Variables and concepts are important tools in theoretical research. A concept is an abstract idea that combines related observations, while a variable is an empirical measure that can take different values. There are different types of variables, including independent, dependent, and intervening variables that indicate causal relationships. Numerical variables can be discrete or continuous, while categorical variables involve mutually exclusive and exhaustive categories. Researchers define concepts and variables conceptually by relating them to other concepts, and operationally by specifying how to measure them. Propositions state relationships between concepts or variables but are not tested directly like hypotheses.
The document discusses different types of variables that are important in research, including independent, dependent, moderator, control, and intervening variables. It also describes different measurement scales used to measure variables, including nominal, ordinal, interval, and ratio scales. Key variables discussed include age, country, intelligence, and proficiency.
This document discusses correlational research designs. Correlational studies can show relationships between two variables to indicate cause and effect or predict future outcomes. There are three main types of correlational studies: observational research, survey research, and archival research. Correlational research allows analysis of relationships among many variables and provides correlation coefficients to measure direction and degree of relationships. Interpreting correlations involves scattergrams, correlation coefficients from -1 to 1, and determining explained variance through r-squared values. However, correlation does not necessarily prove causation as third variables could be the true cause.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
This document discusses experimental design in statistics. It defines experimental design as a planned interference by the researcher to manipulate events rather than just observe them. It discusses key principles of experimental design like replication and randomization. It also describes different types of experimental designs like completely randomized design, randomized block design, and Latin square design; and notes that researchers use experimental designs to make causal inferences and rule out alternative explanations. The goal of experimental design is to gain unambiguous information about what factors cause the effects being studied.
This document defines different types of variables that may be studied in research. It explains that independent variables are those that are manipulated by the researcher, while dependent variables are those affected by the independent variable. Examples are provided such as stress being an independent variable that could affect the dependent variable of mental state. Other variable types discussed include intervening variables, constant variables, and attribute variables. Tests are provided to help understand the difference between independent and dependent variables.
This document discusses variables, hypotheses, study types, and validity/reliability in research. It defines variables as characteristics that can take different values, and categorizes them as numerical, categorical, continuous, discrete, ordinal, and nominal. Hypotheses predict relationships between factors and problems that can be tested. Study types include descriptive studies, comparative/analytical studies, experimental studies, quasi-experimental studies, and before-after studies. Validity means measurements actually assess what is intended, while reliability means repeatability of findings.
This document discusses the measurement of variables in research. It defines a variable as a measurable characteristic that can take on different values. There are different types of variables, including independent variables, dependent variables, intervening variables, moderator variables, control variables, and extraneous variables. Variables can be measured at nominal, ordinal, interval, or ratio levels. The level of measurement determines what statistical analyses can be used. In conclusion, understanding how variables are defined and measured is important for conducting research.
This document provides an overview of quantitative research design. It defines quantitative research as the systematic investigation of numerical data to explain phenomena through statistical analysis and testing of theories. The key aspects covered include: variables are the basic building blocks and can be categorical or quantitative; variables can be independent, dependent, mediating or moderating; experimental research involves manipulating the independent variable while non-experimental research observes relationships; correlational research examines relationships between variables; and ex post facto research investigates causes retrospectively when manipulation is not possible. Experimental, quasi-experimental, single-case and meta-analysis are also discussed as research methods.
The document discusses different types of variables that are important in research, including independent, dependent, moderator, control, and intervening variables. It also describes different measurement scales used to measure variables, including nominal, ordinal, interval, and ratio scales. Key variables discussed include age, country, intelligence, and proficiency.
This document discusses correlational research designs. Correlational studies can show relationships between two variables to indicate cause and effect or predict future outcomes. There are three main types of correlational studies: observational research, survey research, and archival research. Correlational research allows analysis of relationships among many variables and provides correlation coefficients to measure direction and degree of relationships. Interpreting correlations involves scattergrams, correlation coefficients from -1 to 1, and determining explained variance through r-squared values. However, correlation does not necessarily prove causation as third variables could be the true cause.
Analysis of data is a process of inspecting, cleaning, transforming, and modeling data with the goal of discovering useful information, suggesting conclusions, and supporting decision-making.
This document discusses experimental design in statistics. It defines experimental design as a planned interference by the researcher to manipulate events rather than just observe them. It discusses key principles of experimental design like replication and randomization. It also describes different types of experimental designs like completely randomized design, randomized block design, and Latin square design; and notes that researchers use experimental designs to make causal inferences and rule out alternative explanations. The goal of experimental design is to gain unambiguous information about what factors cause the effects being studied.
This document defines different types of variables that may be studied in research. It explains that independent variables are those that are manipulated by the researcher, while dependent variables are those affected by the independent variable. Examples are provided such as stress being an independent variable that could affect the dependent variable of mental state. Other variable types discussed include intervening variables, constant variables, and attribute variables. Tests are provided to help understand the difference between independent and dependent variables.
This document discusses variables, hypotheses, study types, and validity/reliability in research. It defines variables as characteristics that can take different values, and categorizes them as numerical, categorical, continuous, discrete, ordinal, and nominal. Hypotheses predict relationships between factors and problems that can be tested. Study types include descriptive studies, comparative/analytical studies, experimental studies, quasi-experimental studies, and before-after studies. Validity means measurements actually assess what is intended, while reliability means repeatability of findings.
This document discusses the measurement of variables in research. It defines a variable as a measurable characteristic that can take on different values. There are different types of variables, including independent variables, dependent variables, intervening variables, moderator variables, control variables, and extraneous variables. Variables can be measured at nominal, ordinal, interval, or ratio levels. The level of measurement determines what statistical analyses can be used. In conclusion, understanding how variables are defined and measured is important for conducting research.
This document provides an overview of quantitative research design. It defines quantitative research as the systematic investigation of numerical data to explain phenomena through statistical analysis and testing of theories. The key aspects covered include: variables are the basic building blocks and can be categorical or quantitative; variables can be independent, dependent, mediating or moderating; experimental research involves manipulating the independent variable while non-experimental research observes relationships; correlational research examines relationships between variables; and ex post facto research investigates causes retrospectively when manipulation is not possible. Experimental, quasi-experimental, single-case and meta-analysis are also discussed as research methods.
This document discusses the development of health measurement scales and the importance of reliability and validity in scale development. It notes that if you cannot quantify something you are describing, your knowledge of it is limited. It then discusses the relationship between reliability and validity, and explains that reliability places an upper limit on validity, so higher reliability allows for higher maximum validity. The document emphasizes that the quality and usefulness of any measurement depends on establishing its validity and reliability.
This document provides an overview of research methodology. It discusses what research is, different types of research including quantitative, qualitative, descriptive, and longitudinal research. It also outlines the research process and covers topics such as research questions, hypotheses, data collection methods, analysis, and reporting. Research is defined as a systematic and organized way to find answers to questions. It is considered a more valid basis of knowledge than alternatives like authority, tradition, or personal experiences.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
This document defines key terms related to variables in research. It discusses that a variable is anything that can take on different values, such as gender or marital status. There are several types of variables: independent variables which are manipulated by the researcher; dependent variables which depend on the independent variables; moderator variables which influence the relationship between independent and dependent variables; intervening variables which link independent and dependent variables but cannot be directly measured; control variables which are kept constant during an experiment; and extraneous variables which are uncontrolled factors that could influence dependent variables. Research involves identifying these different types of variables to understand relationships and effects.
This document discusses different types of variables that are important to consider when studying relationships. It defines a variable as any characteristic that can vary, and a constant as a characteristic that is the same for all members of a group. The document outlines independent and dependent variables, quantitative and categorical variables, moderator variables, mediator variables, and extraneous variables. Understanding the relationships between these different types of variables is essential for explaining phenomena in research.
The document provides an overview of quantitative research methodology. It discusses key concepts including population, sampling, samples, and qualitative scales. Specifically, it defines population as any complete group with at least one characteristic in common. It explains that sampling is used to select a subset of a population for a study. The document also outlines different types of measurement scales in quantitative research including nominal, ordinal, interval, and ratio scales.
Correlational research studies relationships between two or more variables without manipulating them. It can be used to predict outcomes and explain behaviors. Correlational studies describe relationships through correlation coefficients and scatterplots. More complex techniques include multiple regression, discriminant analysis, factor analysis, path analysis, and structural modeling. Correlational research aims to understand relationships, not prove causation. Threats to internal validity like subject characteristics, history, and testing must be controlled.
difference between the qualitative and quantitative researcher, variables, co...laraib asif
This document summarizes the key differences between qualitative and quantitative research methods. Qualitative research aims to understand human behaviors and contexts through inductive analysis like interviews and observations, while quantitative research tests hypotheses through deductive analysis using numerical data and statistics. Some key differences include sample sizes (smaller for qualitative), data collection techniques (open-ended for qualitative vs. standardized for quantitative), and generalizability of findings (qualitative explores specific contexts while quantitative seeks broader application). The document also discusses mixed methods research, which combines qualitative and quantitative approaches to leverage their respective strengths.
This document provides an overview of qualitative research. It discusses the history and characteristics of qualitative research, including that it seeks to understand perspectives from local populations. The document outlines various qualitative methods like case studies, ethnography, and grounded theory. It also discusses issues in qualitative research such as gaining entry, selecting participants, and enhancing validity. Strategies to reduce bias like triangulation and examining outliers are presented.
This document summarizes key aspects of research philosophies and methodologies based on the Research Onion model. It discusses three philosophies outside the onion - ontology, epistemology, and axiology. For each layer of the onion, it defines important research concepts like objectivism, constructivism, positivism, deductive approaches, inductive approaches, and specific methodologies like experiments, surveys, case studies, grounded theory, ethnography, and archival research. The document provides concise yet thorough explanations of these fundamental elements to consider in planning and conducting research.
Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
This document defines and provides examples of different types of variables:
- Dependent variables are affected by independent variables. Independent variables are presumed to influence other variables.
- Intervening/mediating variables are caused by the independent variable and themselves cause the dependent variable.
- Organismic variables are personal characteristics used for classification.
- Control/constant variables are not allowed to change during experiments.
- Variables can also be interval, ratio, nominal/categorical, ordinal, dummy, preference, multiple response, or extraneous.
Definition of Hypothesis
Characteristics of Hypothesis
Types of Hypothesis
Roles/ Functions of Hypothesis
Importance of Hypothesis
Sources of Hypothesis
This document summarizes quantitative data analysis techniques for summarizing data from samples and generalizing to populations. It discusses variables, simple and effect statistics, statistical models, and precision of estimates. Key points covered include describing data distribution through plots and statistics, common effect statistics for different variable types and models, ensuring model fit, and interpreting precision, significance, and probability to generalize from samples.
Mixed-method research combines both qualitative and quantitative data and analysis techniques to explore research questions. It uses both methods simultaneously or sequentially. The rationale is that it brings rigor, richness, and triangulation to data collection and supports findings by using multiple data sources and methods. Researchers must justify using a mixed approach and ensure they have the expertise to deal with its complexities.
Concept, construct and variable by sajjad ahmad-upmSajjad Ahmad
This document discusses key concepts in research methods including concepts, constructs, variables, and different types of variables.
It defines a concept as an abstraction formed from generalizing particulars that helps understand categories. A construct is a focused abstract idea inferred from observable phenomena. A variable is a factor or aspect that can be measured and varies in values.
The document outlines different types of variables including attribute and active variables, dependent and independent variables, qualitative and quantitative variables, moderator and combined variables. It also distinguishes between continuous and categorical variables.
This document summarizes four scales of measurement used in research methodology: nominal, ordinal, interval, and ratio scales. Nominal scales classify data into categories without order. Ordinal scales place variables in order from highest to lowest. Interval scales show the distance between measures and have an arbitrary zero point. Ratio scales have all the properties of the previous scales and also have an absolute zero, allowing for absolute comparisons and calculations.
This document discusses different types of research designs used in experimental research. It begins by defining research and outlining the key characteristics of systematic, logical, empirical, reductive, and replicable research. It then presents a continuum of research designs ranging from analytical to experimental. Several types of experimental designs are discussed in detail, including true experimental designs involving manipulation, control and randomization, as well as quasi-experimental and pre-experimental designs that lack one or more of these elements. Specific true experimental designs explained include post-test only, pretest-posttest, Solomon four-group, factorial, randomized block, and crossover designs. Quasi-experimental designs covered are nonrandomized control group and time-series designs. The
This document discusses concepts, constructs, and conceptual systems. It defines a concept as a generic idea or thought developed from experiences that are used to make sense of the world. Concepts are the building blocks of thinking. Constructs refer to higher order concepts that group concepts together at a higher level of abstraction. Variables are a type of construct that have different levels or values. Conceptual systems link concepts together to represent relationships and provide understanding of reality by identifying, organizing, and explaining phenomena. The goal of conceptual systems is to achieve understanding to satisfy goals of satisfaction and control.
This document defines and explains key concepts in research methods. It defines a concept as something that helps understand a category or phenomenon. A construct is an abstract idea inferred from observable phenomena. A variable is a factor or aspect that can be measured, such as demographic or economic variables in a study of villages. The document outlines different types of variables, including dependent and independent variables; experimental and measured variables; active and assigned variables; qualitative and quantitative variables; and moderator and combined variables. It provides an example of each type of variable.
This document discusses the development of health measurement scales and the importance of reliability and validity in scale development. It notes that if you cannot quantify something you are describing, your knowledge of it is limited. It then discusses the relationship between reliability and validity, and explains that reliability places an upper limit on validity, so higher reliability allows for higher maximum validity. The document emphasizes that the quality and usefulness of any measurement depends on establishing its validity and reliability.
This document provides an overview of research methodology. It discusses what research is, different types of research including quantitative, qualitative, descriptive, and longitudinal research. It also outlines the research process and covers topics such as research questions, hypotheses, data collection methods, analysis, and reporting. Research is defined as a systematic and organized way to find answers to questions. It is considered a more valid basis of knowledge than alternatives like authority, tradition, or personal experiences.
A measurable characteristic that varies and may change from group to group, person to person, or even within one person over time.
Variable is a logical grouping of attributes, characteristics or qualities that describe an object. It may be either height, weight, anxiety levels, body temperature, income and so on.
Variable is frequently used in quantitative research projects pertinent to define and identify variables.
A variable incites excitement in any research than constants as it facilitate accurate explanation of relationship between the variables.
This document defines key terms related to variables in research. It discusses that a variable is anything that can take on different values, such as gender or marital status. There are several types of variables: independent variables which are manipulated by the researcher; dependent variables which depend on the independent variables; moderator variables which influence the relationship between independent and dependent variables; intervening variables which link independent and dependent variables but cannot be directly measured; control variables which are kept constant during an experiment; and extraneous variables which are uncontrolled factors that could influence dependent variables. Research involves identifying these different types of variables to understand relationships and effects.
This document discusses different types of variables that are important to consider when studying relationships. It defines a variable as any characteristic that can vary, and a constant as a characteristic that is the same for all members of a group. The document outlines independent and dependent variables, quantitative and categorical variables, moderator variables, mediator variables, and extraneous variables. Understanding the relationships between these different types of variables is essential for explaining phenomena in research.
The document provides an overview of quantitative research methodology. It discusses key concepts including population, sampling, samples, and qualitative scales. Specifically, it defines population as any complete group with at least one characteristic in common. It explains that sampling is used to select a subset of a population for a study. The document also outlines different types of measurement scales in quantitative research including nominal, ordinal, interval, and ratio scales.
Correlational research studies relationships between two or more variables without manipulating them. It can be used to predict outcomes and explain behaviors. Correlational studies describe relationships through correlation coefficients and scatterplots. More complex techniques include multiple regression, discriminant analysis, factor analysis, path analysis, and structural modeling. Correlational research aims to understand relationships, not prove causation. Threats to internal validity like subject characteristics, history, and testing must be controlled.
difference between the qualitative and quantitative researcher, variables, co...laraib asif
This document summarizes the key differences between qualitative and quantitative research methods. Qualitative research aims to understand human behaviors and contexts through inductive analysis like interviews and observations, while quantitative research tests hypotheses through deductive analysis using numerical data and statistics. Some key differences include sample sizes (smaller for qualitative), data collection techniques (open-ended for qualitative vs. standardized for quantitative), and generalizability of findings (qualitative explores specific contexts while quantitative seeks broader application). The document also discusses mixed methods research, which combines qualitative and quantitative approaches to leverage their respective strengths.
This document provides an overview of qualitative research. It discusses the history and characteristics of qualitative research, including that it seeks to understand perspectives from local populations. The document outlines various qualitative methods like case studies, ethnography, and grounded theory. It also discusses issues in qualitative research such as gaining entry, selecting participants, and enhancing validity. Strategies to reduce bias like triangulation and examining outliers are presented.
This document summarizes key aspects of research philosophies and methodologies based on the Research Onion model. It discusses three philosophies outside the onion - ontology, epistemology, and axiology. For each layer of the onion, it defines important research concepts like objectivism, constructivism, positivism, deductive approaches, inductive approaches, and specific methodologies like experiments, surveys, case studies, grounded theory, ethnography, and archival research. The document provides concise yet thorough explanations of these fundamental elements to consider in planning and conducting research.
Data analysis is a process that involves gathering, modeling, and transforming data with the goal of highlighting useful information, suggesting conclusions, and supporting decision making. It describes several major techniques for data analysis, including correlation analysis, regression analysis, factor analysis, cluster analysis, correspondence analysis, conjoint analysis, CHAID analysis, discriminant/logistic regression analysis, multidimensional scaling, and structural equation modeling.
This document defines and provides examples of different types of variables:
- Dependent variables are affected by independent variables. Independent variables are presumed to influence other variables.
- Intervening/mediating variables are caused by the independent variable and themselves cause the dependent variable.
- Organismic variables are personal characteristics used for classification.
- Control/constant variables are not allowed to change during experiments.
- Variables can also be interval, ratio, nominal/categorical, ordinal, dummy, preference, multiple response, or extraneous.
Definition of Hypothesis
Characteristics of Hypothesis
Types of Hypothesis
Roles/ Functions of Hypothesis
Importance of Hypothesis
Sources of Hypothesis
This document summarizes quantitative data analysis techniques for summarizing data from samples and generalizing to populations. It discusses variables, simple and effect statistics, statistical models, and precision of estimates. Key points covered include describing data distribution through plots and statistics, common effect statistics for different variable types and models, ensuring model fit, and interpreting precision, significance, and probability to generalize from samples.
Mixed-method research combines both qualitative and quantitative data and analysis techniques to explore research questions. It uses both methods simultaneously or sequentially. The rationale is that it brings rigor, richness, and triangulation to data collection and supports findings by using multiple data sources and methods. Researchers must justify using a mixed approach and ensure they have the expertise to deal with its complexities.
Concept, construct and variable by sajjad ahmad-upmSajjad Ahmad
This document discusses key concepts in research methods including concepts, constructs, variables, and different types of variables.
It defines a concept as an abstraction formed from generalizing particulars that helps understand categories. A construct is a focused abstract idea inferred from observable phenomena. A variable is a factor or aspect that can be measured and varies in values.
The document outlines different types of variables including attribute and active variables, dependent and independent variables, qualitative and quantitative variables, moderator and combined variables. It also distinguishes between continuous and categorical variables.
This document summarizes four scales of measurement used in research methodology: nominal, ordinal, interval, and ratio scales. Nominal scales classify data into categories without order. Ordinal scales place variables in order from highest to lowest. Interval scales show the distance between measures and have an arbitrary zero point. Ratio scales have all the properties of the previous scales and also have an absolute zero, allowing for absolute comparisons and calculations.
This document discusses different types of research designs used in experimental research. It begins by defining research and outlining the key characteristics of systematic, logical, empirical, reductive, and replicable research. It then presents a continuum of research designs ranging from analytical to experimental. Several types of experimental designs are discussed in detail, including true experimental designs involving manipulation, control and randomization, as well as quasi-experimental and pre-experimental designs that lack one or more of these elements. Specific true experimental designs explained include post-test only, pretest-posttest, Solomon four-group, factorial, randomized block, and crossover designs. Quasi-experimental designs covered are nonrandomized control group and time-series designs. The
This document discusses concepts, constructs, and conceptual systems. It defines a concept as a generic idea or thought developed from experiences that are used to make sense of the world. Concepts are the building blocks of thinking. Constructs refer to higher order concepts that group concepts together at a higher level of abstraction. Variables are a type of construct that have different levels or values. Conceptual systems link concepts together to represent relationships and provide understanding of reality by identifying, organizing, and explaining phenomena. The goal of conceptual systems is to achieve understanding to satisfy goals of satisfaction and control.
This document defines and explains key concepts in research methods. It defines a concept as something that helps understand a category or phenomenon. A construct is an abstract idea inferred from observable phenomena. A variable is a factor or aspect that can be measured, such as demographic or economic variables in a study of villages. The document outlines different types of variables, including dependent and independent variables; experimental and measured variables; active and assigned variables; qualitative and quantitative variables; and moderator and combined variables. It provides an example of each type of variable.
construct and variables in research methodologyLavina Singh
This document discusses key concepts in understanding theory, including concepts, constructs, definitions, variables, propositions, and hypotheses. It defines concepts as bundles of meanings associated with events and objects, and notes that concepts provide a common ground for understanding. Constructs are specifically invented images or ideas used for research purposes. Definitions, especially operational definitions, provide a way to measure concepts. Variables represent constructs and can take various forms, and the relationship between variables can be independent, dependent, moderating, or extraneous. Propositions make statements about observable phenomena, while hypotheses are testable statements used for empirical research. A good hypothesis guides a study, limits what is studied, and is adequate, testable, and better than alternatives.
This document discusses key concepts in research variables including:
1) Independent variables are those that influence or explain variation in the dependent variable, while dependent variables are outcomes measured.
2) Variables can be categorical (taking a small set of values) or continuous (quantitative and measured on a scale).
3) Scales of measurement include nominal (labels), ordinal (ordered ranks), interval (equal intervals), and ratio (true zero point).
4) Identifying the independent and dependent variables and their properties (categorical vs. continuous, scale of measurement) is important for research questions.
5. Identifying variables and constructing hypothesisRazif Shahril
This document discusses identifying variables and constructing hypotheses in research methodology. It defines a variable as a concept that can be measured on different scales and provides examples of converting concepts into measurable variables. The document outlines different types of variables based on their role in a cause model or study design. It also describes nominal, ordinal, interval and ratio measurement scales. Finally, the document defines a hypothesis as a testable statement about the relationship between two or more variables and lists some functions of a hypothesis in focusing and guiding a research study.
This document discusses key concepts related to operationalizing and measuring variables in research. It defines constructs, operational definitions, scales of measurement, and different modalities of measurement. Specifically, it explains that researchers must operationally define abstract concepts by developing clear rules for measurement. Variables can be measured directly or indirectly, and must be defined conceptually and operationally. Measurement involves assigning numerals to objects or events according to specific rules and can occur on scales involving multiple observed items that capture an underlying latent variable. Researchers must also consider validity, reliability, and different modalities of measurement including self-report, physiological, and behavioral measures.
The document discusses concepts, operationalization, and measurement in quantitative research. It defines concepts as abstract ideas developed from theory, while variables are concrete and specific measures that allow concepts to be observed and measured in the real world. Operationalizing concepts involves defining variables that capture the dimensions of the concepts. Measures should avoid causes, consequences, and correlates, and instead directly assess the concept. The levels of measurement are nominal, ordinal, interval, and ratio. Validity and reliability are also discussed, where validity refers to accurately measuring concepts and reliability means consistency across repeated measures.
The document discusses different types of variables that may be present in research studies. It defines independent and dependent variables as those that are interrelated, with the independent variable being manipulated by the researcher to affect the dependent variable. Research variables are qualities or characteristics that are observed without manipulation. Demographic variables describe characteristics of study subjects, while extraneous variables are uncontrolled factors that may influence dependent variables. The document provides examples of how these different types of variables may appear in descriptive, exploratory, correlational, comparative, experimental, and quasi-experimental study designs.
The document discusses different types of variables in research:
1. Independent variables are factors that are manipulated by researchers to determine their effect on dependent variables.
2. Dependent variables are factors that are observed and measured to determine the effect of independent variables.
3. Moderating variables modify the relationship between independent and dependent variables.
4. Control variables are controlled by researchers to neutralize their potential effects on the relationship between independent and dependent variables.
5. Intervening variables theoretically affect phenomena but cannot be directly observed or manipulated.
It also discusses different types of data (qualitative, quantitative), measures of central tendency (mean, median, mode), and measures of variability (
Research is defined as a systematic investigation designed to develop or contribute to generalizable knowledge. It involves carefully defining problems, formulating hypotheses, collecting and organizing data, making deductions, reaching conclusions, and testing conclusions. The main objectives of research are to gain familiarity with phenomena, accurately portray characteristics, determine frequencies of occurrences, and test hypotheses of causal relationships between variables. In conclusion, research is a systematic and logical process that follows specified steps in a specified sequence according to a set of rules.
This document discusses a study that aimed to identify sources of knowledge for first-year students entering a gateway science course. Researchers interviewed 8 students to understand their epistemic beliefs and sources of knowledge. They found students drew knowledge from perceptual, memorial, deductive, inductive, and testimony-based sources. The researchers provided 10 recommendations for science instructors, including assessing students' epistemologies and ways of knowing, using evidence-based teaching methods to promote conceptual understanding and sophisticated epistemic beliefs, and employing authentic science activities and social learning tools.
The document discusses six ways to acquire knowledge:
1. Tenacity - Acquiring knowledge through habit or superstition without a mechanism for error correction.
2. Intuition - Acquiring knowledge through guesses or hunches that can be misleading without a way to determine accuracy.
3. Authority - Unquestioningly accepting knowledge from respected sources without a way to validate it.
4. Rationalism - Using logical reasoning which can reach incorrect conclusions if premises or steps are flawed.
5. Empiricism - Acquiring knowledge through subjective experience alone which lacks control and ignores unobserved cases.
6. Science - Acquiring knowledge through objective evidence and testing of hypotheses according to a standardized procedure open to scrutiny
This document discusses different types of variables and research designs. It defines constructs, indicators, and operational definitions. It also describes different types of variables like independent, dependent, attribute and extraneous variables. Finally, it explains quasi-experimental designs like non-equivalent groups, interrupted time series, and regression discontinuity designs. It also covers single-case designs like A-B-A, multiple baseline, and changing criterion designs. The document provides examples and diagrams to illustrate these research concepts and designs.
This document discusses different study designs used in research. It defines a study design as a specific plan for conducting a study that allows the investigator to translate a conceptual hypothesis into an operational one. The document outlines different types of study designs including descriptive studies, analytical observational studies like cross-sectional studies, case-control studies, and cohort studies, as well as experimental/interventional studies. For each study design, it provides details on the unit of study, study question, direction of inquiry, and key aspects of the design.
The document discusses key concepts related to measurement, evaluation, and assessment. It defines tests as instruments used to collect data on a person's abilities, and measurement as obtaining a score from a test. Evaluation is analyzing test data to determine if objectives were achieved, while assessment aims to improve learning. The document outlines various types of tests, measurements, evaluations and assessments, as well as factors like reliability, validity, norms, and formative vs. summative evaluations.
This document discusses different sources of knowledge and methods of acquiring knowledge. It outlines several ways that humans gain knowledge, including common sense, intuition, beliefs, tradition, personal experience, authority, reason/logic, and scientific methods. The document also summarizes Charles Peirce's four methods of "fixing belief" or determining what is true - tenacity, authority, a priori reasoning, and the scientific method. The scientific method is described as a way to satisfy doubts and reach the same conclusions, where conclusions are determined by external factors instead of human thinking.
This document defines the key types of variables in scientific experiments: independent variables, which are intentionally changed by the researcher; dependent variables, which change in response to the independent variable; and controlled variables, which are kept the same. It provides examples of identifying the independent, dependent, and controlled variables for experiments testing how temperature affects how high a ball bounces, how light color affects plant growth rate, and how parachute size affects how quickly a hippo falls.
This document provides an overview of hypotheses for a presentation. It begins with learning outcomes which are to explain the meaning and significance of hypotheses, identify types of hypotheses, and illustrate why hypotheses are needed.
The presentation will cover the scientific method, meaning and types of variables, characteristics of good hypotheses, categories of hypotheses including null and alternative, and how to form and test hypotheses. Hypotheses are defined as educated guesses that relate variables and guide research. They must be testable, falsifiable, and contribute to theory. Hypotheses can be categorized by their formulation as null or alternative, by direction as directional or non-directional, and by their derivation as inductive or deductive.
This document provides an overview of hypothesis testing including:
- Defining null and alternative hypotheses
- Types of errors like Type I and Type II
- Test statistics and significance levels for comparing means, proportions, and standard deviations of one and two populations
- Examples are given for hypothesis tests on population means, proportions, and comparing two population means.
This document outlines a research proposal that investigates teachers' perceptions of assessing students' oral reading skills in rural primary schools. The study aims to understand teachers' views on reading aloud assessments, and whether their perceptions differ based on years of teaching experience or education level. A mixed methods approach is proposed, using questionnaires to collect quantitative data on 80-120 teachers, and interviews of 12 teachers to obtain qualitative views. The research questions focus on teachers' perceptions of reading assessments, any differences related to experience or qualifications, and factors influencing perceptions. The significance, limitations, and methodology are described in the proposal.
MidTerm Exam 1Subject Differential EquationNote This e.docxARIV4
This document contains lecture materials on theory development from a course. It defines what a theory is, discusses the key components and characteristics of theories, and different forms theories can take. It also covers philosophical bases of theories and provides exercises for students to develop their own theories on a given topic.
PROFESSOR ROBERTO N. PADUATHEORY CONSTRUCTION AND DEVELO.docxbriancrawford30935
PROFESSOR ROBERTO N. PADUA
THEORY CONSTRUCTION AND DEVELOPMENT
COURSE OUTLINE
I. Theory,Philosophical Bases and Logic
II. Deductive Methods of Theory Development
III. Inductive Methods of Theory Development
IV. Theory Development Versus Theory Verification
Course Requirements: Workshop Outputs
LECTURE I: Theory and Philosophical Bases
1. SCIENTIFIC RESEARCH: is systematic, controlled, empirical, and critical investigation of hypothetical propositions about the presumed relationships among phenomena.
2. THEORY: is a set of interrelated constructs (concepts), definitions, and propositions that presents a systematic view of phenomena by specifying relations among variables, with the purpose of explaining, predicting, and controlling the phenomena.
DEFINITIONS
A Theory is a statement that explains why things happen as they do. There are three forms of a theory:
1. The "set-of-laws" form defines theory as a set of well-supported empirical generalizations, or "laws." Here, theory is thought of as "things we feel very certain about." This is the inductive form.
2. The "axiomatic" form defines theory as a set of interrelated propositions and definitions derived from axioms (i.e., things we feel certain about). This is the deductive form of a theory.
3. The "causal" form defines theory as a set of descriptions of causal processes. Here, theory "tells us how things work."
FUNCTIONS OF THEORY
a. EXPLANATION: provides an answer to the question "why is the fact what it is?" that is intellectually satisfying. Formal explanation: subsuming a proposition under a broader proposition which needs no explanation. It consists of a universal generalization that is assumed to be true, a particular set of circumstances, and a conclusion which asserts that an event had to occur because it was deducible from the logic of the propositions of the theory. Such explanations are deterministic/causal/nomic. Law: (x) <If Px then Qx>; Antecedent Condition: Px; Conclusion: Qx.
FUNCTIONS OF THEORY:
b. PREDICTION: proposing the occurrence of a future event given some awareness of a past or present relationship which may or may not be understood (e.g., astronomy). One can predict without explanation, but the reverse is not true. Thus explanation, rather than prediction, is the end of science.
FUNCTIONS OF THEORY
c. CONTROL: ability to intervene in a particular case or to alter the case of a particular relationship. In the pure case it implies complete understanding of elements and their relationships as well as a closed system. Less purely, it implies knowledge of the principles along which the phenomena vary.
CHARACTERISTICS OF A THEORY
ABSTRACTNESS
Abstract concepts are independent of a specific time and place. Because scientific statements must predict future events, they cannot be specific to past events. Scientists prefer theories that are as general as possible to time and place.
Abstract concepts are independent of specific circumst.
Prof. Dr. Mansoor Dawood is an associate professor at the University of California, Los Angeles. The document discusses key elements of research methodology, including facts, relations, concepts, variables, and definitions. Facts must be established through scientific research rather than intuition or logic alone. Relations between events can be causal or accidental, with causal relations demonstrating covariation over time in a nonspurious manner. Concepts represent objects or phenomena and serve as the building blocks of theories. Variables are properties that vary and can be quantitative or qualitative, independent or dependent. Definitions are needed to ensure concepts have consistent meaning, and can be nominal or operational to link conceptual and empirical levels.
There are several considerations when selecting a research topic, including academic/intellectual factors and practical applicability. Students may choose from assigned topics, field study topics using various resources, or free choice topics based on their own interests. Key factors in topic selection include the researcher's ability to study the topic thoroughly, available resources and techniques, and the topic's relevance to existing theories. Formulating a research problem involves discovering an issue in need of study and narrowing it to a manageable size. Developing testable hypotheses, clearly defining concepts, and establishing operational definitions allows relating findings to broader knowledge.
This document discusses research methodology concepts related to variables and hypotheses. It defines key terms like variables, independent and dependent variables, and different types of hypotheses. The document provides examples and explanations of these concepts. It outlines the objectives of understanding variables, hypothesis sources and types, and characteristics of a good hypothesis.
Essay On The Color Purple. The Color Purple Essay Example Topics and Well Wr...Hannah Davis
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This document discusses hypotheses and types of variables in research. It defines a hypothesis as a conjectural statement about the relationship between two or more variables. A hypothesis guides research and can be tested. The document outlines null and alternative hypotheses and discusses types of variables such as independent, dependent, intervening, stimulus, response, quantitative, qualitative, discrete, continuous, dichotomous, and polytomous variables. It provides examples to illustrate each variable type.
Reevy proposed that there are five categories of empathy. The first three categories are considered "primitive" forms of empathy and include:
1. Emotional contagion - Sharing another person's emotional state automatically and unconsciously.
2. Self-other awareness - Recognizing that another person's perspective is different than one's own.
3. Mentalizing - Inferring another person's thoughts and feelings and seeing things from their point of view.
The Context document explores theories related to gender and commu.docxchristalgrieg
The Context document explores theories related to gender and communication. Take time to review the document for an overview of key communication-style theories, including the following:
· Symbolic Interaction Theory.
· Performative Theory.
· Standpoint Theory.
context
Theories About Gender and Communication
According to Fixmer-Oraiz and Wood, a theory is a "way to describe, explain, and predict relationships among phenomena" (Fixmer-Oraiz & Wood, 2019, p. 34). Specifically, theories help make sense of the world around us. "Although we are not always aware of the theories we hold, they still shape how we act and how we expect others to act" (Fixmer-Oraiz & Wood, 2019, p. 34). Moreover, theories are a practical way of explaining what goes on around us, and although we sometimes believe theories are removed from the real world, they are directly connected to our everyday actions (Fixmer-Oraiz & Wood, 2019). It is important to remember the following:
· A theory represents a particular viewpoint as an attempt to understand phenomena.
· A theory offers more than explanations; it also influences attitudes and behaviors.
· One theory should not be considered the theory on gender development; multiple theories work together to create a fuller picture.
The study of theoretical approaches to gender development and communication has been on the focus of researchers for many years. Before we can truly understand how males and females communicate, we must understand why we communicate a certain way. To do this we must study the theoretical approaches to gender development and communication.
Specifically, gender is a social construct shaped by a number of social characteristics, larger normative expectations, personal experiences, and socializations. As communicators we must recognize the power of language and communication. We must understand why we use this to perceive, judge, and evaluate others.
Communication Style Theories
Following are some of the main theories that help us better understand the why behind our communication styles:
Symbolic Interaction Theory
Symbolic interaction theory helps us negotiate and define a situation. It helps us understand questions such as the following:
· Who am I?
· What should I do?
· What can I expect from you?
· What does this behavior mean?
The symbolic interaction theory suggests that cultural definitions of gender follow us into the workplace, along with specific value placed on "masculine" versus "feminine" behaviors. It suggests that, because you must interpret a new situation based on previous experience, you may have to "feel uncomfortable" to create the perception that is important to you. This discomfort comes from breaking social norms, that is, you are requiring others to actively negotiate a new definition of the situation.
Performative Theory
The performative theory suggests that gender is an expression of identity. Language and how we communicate are part of how we perform this identity. It suggests ...
Viewing cognitive conflicts as dilemmas, implications for mental healthGuillem Feixas
The idea that internal conflicts play a significant role in mental health has
been extensively addressed in various psychological traditions, including personal
construct theory. In the context of the latter, several measures of conflict
have been operationalized using the Repertory Grid Technique (RGT). All of
them capture the notion that change, although desirable from the viewpoint of
a given set of constructs, becomes undesirable from the perspective of other constructs.
The goal of this study is to explore the presence of cognitive conflicts in
a clinical sample (n = 284) and compare it to a control sample (n = 322).
It is also meant to clarify which among the different types of conflict studied
provides a greater clinical value and to investigate its relationship to symptom
severity (SCL-90-R). Of the types of cognitive conflict studied, implicative dilemmas
were the only ones to discriminate between clinical and nonclinical samples.
These dilemmas were found in 34% of the nonclinical sample and in 53% of
the clinical sample. Participants with implicative dilemmas showed higher symptom
severity, and those from the clinical sample displayed a higher frequency of
dilemmas than those from the nonclinical sample.
The document discusses various social science disciplines including sociology, political science, psychology, anthropology, and economics. It defines each discipline and provides examples of topics studied within each field. It also discusses sociological research methods and key concepts like variables, hypotheses, validity, reliability, and triangulation of methods.
This document provides an overview of key concepts in research methodology, including variables and their types, research objectives, hypotheses, research questions, and ethics. It defines variables as concepts that can take on different values, distinguishing between continuous, discrete, dependent, independent, controlled, confounding, intervening, extraneous, and organismic variables. It also outlines levels of measurement including nominal, ordinal, interval, and ratio scales. Research objectives are described as lay descriptions of what a research project aims to achieve. Hypotheses are defined as tentative statements about solving a problem that can be empirically tested. Research questions are broader focus areas that may be narrowed through investigative questions. Finally, it notes that research involving human participants raises unique ethical
This document discusses several sociological theories related to human action and agency. It begins by introducing action theories as micro-level approaches that focus on individual actions and interactions, in contrast to structural theories. It then summarizes key aspects of social action theory proposed by Weber, who saw both structural and action approaches as necessary. The document also discusses symbolic interactionism and how meanings arise through social interactions. Finally, it evaluates attempts to combine structural and action approaches, such as Giddens' structuration theory.
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.
This document discusses key concepts in social science research including different types of studies, levels of knowledge, variables, and quantitative vs qualitative research methods. It describes case studies, cross-sectional studies, and longitudinal studies. It also defines variables, independent vs dependent variables, and how to operationalize variables. Finally, it contrasts quantitative research which uses numerical data to quantitative research which focuses on meaning and interpretation.
This document discusses key concepts in social science research including different types of studies, levels of knowledge, variables, and quantitative vs qualitative research methods. It describes case studies, cross-sectional studies, and longitudinal studies. It also defines variables, independent vs dependent variables, and how to operationalize variables. Finally, it contrasts quantitative research which uses numerical data to quantitative research which focuses on meaning and interpretation.
This document outlines the basic building blocks of social scientific research:
1. It discusses specifying a research question, proposing explanations, and formulating testable hypotheses.
2. It also covers identifying key concepts, variables, and units of analysis to establish relationships between variables.
3. Developing hypotheses involves stating empirical, general, plausible, specific, and testable relationships between independent and dependent variables.
This document discusses objectivity in social science and comparative politics. It makes three main points:
1. Max Weber and Ernest Nagel argue that social science involves subjective presuppositions since researchers only focus on culturally significant events. Complete objectivity is not possible.
2. Nagel believes value judgments can be distinguished from factual assessments with careful analysis, though it is difficult. Social scientists should make their value assumptions explicit.
3. Charles Taylor argues that theoretical frameworks in political science implicitly promote certain values and dimensions over others. While complete neutrality may not be possible, researchers should still aim for objectivity.
1. Variables and Measurements Communication Research Syed Muhammad Jamal MS in Media and Communication Studies International Islamic University Islamabad smjamaal@gmail.com
2. Concepts and Constructs Concepts (formally developed ideas that a researcher may seek to operationalize) They are the abstract terms we employ to explain or make sense of our experience. Definition: A concept is a term that expresses an abstract idea formed by generalizing from particulars and summarizing related observations. Our understanding of the concept develops as we improve our ability to relate it to particular phenomena. Parts of Concepts: Symbol (word or term) Definition Example Height(symbol) is a simple concept from everyday experience. It is a characteristic of a physical object, the distance from top to bottom(definition). We can measure height or compare it. A height of zero is possible and height can increase or decrease over time. As with many words we use the word in several ways. Height is used in the expressions the height of battle, the height of the summer, and the height of fashion.
5. People must share an understanding of a concept in order for the concept to be useful.Example Authoritarianism It is a concept that was developed by social scientists to explain a phenomenon which came to be recognized after World War II: the state of mind that disposed individuals to accept the kind of authoritarian regime that appeared most dramatically in Nazi Germany. In this case, a number of beliefs and opinions, which appeared to be logically connected to one another and to the kind of behavior the social scientists were trying to explain, were drawn together to form a single concept—authoritarianism.
6.
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8. Because of its abstraction, a construct usually cannot be observed directly.
9. A construct is usually designed for some particular research purpose so that its exact meaning relates only to the context in which it is found.
10. Concepts and Construct are valuable tools in theoretical research.Example The term authoritarianism represents a construct defined to describe a certain type of personality; it comprises nine different concepts, including conventionalism, submission, superstition, and cynicism. Authoritarianism itself cannot be seen; some type of questionnaire or standardized test must determine its presence. The results of such tests indicate what authoritarianism might be and whether it is present under given conditions, but the tests do not provide exact definitions for the construct itself.
11. Variables Variable (A measure on which differences in response can be established) Definition The empirical counterpart of a construct or concept is called a variable. Variables are important because they link the empirical world with the theoretical; they are the phenomena and events that can be measured or manipulated in research. Variables take on two or more values. The values or the categories of a variable are its attributes. Example Gender is a variable; it can take on one of two values: male or female. Male is not a variable; it describes a category of gender and is an attribute of the variable gender.
23. “communication” verbal and nonverbal interaction between two or more persons To fully define construct communication, definition of verbal and non verbal are needed. ‘Verbal’ written or spoken words ‘Non-Verbal’ gesture or other nonlinguistic devices ‘Interaction’ the exchange of messages Conceptual definition: verbal and non verbal exchange of messages between two or more persons.
24. Primitive Primitive term is a concept with an agreed upon meaning. Example Communication between two or more persons between two or more persons is generally agreed upon primitive term and no formal definitions are needed to explain them.
25.
26. The researcher must provide conceptual definition to all concepts using in problem statement to avoid confusion and misunderstanding
33. Bivariate: A proposition that discusses the relationship between two variables is called ‘bivariate’.
34.
35. There is a continuous increase in the urban population of Pakistan.
36. Increase in the urban population will increase illiteracy higher the increase in population—higher the illiteracy in urban area.
37. Increase in population will increase illiteracy and crime rate Increase in population—increase in illiteracy. Increase in the population– increase in the crime rate.
38. Relationship b/w VariablesCorrelation, Positive, Negative Correlation: when a proposition says that two or more variables e.g. X and Y are related, it means that they vary together. It means that X is accompanied by a change in Y and vice versa. Such variation is called correlation. Example When the population increases, illiteracy and/or crime rate also increase. The proposition can say that increase in the population, illiteracy and crime rate are correlated. If increase in the population does not influence the literacy ratio or the crime rate, these variables are uncorrelated.
39. Correlation, Positive, Negative Positive or Direct relationship: if an increase in the value of one variable is accompanied by an increase in the value of the second variables, the relationship is called positive. Similarly, if decrease in on variable is accompanied by decrease in other variable, the relationship is called positive or direct. Negative or Inverse relationship: If increase in one variable is accompanied by decrease in the second variable, the relationship is called a negative or inverse. Example Increase in education level is accompanied by an increase in the income, the relationship is positive. If increase in the education level is accompanied by a decrease in the crime rate, the relationship between the two variables is negative or inverse.
40. Scales of Measurement Most studies in the Social and behavioral sciences collect data that are in the form of numbers. Usually, it is not the actual numbers that are of interest, but what the numbers represent. Measurement is the assignment of numbers to objects or events according to predetermined rules. Because there are different rules for assigning numbers, the same number have a different meaning, depending on the rules used to assign the number. S.S. Stevens(1951) proposed four levels of measurement that differ in the assumptions that are made regarding the underlying characteristic dimensions to which the numbers apply.
41. Measurement It indicates that measurement is a doing activity—assigning numbers—which involves performing operations sequentially(following a particular order). It specifies that what you are doing must follow certain rules or a model which lays out the principles of the measurement system. Example Suppose you are measuring the variable educational attainment. You decide to use “number of years of education completed” to represent the variable of educational attainment. A person finishing elementary school would be assigned an 8 (for eight years of schooling); a person who dropped out of high school in tenth grade would be assigned a 9; a graduate of a two-year college would be assigned a 14; a holder of a master’s degree would be assigned an 18.
44. Quantitative: Reflecting the amount or extent of an attribute.We can measure gender, male or female, by assigning the number 1 to males and the number 2 to females. This is qualitative measurement as the numbers reflect the underlying dimension of gender. These numbers reflect a quality (maleness or femaleness) and don’t provide any quantitative information. We can’t say that women possess more of the quality of gender than do men because they are given a score of 2 while men are only given a score of 1.
45. Measurement With qualitative measurement the numbers are arbitrary and don’t provide any quantitative information such as rank or distance. If, in measuring gender, we arbitrarily assign the number 37 to men and the number 3.21 to women, we are still performing measurement, since different numbers still accurately reflect the different qualities. The number here function as labels.
46. Levels or Scales of Measurement Levels of measurements Categorical Numerical Nominal Ordinal Interval Ratio Scale Scale Scale Scale Researchers rely on scales of measurement to select statistical techniques. In the social sciences four types of scales for measuring a variable (two types for categorical variables, two types for numerical variables) have been delineated. These scale type ( or levels of measurement, as they are usually called) are useful in helping to classify and catalog variables in a study, as well as in designing questions to measure variables.
48. Levels of Measurements Nominal Measurement The assignment of numbers using a nominal scale is a labeling activity. When using a nominal scale, one cannot interpret the numbers as anything other than the names of things. A variable with a nominal level of measurement consists of a set of distinctive categories that imply no specific order. It is the weakest form of measurement. In it numerals or other symbols are used to classify persons, objects, or characteristics. Example In physical sciences rocks, can generally be classified into three categories: igneous, sedimentary and metamorphic. A geologist who assigns a 1 to igneous, a 2 to sedimentary, and a 3 to metamorphic has formed a nominal scale. Note that the numerals are simply labels that stand for the respective categories; they have no mathematical significance. A rock that is placed in Category3 does not have more “rockness” than those in Categories 2 and 1. Gender: Male or Female
49. Levels of Measurements An essential requirement of nominal scaling is that subjects be classified into mutually exclusive and exhaustive categories. In other words, each subject or observation is assigned to one and only one category, and all observations or subjects are classified into the specific categories. The use of a nominal scale requires a consistent application of an assignment rule. Suppose a researcher wants to indentify which set of variables predicts response to treatment. All subjects are assigned to one of to categories: responder or non-responder. According to the rule of mutual exclusivity and exhaustiveness, subjects are assigned to one or the other category, not both, and all subjects are assigned to one or the other category. The researcher must clearly define what is meant by responder versus non-responder. In this instance, a responder might be defined as someone who falls within the “normal” range of anxiety and a non-responder as someone who falls outside of this range.
50. Levels of Measurements Ordinal Measurement Variables that have two or more categories with an inherent order among them are measured at an ordinal level of measurement. Nothing is specified with regard to the distance between any two rankings i.e., this level possesses the property of equivalence. It also possesses the property of order among the categories. Any given category can be defined as being higher or lower than any other category. Example While measuring the variable “socioeconomic status” by categorizing families according to class: lower, lower middle, middle, upper middle, or upper. A rank of 1 is assigned to lower, 2 to lower middle, 3 to middle, and so forth. In this situation, the numbers have some mathematical meaning: families in category 3 have a higher socioeconomic status than families in category 2. All families placed in a category are treated equally, even though some might have greater incomes than others.
51. Levels of Measurements Interval Measurement An interval scale is a created scale that has clearly defined intervals between the points on the scale, and it has order; but it has no true zero point. It assist us in ordering things quite precisely. An interval level of measurement has separate categories, like nominal scales, and also has ordered categories, like ordinal scales; but in addition, the distance between the points on an interval scale can be determined mathematically and precisely. These are used for continuous variables that can register very small differences between categories. Example Think of a thermometer. It has lines marking off points on the scale to register the changing temperatures. However, there is no true zero point—no point at which there would be no temperature. If it is a Fahrenheit thermometer, the zero on the scale will be at 32 degrees below the freezing point of water; if it is a centigrade thermometer, the zero on the scale will be at the freezing point of water. In neither case, however, will the zero refer to a point where there is no temperature.
52. Levels of Measurements Ratio Measurement A ratio scale encompasses all the qualities of the earlier forms of scale: it must have more than one category; it must have an implicit order; it must be able to determine the exact distance between the intervals. In addition, however, it must have a true zero point. Example Income, age, number of children, or cost of housing. Your income could be zero; you might not have children; your rent could be free.
53. References Mass Media Research- Wimmer, Dominick Social Research Methods- Neuman Contemporary Communication Research Methods- Smith http://www.socialresearchmethods.net/kb/index.php http://www.une.edu.au/WebStat/unit_materials/index.htm