allow comparisons of magnitudes. The document also discusses sources of error in measurement, tests of valid measurement including validity and reliability, and the relationship between reliability and validity.
The study aimed to describe classroom discipline problems identified by student teachers and their strategies for addressing these issues, with no hypotheses stated given the descriptive nature of the research. Student teachers provided open-ended responses which were analyzed using content analysis to generate five categories of discipline problems and strategies. The sample was described including demographics like gender, grade levels, age, and percent who were parents.
This document provides an overview of psychological research. It defines research as a careful, systematic study to establish facts or principles. Psychology is defined as the study of mental processes and behavior. There are three main types of psychological research: correlational research, descriptive research, and experimental research. Researchers use tools like questionnaires, interviews, observation, and checklists. The purpose of research is to describe behavior, understand why events occur, and apply knowledge to problems.
This document discusses different types of research designs used in psychology, including correlational research, quasi-experimental research, and problems to look for in research studies. It provides examples of each type of research design. Correlational research seeks to establish relationships between variables without manipulation. Quasi-experimental research blends correlation and experimental approaches by examining interactions between individual differences and manipulations. Problems to look for include confounds, nonrandom sampling, failure to replicate, and lack of comparison groups.
1. Sampling error occurs when statistical characteristics estimated from a sample differ from the true population parameters, since samples do not include all members of the population.
2. SPSS Statistics is statistical analysis software used for tasks like survey deployment, data mining, text analytics, and collaboration. It was originally called the Statistical Package for the Social Sciences.
3. Empirical research uses direct observation or experience to gain knowledge, relying on evidence from data collected through experience or experimentation. Both qualitative and quantitative analysis can be used.
The document discusses different types of research methods and designs, including experimental, quasi-experimental, non-experimental, qualitative, and quantitative approaches. It provides examples of true experimental designs, quasi-experimental designs, and non-experimental designs. It also outlines the key differences between qualitative and quantitative research, such as qualitative research being inductive while quantitative research is deductive. Finally, it discusses developing research questions and hypotheses for different types of studies.
Inquiries & investigations week 5 6 activitesJojoDeLeon1
This document provides guidance on synthesizing a review of related literature. It begins by outlining the expected learning outcomes, which are to evaluate sources cited, identify gaps/themes/ideas, and write a synthesis. It then defines synthesis as going beyond critique to determine relationships among sources. The key steps are: 1) understand content and identify similarities, 2) review critically and identify differences, 3) synthesize to determine patterns and compare/contrast themes. A good synthesis makes connections between ideas, applying the research to a larger framework. It emphasizes the importance of higher-order thinking skills in writing an objective, logical, and focused synthesis that identifies what the research does and does not tell us.
There are two main types of composite measures - indexes and scales. Indexes use nominal level indicators that are given equal weight, while scales use continuous level indicators where each response contributes differently to the total score. Both can be weighted or unweighted. Constructing indexes and scales requires selecting valid items, examining relationships between items and constructs, and handling missing data. Common scale types include Thurstone, Likert, semantic differential, and Guttman scales. Typologies summarize variables into nominal categories but risk oversimplification. Validity and reliability are important concepts for measuring devices.
This document provides an overview of statistical analysis of questionnaire data. It discusses topics like questionnaire construction, data entry, reliability analysis using Cronbach's alpha, descriptive statistics for Likert scale items including frequencies, medians, interquartile ranges and box plots. It also covers composite scale analysis using means, standard deviations and comparisons between groups. An example is provided on assessing student satisfaction regarding teaching using 4 questionnaire items from 60 students. Results would be reported using tables and figures with interpretations.
This glossary provides definitions of many of the terms used in the guides to conducting qualitative and quantitative research. The definitions were developed by members of the research methods seminar taught by Mike Palmquist in the 1990s and 2000s at the Colorado State University.
The document discusses different scales of measurement used in research including nominal, ordinal, interval, and ratio scales. It also discusses key concepts in statistical analysis like validity, reliability, inferential statistics, the null hypothesis, t-tests, and analysis of variance (ANOVA). Specifically, it explains that t-tests are used to compare two means, the null hypothesis states there is no relationship between variables, and ANOVA computes the F-ratio to measure differences between groups.
The document discusses qualitative content analysis. It defines content analysis as the systematic classification and interpretation of text through coding and identifying themes. Content analysis allows researchers to understand social reality and explore meanings in a scientific manner. It can use inductive or deductive approaches to analyze data. Unique characteristics include flexibility in approaches and ability to extract manifest and latent meanings from text. Researchers use content analysis to describe message characteristics and identify themes. The process involves defining a research question, sampling material, developing a coding scheme of themes, coding the content, and analyzing results both qualitatively and quantitatively. Validity and reliability are also addressed.
eeMba ii rm unit-3.1 measurement & scaling aRai University
This document discusses various types of measurement scales used in business research including nominal, ordinal, interval, and ratio scales. It also describes commonly used scaling techniques such as rating scales, attitude scales, Thurstone scales, Likert scales, and semantic differential scales. The key aspects of valid and reliable measurement are highlighted, including selecting observable events, developing a mapping rule to assign numbers, and applying the rule consistently.
This document defines content analysis and discusses Kerlinger's definition. Kerlinger defines content analysis as a systematic, objective, and quantitative method for studying communication. It involves treating all content in an identical manner (systematic), allowing different researchers to obtain the same results (objective), and quantifying the analysis to aid precision (quantitative). The document also lists uses of content analysis, such as identifying what exists in media, comparing media to reality, testing hypotheses, and exploring media images of minority groups.
SOURCES OF ERROR AND SCALES OF MEASUREMENTashanrajpar
This document discusses different scales of measurement used in behavioral sciences research: nominal, ordinal, interval, and ratio scales. It provides examples of variables that fall under each scale. Nominal scales involve categorizing variables without rank. Ordinal scales involve ordering categories but without precise intervals between ranks. Interval scales have equal intervals between ranks and a true zero point. Ratio scales have all the properties of interval scales plus a meaningful zero point. The document also discusses sources of error in measurement, including issues related to respondents, measurers, and instruments.
This document discusses correlational research, which aims to describe relationships between variables and measure their strength. Correlational research observes how variables change together without manipulation. A correlation indicates the direction, form, and consistency of a relationship. Positive relationships involve variables changing in the same direction, while negative relationships involve opposite direction changes. Correlational designs can be explanatory, relating co-varying variables, or predictive, identifying predictors of an outcome variable. Correlational research allows investigating relationships but cannot determine causation due to low internal validity.
This document provides an overview of non-parametric statistics. It begins by explaining that non-parametric statistics do not specify conditions about a population's parameters and can be used when distributions are not normal or variables are measured nominally or ordinally. Some common non-parametric tests are then described for comparing independent groups (like the Mann-Whitney U test) or dependent groups (like the Wilcoxon matched-pairs test). The document concludes by contrasting parametric and non-parametric tests, noting that non-parametric tests are less powerful but also have fewer assumptions.
Commonly Used Statistics in Survey ResearchPat Barlow
This is a version of our "commonly used statistics" presentation that has been modified to address the commonly used statistics in survey research and analysis. It is intended to give an *overview* of the various uses of these tests as they apply to survey research questions rather than the point-and-click calculations involved in running the statistics.
This document provides an overview of quantitative data analysis, including different data types like nominal, ordinal, interval, and ratio scales. It also describes common statistical analyses like descriptive statistics to analyze frequencies, means, medians and modes, as well as inferential statistics for comparison tests, correlation, and regression. Specific comparison tests are outlined for within-groups, between-groups, and mixed subject designs depending on the number of groups being compared.
The document discusses measurement, scaling, and sampling in research. It defines measurement instruments and explains that validity and reliability are key indicators of quality. Scaling is discussed as a way to quantify qualities using numerical values. The document also defines sampling as selecting a subset of a population for observation, and explains probability and non-probability sampling methods.
This document discusses conceptualization, operationalization, and measurement in statistics. It defines conceptualization as communicating concepts about external realities through concepts, constructs, dimensions, and indicators. Operationalization is selecting observable phenomena to represent abstract concepts through attributes, variables, and levels of measurement including nominal, ordinal, interval, and ratio. Measurement involves assigning data to these levels based on their qualities and relationships.
This document discusses measurement and different types of scales used in measurement. It defines measurement as assigning numbers to objects or events according to rules. There are four main types of scales - nominal, ordinal, interval, and ratio scales. Nominal scales simply classify items into categories while ordinal scales rank items. Interval scales have equal intervals but no true zero point, and ratio scales have a true zero point and allow comparisons of magnitudes. The document also discusses sources of error in measurement, tests of valid measurement including validity and reliability, and the relationship between reliability and validity.
The study aimed to describe classroom discipline problems identified by student teachers and their strategies for addressing these issues, with no hypotheses stated given the descriptive nature of the research. Student teachers provided open-ended responses which were analyzed using content analysis to generate five categories of discipline problems and strategies. The sample was described including demographics like gender, grade levels, age, and percent who were parents.
This document provides an overview of psychological research. It defines research as a careful, systematic study to establish facts or principles. Psychology is defined as the study of mental processes and behavior. There are three main types of psychological research: correlational research, descriptive research, and experimental research. Researchers use tools like questionnaires, interviews, observation, and checklists. The purpose of research is to describe behavior, understand why events occur, and apply knowledge to problems.
This document discusses different types of research designs used in psychology, including correlational research, quasi-experimental research, and problems to look for in research studies. It provides examples of each type of research design. Correlational research seeks to establish relationships between variables without manipulation. Quasi-experimental research blends correlation and experimental approaches by examining interactions between individual differences and manipulations. Problems to look for include confounds, nonrandom sampling, failure to replicate, and lack of comparison groups.
1. Sampling error occurs when statistical characteristics estimated from a sample differ from the true population parameters, since samples do not include all members of the population.
2. SPSS Statistics is statistical analysis software used for tasks like survey deployment, data mining, text analytics, and collaboration. It was originally called the Statistical Package for the Social Sciences.
3. Empirical research uses direct observation or experience to gain knowledge, relying on evidence from data collected through experience or experimentation. Both qualitative and quantitative analysis can be used.
The document discusses different types of research methods and designs, including experimental, quasi-experimental, non-experimental, qualitative, and quantitative approaches. It provides examples of true experimental designs, quasi-experimental designs, and non-experimental designs. It also outlines the key differences between qualitative and quantitative research, such as qualitative research being inductive while quantitative research is deductive. Finally, it discusses developing research questions and hypotheses for different types of studies.
Inquiries & investigations week 5 6 activitesJojoDeLeon1
This document provides guidance on synthesizing a review of related literature. It begins by outlining the expected learning outcomes, which are to evaluate sources cited, identify gaps/themes/ideas, and write a synthesis. It then defines synthesis as going beyond critique to determine relationships among sources. The key steps are: 1) understand content and identify similarities, 2) review critically and identify differences, 3) synthesize to determine patterns and compare/contrast themes. A good synthesis makes connections between ideas, applying the research to a larger framework. It emphasizes the importance of higher-order thinking skills in writing an objective, logical, and focused synthesis that identifies what the research does and does not tell us.
This document discusses concepts, variables, and measurement in research. It defines key terms like concept, variable, and dimension. It explains different levels of measurement like nominal, ordinal, interval, and ratio scales. It also discusses different types of variables, constructs, hypotheses, and sampling. Measurement methods like questionnaires, observation, and triangulation are presented. The relationship between concepts, indicators, and dimensions is explained. The importance of operationalizing variables is highlighted.
The document provides an overview of quantitative and qualitative data analysis methods. It discusses the differences between quantitative and qualitative data/analysis, as well as various statistical and coding techniques used in each method. For quantitative analysis, it covers descriptive statistics, inferential statistics, univariate analysis including measures of central tendency and variation, bivariate analysis including crosstabulation and correlation, and multivariate analysis including elaboration models. For qualitative analysis, it discusses social anthropological versus interpretivist approaches, the relationship between data and ideas, strengths and weaknesses, and typical analysis steps including coding, data reduction, and conclusion drawing.
The document discusses various techniques for analyzing different types of data in research. It describes statistical procedures like parametric and non-parametric statistics that have assumptions about the type of data. Qualitative data analysis involves deriving categories from the text or applying existing systems. Descriptive research uses frequencies, central tendencies, and variabilities to analyze data. Correlational research examines relationships between variables using correlations. Multivariate research analyzes multiple dependent and independent variables simultaneously using multiple regression, discriminant analysis, and factor analysis. Experimental research compares groups using t-tests and analyzes more than two groups with one-way ANOVA.
This document provides an overview of key concepts in educational research methods. It discusses the purposes of educational research as explaining educational issues and helping to understand, predict, improve, and generate new questions. It also outlines the main steps of scientific inquiry as recognizing a problem, collecting information, analyzing data, and stating implications. The document then distinguishes between basic, applied, and evaluation research. It explains quantitative and qualitative research methods and various research designs including descriptive, correlational, causal-comparative, experimental, and historical. It also discusses sampling techniques, variables and scales of measurement, types of instruments, validity and reliability, and statistical analysis methods.
This document discusses quantitative research methods. It defines quantitative research as the systematic empirical investigation of observable phenomena via statistical techniques. The key aspects covered are that quantitative research aims to develop and test mathematical models and hypotheses about variables, uses statistics to generalize findings and establish causal relationships, and seeks to objectively measure variables using structured data collection techniques like surveys, experiments, and questionnaires to facilitate numerical analysis and interpretation of results.
This document discusses different methods for collecting and analyzing quantitative and qualitative data in research. It describes the following key points:
- Quantitative data involves numerical data that can be statistically analyzed, while qualitative data involves non-numerical data like text.
- Common statistical analyses for quantitative data include descriptive statistics like frequencies, means, and variability measures. Correlational research examines relationships between variables. Experimental research compares means between groups using t-tests or analyzes variance between groups using ANOVA.
- Qualitative data analysis involves deriving categories from text and identifying patterns. It requires intuition to understand the data.
- The document outlines various multivariate techniques like regression, discriminant analysis, and factor analysis that can analyze multiple
Research is undertaken to answer questions through a systematic process. It aims to find solutions to problems, understand causes and effects, and assess outcomes. High quality research builds upon previous work, can be replicated, applies to other contexts, and generates logical, testable hypotheses. Research uses both theoretical frameworks and empirical observations to explore relationships between variables in a probabilistic and often causal manner. Studies can be descriptive, relational, or causal, and take a cross-sectional or longitudinal approach over time. Hypotheses make specific, testable predictions about expected relationships between independent and dependent variables. Research relies on quantitative and qualitative data from both primary and secondary sources.
Three key points about the document:
1. The document discusses correlational research and survey research. It defines correlational research as studying relationships between two or more variables without influencing them. Survey research involves collecting data through questionnaires or interviews to answer questions about populations.
2. The basic steps of correlational research are discussed, including problem selection, sampling, instrumentation, design/procedures, data collection/analysis. Threats to internal validity like subject characteristics and mortality are also covered.
3. The different types of surveys - cross-sectional, longitudinal (trend, cohort, panel), are defined. The key steps in conducting survey research are outlined, such as defining the problem, identifying the population,
1. The document discusses correlational and survey research methods. It defines correlational research as studying relationships between two or more variables without influencing them.
2. The basic steps in correlational research are outlined as problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis and interpretation.
3. Survey research is defined as collecting data using questionnaires or interviews to answer questions about populations. Cross-sectional and longitudinal survey designs are described.
Three key points about the document:
1. The document discusses correlational research and survey research methods. It defines correlational research as studying relationships between two or more variables without influencing them. Survey research involves collecting data through questionnaires and interviews to answer hypotheses or questions about populations.
2. The basic steps of correlational research are outlined, including problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis. Threats to internal validity like subject characteristics, location, instrumentation, and mortality are also discussed.
3. The document provides details on longitudinal and cross-sectional survey designs. The key types of longitudinal surveys - trend studies, cohort studies, and panel studies - are explained.
1. The document discusses correlational and survey research methods. It provides definitions and purposes of correlational research, including describing relationships between variables and using relationships to predict outcomes.
2. The basic steps of correlational research are outlined, including problem selection, sampling, instrumentation, design and procedures, data collection, and data analysis. Threats to internal validity like subject characteristics and mortality are also discussed.
3. Survey research is defined as collecting data using questionnaires to answer questions about populations. Different types of surveys like cross-sectional, longitudinal, trend, cohort and panel studies are explained. The key steps in conducting survey research are identified.
Three key points about the document:
1. The document discusses correlational research and survey research. It defines correlational research as studying relationships between two or more variables without influencing them. Survey research involves collecting data through questionnaires or interviews to answer hypotheses or questions about populations.
2. The basic steps of correlational research are discussed, including problem selection, sampling, instrumentation, design/procedures, data collection/analysis. Threats to internal validity like subject characteristics and mortality are also covered.
3. The different types of surveys - cross-sectional, longitudinal (trend, cohort, panel), are defined. The key steps of conducting survey research are outlined, such as defining the problem, identifying the
Chapter2 the methods_of_psychological_researchayeshakhan1000
The document outlines the scientific method and different types of psychological research methods. It discusses descriptive research which observes and describes behavior without explaining causes. Correlational research predicts behavior by assessing relationships between variables. Experimental research aims to explain behavior by manipulating independent variables and measuring effects on dependent variables. The document also covers ethical considerations like informed consent and debriefing when involving human participants in research.
Qualitative Research and Family Psychology by Jane F. GilgunJim Bloyd
Abstract: Qualitative approaches have much to offer family psychology. Among the uses for qualitative methods are theory building, model and hypothesis testing, descriptions of lived experiences, typologies, items for surveys and measurement tools, and case examples that answer ques- tions that surveys cannot. Despite the usefulness of these products, issues related to gener- alizability, subjectivity, and language, among others, block some researchers from appreci- ating the contributions that qualitative methods can make. This article provides descriptions of procedures that lead to these useful products and discusses alternative ways of under- standing aspects of qualitative approaches that some researchers view as problematic.
Gilgun, J. (2005). Qualitative Research and Family Psychology. Journal of Family Psychology, 19(1), 40-50. doi:10.1037/0893-3200.19.1.40
Social research is a research conducted by social scientists following a systematic plan. Social research methodologies can be classified as quantitative and qualitative.
·Quantitative Data Analysis StatisticsIntroductionUnd.docxlanagore871
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Quantitative Data Analysis: Statistics
Introduction
Understanding the use of basic statistical strategies is part of being a critical consumer of published research literature. Unless they plan to conduct research themselves, it is not as important for counselors to understand the mathematical calculations of the statistical techniques as it is to be able to recognize the names of the common ones and what kind of information they provide. There are several commercially-available software packages for analyzing quantitative data, one of which is described in detail in Chapter 14 of
Counseling Research: Quantitative, Qualitative, and Mixed Methods
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Descriptive and Inferential Statistics
In quantitative studies, statistical techniques are used for data analysis. The two main categories of statistics are descriptive and inferential. Descriptive statistics are used to summarize the data. Some common descriptive statistics are the measures of central tendency: the mean, median, and mode. They provide information about where the middle is in distribution of scores. On the normal distribution, the mean, median, and mode are the same. Distributions are said to be skewed when extreme scores draw the mean away from the middle of the distribution. Measures of variability, such as the range, variance, and standard deviation, provide information about how widely a distribution of scores is dispersed (Erford, 2015, p. 250). The standard deviation is a measure of how the scores cluster around the mean. The greater the standard deviation, the greater the spread of scores.
Toggle DrawerHide Full Introduction
Inferential statistics are used to make inferences from the sample to the population. All inferential statistical procedures are based on probability theory. They are used to test hypotheses. Three commonly used inferential statistics are chi square, t-test, and analysis of variance (ANOVA). Chi square is used with nominal data to determine if the observed expected frequency differs significantly from the expected frequency. A t-test is used to determine whether there is a statistically significant difference between the means of two groups. ANOVA is used to determine whether there is a statistically significant difference between the means of three or more groups.
Statistical Significance
When a quantitative study tests a hypothesis, it is technically the null hypothesis being tested. The null hypothesis says there is no difference between the groups, or relationship between the variables (depending on the research design). If the statistical procedure indicates there is statistical significance, the null hypothesis is rejected, meaning that the probability is high that there really is a group difference or strong relationship between the variables.
Rejecting the null hypothesis is not equivalent to proving the research or alternative hypothesis. Researchers can embrace the research hypothesis as one plausible explanation, but because only .
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This PowerPoint compilation offers a comprehensive overview of 20 leading innovation management frameworks and methodologies, selected for their broad applicability across various industries and organizational contexts. These frameworks are valuable resources for a wide range of users, including business professionals, educators, and consultants.
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Innovation Management Frameworks: Your Guide to Creativity & Innovation
308
1. 3rd step in intro significant of the topic
comes up where
abstract
explains to the reader the
basic purpose for writing
the paper and any relevant
results and conclusions
abstract variables
variables that change or differ
overt time or across
situations or contexts
(communication satisfaction
or self-disclosure)
accuracy, authority,
currency, objectivity
in order to check the
validity of a web source you
must look for what?
accurate, self contained,
concise
abstracts should be what
2. alters parameter of
statistical test
limitation of unidirectional
hypotheses
anchors strongly agree, agree,
disagree, are what
antecedent and consequent what do propositions
consist of
apparatus
describes the materials used
int eh specific study, what
they are (explain), if yo built
them include directions
argument
a series of propositions in
which one follows logically
as a conclusion from the
others
3. attribute
refer to the specific category
of a variable i.e. biological sex
includes the attributes male
or female categorical
axiom generally accepted
principles or rules
Berlo who said communication is
like a river
bimodial, multimodial two modes, more than two
modes
bipolar scale semantic differential scale
is also known as
4. categorical attributes are
cause
manipulation is the blank in
a cause and effect
relationship
chronbach's alpha
reliability
most commonly used single
administration scalar
reliability used by social
scientist
clever hans
an example of a scientist
not maintaining control of
extraneous variables
communication
the process by which one
person stimulates meaning in
the minds of another person
through verbal and nonverbal
messages
5. communication and mass
media complete
what is the most useful
database for com majors
communications
focuses on mediated
technology is used to
communicate messages to
large numbers of people
comparison group (control)
which group in a study
receives no treatment (no
control condition)
concrete variables stable of consistent as in
biological sex or birth order
conditional or hypothetical what are the most
important propositions
6. confounding variables cannot always be prevented
but can be controlled
control
the process where an individual
both prevents personal biases from
interfering with the research study
and makes sure there are no other
explanations from what is seen in
the study
controlled the confounding variable is
what
criterion variable dependent variable is aka
data any record or observation
7. dependent variable measurement focuses on
which variable
describe, organize,
interpret data
statistics allows us to
descriptive statistics
are used to organize and
summarize information or
data (describes number of)
percentages
difference
the degree to which one
person or a group of people
are dissimilar from another
person or group of people
difference in kind
when two or more groups do
different things associated
with their groups (footballers
play football, cheerleaders
cheer)
8. difference of degree
when two groups have
differing degree of a variable
that they both display (look at
verbal aggression in football
players and cheerleaders)
directional research
questions
asks if there is a positive or
negative relationship
between two variables
do not
differences in kind
___________relate to
statistics
dyads
focuses on collecting
information about two
people involved in an
interpersonal relationship
dynaminc and changing human communication is
what
9. ecological fallacy
• a concept similar to stereotyping and
occurs when researchers assume that
because a participant is a member of a
specific group or culture that he or she
possesses all the same communication
characteristics as the group
empirical generalizations
an attempt to describe a
phenomenon based on what we
know about the phenomenon at
the time (basically based on
what we observed)
empirical, objective, and
controlled
observations must be what?
empiricism
science is only acceptable
insofar as the phenomenon in
question can be sensed (if you
don't have physical proof it
doesn't exist)
epistemology
ways of knowing or how we
know what we know,
through which framework
10. face or content, predictive
or concurrent, construct or
factorial
three types of validity
face validity
does not involve the use of a
correlation coefficient,
subjective type of validity, just
seeing if at face value people
false
on semantic differential
scales you have to write the
numbers
falsified
in order for a theory to be a
true theory it must have the
ability to be what?
fifth component a thesis is what component
of an introduction
11. frequency then percentile sex is indicated with
greater than
on a positively skewed set of
data the mean will be
___________the median
or mode
hasty generalization
generalizing something
when there is not enough
evidence to make the
generalization.
hypotheses
the conclusion that occurs
at the end of a series of
propositions is a what?
Ibn al-Haytham who created the first
scientific method
12. importance of topic, it's a
relatable phenomena, why
we should care
significane of the topic
section should show
increase reliability, ****
over validity
response sets typically do
what to reliability
independent variable manipulation primarily
focuses on which cariable
individuals
these studies focus on
temperament, personality or
communication traits or the
research could focus on specific
communication strategies or
behaviors selected by a person
individuals, dyads, groups,
organizations
most communication
research examines
communication in these
four units of analysis
13. inferential statistics
Used in order to make
prediction draw conclusions
about the larger group based
upon the smaller group.
interpret the results,
connect to lit review,
explain how knowledge has
changed as a result of this
results discussion should do
what?
interval
multiple items on a
semantic differential scale
are what?
interval
multiple items on a likert
scale are thought to be what
type of measures
interval level variables
allow a researcher to rank
order items and to compare
the magnitude of difference
between each category
14. interval level variables
likert, semantic differential,
and scalagram scales
measure what?
interval variables
the personal report of
public speaking anxiety
sues what type of variables
intervening variables
additional variables whose
presence may impact the
relationship between he
dependent and independent
variables
isomorphic
identity or similarity form, ties
results to reality and shows
that what you are measuring is
actually what you think you
are measuring
italicized anchors should always be
what according to apa
15. item construction, length of
instrument, administration
of test
three ways to improve
reliabiity
kind and degree two types of differences
latent variable
(hypothetical variable)
a variable that a researcher
cannot directly observe but is
inferred from other variables
that are observable and
measured directly
less than
negatively skewed data the
mean will be_______ the
median or mode
likert scale
strongly agree agree
disagree strongly disagree
scale type
16. likert scales
designed to measure
attitudes, start with a
descriptive sentence and then
offer a range of possible
choices
likert, semantic differential,
scalagram scales
list three types of scales
used to measure interval
level variables
lit review developing rationale is
where
literature review
the selection of available documents (published
and unpublished) on a given topic that contain
information, opinions, data and evidence written
from a particular point of view that aids in a
reader's understanding of pertinent review prior to
examing the results in a new study. A series of brief
research papers about various related topics.
manipulated the independent variable is
17. manipulation,
measurement, and control
primary mission of research
is the (blank) of variables
manipulation,
measurement, control
what are the three pillars of
experimental research
may skew data, outliers limitations of mean
mean
most powerful measure of
central tendency for
statistical analysis
mean and standard
deviation
when reporting the
participants ages you must
include
18. mean, median, mode measures of central
tendancy
measured the dependent variable is
measurement
the systematic observation
and assignment of numbers
to phenomena according to
rules
median
best measure of central
tendency when looking at
data with extreme scores
missing info in a specific
context, different sample
populations, out of date
information, theoretically
driven
types of research gaps
19. mode value that occurs most
frequently in a data set
mode
most general and least
precise of all descriptive
statistics (measures of
central tendency)
mutually exclusive,
equivalent, exhaustive
rules for nominal level
variable categories
mutually exclusive, logically
ordered, balanced to
represent amount of
characteristic possessed by
participant
ordinal variables must be
negatively skewed the tail is longer on the left
20. neutral
when two variables have no
relationship with each
other they are
nominal variables
democrat/republican/other,
occupation, religion,
ethnicity are all examples of
nominal, ordinal, interval,
ratio
four variable levels
non random assignment
(intact group)
groups where participants
choose their own group as
in playing golf or not, you
see, etc
null hypothesis represents there is NO
relationship
21. null hypothesis researches always test
which hypothesis
numerical values are
objective
scientist making sure that
their personal emotions
predictions and biases do not
get in the way is an example
of being what
objective
scientific research is more
what (objective or
subjective)
observations
portion of study where
researcher attempts to test
the hypothesis
22. one tailed (unidirectional)
hypothesis
a hypothesis that predicts the
specific nature of the
relationship or difference as in
this is positively correlated
with this or negatively
operationalize variables defining things allows a
research to do what?
ordinal single items on semantic
differential scales are what
ordinal level variables
allow us to rank order
attributes with regard to
which has less versus which
has more (variable type)
orinal
individual item on a likert
scale is though to be what
type of measurement
23. parameter the average of an entire
population
participants, apparatus,
procedure, instrumentation
methods section is broken
down into what four areas
Popper's
which versions of the
scientific method is most
commonly used today
positive, negative, neutral
relationships between
variables are typically
either
positively skewed
the tail of the curve is
longer on the right side of
distribution
24. predict the future, be
capable of being falsified
a theory must
preview
author lists the specific
sections that he or she is
planning on covering in the
literature review of the paper
proposition
a statement that either
confirms or denies
something
qualitative
modes is best used to
measure what kind of data?
(sex, political affiliation,
class rank)
qualitative
nominal level variables
have what type of
characteristics?
25. qualitative (interpretive or
critical)
the humanism
epistemological approach is
most closely correlated with
what type of research
quantitative
scalar reliability is the most
common form of reliability
in what type of research
quantitative interval level variables have
what characteristics
quantitative and qualitative
ordinal level variables have
what kind of
characteristics?
quantitive
the scientific
epistemological approach is
more closely related with
what type of research?
26. range most general measure of
variability
range
the distance between the
larges value and the lowest
value is known as what?
range
does not tell us how each
score in the distribution
differs from the mean
range, variance, standard
deviation
measures of variability
(dispersion)
relationship
refers to the
correspondence or
connection between two
variables.
27. reliability
the accuracy that a measure
has in producing stable,
consistent measurements
reliability, validity, faking
responses, response sets,
bad items
problems with measure
research the use of the scientific
method to answer questions
research question
an explicit question a
research asks about a
variable of interest
response set
any tendency that causes a
person to give different
responses to test items than
he or she would if the item was
presented in a different form
28. results discussion,
limitations, future research
the discussions section is
broken down into which
three sections
retell arguments made results discussion should
NOT
sample subset of a population
scalar reliability
the most common form of
reliability assessed in
quantitative research
scalar reliability
the generalized attitude
measures scale is an
example of what type of
reliability?
29. scalogram scale
used to determine when
people cut themselves off in
relation to a specific belief
scientific and humanism two most prominent
epistemological approaches
scientific method
in order for humanistic
research to be scientific it
must follow what?
semantic differential scale
good/bad, happy/sad,
eventful/uneventful,
positive/negative scale as in
good (1)_________bad(7)
semantic differential scales
ask respondents to rate
their opinions on a linear
scale between two
endpoints
30. seven most common number of
steps on a likert scale
sextus empiricus who rejected the belief in
the divine nature of illness
Shannon and Weaver
model
the SMCR model is also
known as
skewness the curve of your data
small populations
qualitative research is
typically used to explain
what population
31. standard deviation
tells us how far each score
differs from the average and to
other scores and provides us
with a more descriptive and
easier to use number
standard deviation the square root of the
variance is known as what?
subjective
qualitative research is more
what (objective or
subjective)
subjective
knowledge arises out of the
researcher's own opinions and
perceptions and usually is only
applied to explain small
groups
subjects
when looking through a
database you should first
search what
32. sum of squares variance is dependent upon
what?
sum of squares and number
of participants
in order to calculate
variance you must know
what?
sweeping generalization an ecological fallacy is also
known as
syllogism
three propositions (two
premises and one
conclusion) is known as
test retest reliability
giving the same group of
people the same test at a
different time and seeing if
their results are similar
33. theories
a proposed explanation for how a set of
natural phenomena will occur, capable of
making predictions about the phenomena
for the future and capable of being
falsified through empirical observation
theories, predictions
(hypotheses) observations,
generalizations
steps in the scientific
method
theory
a proposed explanaton for how a set
of natural phenomena will occur,
capable of making predictions
about the phenomena for the future
and capable of being falsified
through empirical observation
thesis
a short declarative sentence
that explains to a reader the
purpose of the paper itself
two-tailed (or
bidirectional) hypothesis
predicts that there is a significant
difference or relationship but does
not indicate the speicifc nature of
the difference (which group would
have a higher score) or relationship
(positive or negative)
34. validity
does your tool measure
what it was intended to
measure
validity
the degree to which an
instrument measures what
it is intended to measure
values
refers to the numerical
designation assigned to each
variable to allow for statistical
analysis. Click one for male
two for female numerical
variability (dispersion)
range, variance, and
standard deviation are all
measures of what?
variable any entity that can take on a
variety of different values
35. variance
how wide or spread out a
distribution is or the average
distance of the scores for an
interval or ratio scale from the
mean in square units
vary a variable must do what
why there could be a
difference between
variables
rationale for a research
question explains what