a) Descriptive data analysis: This refers to the process of organizing, summarizing, and interpreting data to describe the characteristics or patterns within a given sample or population.
b) Inferential data analysis: This involves making inferences or generalizations about a population based on the observed data from a sample. It uses various statistical techniques to determine the likelihood of an observed result occurring by chance.
c) Primary data: These are data that are collected directly from original sources for a specific research purpose. The researcher collects primary data through surveys, experiments, observations, interviews, or other means.
d) Secondary data: These are data that have already been collected by someone else for a different purpose and are made available to researchers. Examples of secondary data include government reports, academic literature, and data collected by other researchers.
e) Units of analysis: It refers to the entities or subjects that are being studied in a research project. The units of analysis can vary depending on the research question and can be individuals, groups, organizations, or even countries.
f) Research: It is the systematic investigation of a topic or problem to discover new knowledge, interpret existing knowledge, or develop or test theories.
h) Sampling frame: It is a list or a set of units from which a researcher selects a sample. It provides a complete representation of the target population and helps ensure that the sample is representative of the population.
i) Qualitative research methods: These are research methods that focus on understanding and interpreting social phenomena through non-numerical data. Qualitative methods include interviews, focus groups, observations, and content analysis.
j) Quantitative research methods: These are research methods that involve the collection and analysis of numerical data to test hypotheses, establish relationships, and make statistical inferences. Quantitative methods include surveys, experiments, and statistical analysis.
k) Inductive research: It is an approach to research that involves formulating theories or generalizations based on observations and patterns in the data. Inductive research starts with specific observations and then develops broader hypotheses or theories.
l) Deductive research: It is an approach to research that involves testing existing theories or hypotheses using new data. Deductive research starts with a hypothesis or theory and then collects data to confirm or refute it.
m) Replicability: It refers to the ability of other researchers to replicate or repeat a study using the same methods and procedures and obtain similar results. Replicability is an important aspect of the scientific method and helps establish the validity and reliability of research findings.
n) Precision: It refers to the level of detail, accuracy, or exactness in measurement or estimation. Precision is important in research because it determines the reliability and validity of the data and findings.
o) Falsifiability: It is the capacity of a hypothesis, statement, or theory to be proven false by empirical evidence or observation. Falsifiability is an essential characteristic of scientific research as it allows for the testing and potential rejection of hypotheses.
p) Parsimony: It is a principle in research that favors the simplest explanation or theory that adequately explains a phenomenon. Parsimony suggests that researchers should avoid unnecessary complexity in their explanations and theories.
q) Probability sampling: It is a sampling technique where each member of the population has a known, non-zero chance of being selected for the sample. Probability sampling methods include simple random sampling, stratified sampling, and cluster sampling.
r) Non-probability sampling: It is a sampling technique where the selection of sample members is based on the researcher's judgment or convenience and does not involve a random selection process. Non-probability sampling methods include purposive sampling, snowball sampling, and convenience sampling.
s) Panel mortality: It refers to the loss of individuals or units in a longitudinal study over time, resulting in a decrease in sample size and potential bias in the results. Panel mortality can occur due to participant dropout, non-response, or attrition.
g) Research design: It refers to the overall plan or structure that guides a research study. The research design includes the selection of research methods, data collection procedures, and data analysis techniques, and helps researchers address their research questions or hypotheses effectively.
Write short notes on each of the following terms as used in research methods
a)Descriptive data
analysis
b)Inferential data analysis
c)Primary data
d)Secondary data
e)Units of analysis
f)Research
h)Sampling frame
i)Qualitative research
methods
j)Quantitative research
methods
k)Inductive research
l)Deductive research
m)Replicability
n)Precision
o)Falsifiability
p)Parsimony
q)Probability sampling
r)Non probability sampling
s)Panel mortality
g)Research design
3 answers
Differentiate between the following concepts as used in research
a)Concepts and variables
b)Change variable and outcome variable
c)Extraneous and intervening variables
d)Active and attribute variables
e)Cause variables and control variables
f)Theoretical model and empirical model
g)Structured and non structured questionnaire
h)Cross-sectional research and longitudinal research
i)Population and sample
j)Naturalistic observation and Participant observation
k)Trend and panel studies
l)Correlation and causality studies
m)Experimental and nonexperimental research designs
a)Concepts and variables
b)Change variable and outcome variable
c)Extraneous and intervening variables
d)Active and attribute variables
e)Cause variables and control variables
f)Theoretical model and empirical model
g)Structured and non structured questionnaire
h)Cross-sectional research and longitudinal research
i)Population and sample
j)Naturalistic observation and Participant observation
k)Trend and panel studies
l)Correlation and causality studies
m)Experimental and nonexperimental research designs
a) Concepts and variables: Concepts are abstract ideas or mental constructs that represent something in the real world. Variables, on the other hand, are measurable attributes or characteristics that can take on different values or levels. Variables are used to operationalize concepts and make them measurable.
b) Change variable and outcome variable: A change variable is a variable that measures the amount of change that occurs between two or more time points or conditions. An outcome variable, on the other hand, is a variable that represents the end result or outcome of a study or experiment. It is often used to assess the impact or effects of an independent variable.
c) Extraneous and intervening variables: Extraneous variables are variables that are not the main focus of a study but can potentially influence the relationship between the independent and dependent variables. Intervening variables, on the other hand, are variables that mediate or explain the relationship between the independent and dependent variables. They help provide a mechanism through which the independent variable affects the dependent variable.
d) Active and attribute variables: Active variables are variables that can be directly manipulated or controlled by the researcher. For example, in an experiment, the independent variable is an active variable. Attribute variables, on the other hand, are inherent characteristics or qualities of the subjects or objects being studied. They cannot be manipulated by the researcher but can be measured or categorized.
e) Cause variables and control variables: Cause variables, also known as independent variables, are variables that are manipulated or changed by the researcher to determine their effect on the dependent variable. Control variables, on the other hand, are variables that are held constant or controlled to remove or reduce their potential influence on the relationship between the independent and dependent variables.
f) Theoretical model and empirical model: A theoretical model is a conceptual framework or a set of proposed relationships between variables that is based on theory or existing knowledge. It is a representation of how the researcher believes the variables and concepts are related. An empirical model, on the other hand, is derived from the data collected during a study. It represents the actual observed relationships between variables in the specific context of the study.
g) Structured and non-structure questionnaire: A structured questionnaire is a questionnaire in which the questions and response options are predetermined and fixed. It is a standardized questionnaire that ensures consistency in data collection. A non-structured questionnaire, also known as an open-ended questionnaire, allows respondents to provide their own responses without predetermined options. It allows for more in-depth and qualitative responses.
h) Cross-sectional research and longitudinal research: Cross-sectional research involves collecting data from a sample or a population at a specific point in time. It provides a snapshot of the variables of interest. Longitudinal research, on the other hand, involves collecting data from the same sample or population over multiple time points. It allows for the examination of changes and trends over time.
i) Population and sample: A population is the entire group of individuals, objects, or events that a researcher is interested in studying. A sample, on the other hand, is a subset of the population that is selected for inclusion in a study. The sample is often used to make inferences about the larger population.
j) Naturalistic observation and participant observation: Naturalistic observation involves the systematic observation and recording of behaviors in their natural setting without any interference or manipulation by the researcher. In participant observation, the researcher becomes actively involved in the setting being observed and may even participate in the activities being studied.
k) Trend and panel studies: Trend studies examine changes in a phenomenon or variable over time in different populations or samples. They provide data on how a variable changes across different time points. Panel studies, on the other hand, involve collecting data from the same sample or population over multiple time points. They are useful for studying individual-level changes and tracking the same group of individuals over time.
l) Correlation and causality studies: Correlation studies examine the relationship between two or more variables without establishing causality. They can determine the strength and direction of the relationship. Causality studies, on the other hand, aim to establish a cause-and-effect relationship between variables. They often involve experimental designs or other methods to establish causal relationships.
m) Experimental and nonexperimental research designs: Experimental research designs involve manipulating the independent variable and randomly assigning participants to different conditions to determine cause-and-effect relationships. Nonexperimental research designs, also known as observational or correlational designs, do not involve manipulating the independent variable. They observe and measure variables as they naturally occur and examine their relationships without manipulation.
b) Change variable and outcome variable: A change variable is a variable that measures the amount of change that occurs between two or more time points or conditions. An outcome variable, on the other hand, is a variable that represents the end result or outcome of a study or experiment. It is often used to assess the impact or effects of an independent variable.
c) Extraneous and intervening variables: Extraneous variables are variables that are not the main focus of a study but can potentially influence the relationship between the independent and dependent variables. Intervening variables, on the other hand, are variables that mediate or explain the relationship between the independent and dependent variables. They help provide a mechanism through which the independent variable affects the dependent variable.
d) Active and attribute variables: Active variables are variables that can be directly manipulated or controlled by the researcher. For example, in an experiment, the independent variable is an active variable. Attribute variables, on the other hand, are inherent characteristics or qualities of the subjects or objects being studied. They cannot be manipulated by the researcher but can be measured or categorized.
e) Cause variables and control variables: Cause variables, also known as independent variables, are variables that are manipulated or changed by the researcher to determine their effect on the dependent variable. Control variables, on the other hand, are variables that are held constant or controlled to remove or reduce their potential influence on the relationship between the independent and dependent variables.
f) Theoretical model and empirical model: A theoretical model is a conceptual framework or a set of proposed relationships between variables that is based on theory or existing knowledge. It is a representation of how the researcher believes the variables and concepts are related. An empirical model, on the other hand, is derived from the data collected during a study. It represents the actual observed relationships between variables in the specific context of the study.
g) Structured and non-structure questionnaire: A structured questionnaire is a questionnaire in which the questions and response options are predetermined and fixed. It is a standardized questionnaire that ensures consistency in data collection. A non-structured questionnaire, also known as an open-ended questionnaire, allows respondents to provide their own responses without predetermined options. It allows for more in-depth and qualitative responses.
h) Cross-sectional research and longitudinal research: Cross-sectional research involves collecting data from a sample or a population at a specific point in time. It provides a snapshot of the variables of interest. Longitudinal research, on the other hand, involves collecting data from the same sample or population over multiple time points. It allows for the examination of changes and trends over time.
i) Population and sample: A population is the entire group of individuals, objects, or events that a researcher is interested in studying. A sample, on the other hand, is a subset of the population that is selected for inclusion in a study. The sample is often used to make inferences about the larger population.
j) Naturalistic observation and participant observation: Naturalistic observation involves the systematic observation and recording of behaviors in their natural setting without any interference or manipulation by the researcher. In participant observation, the researcher becomes actively involved in the setting being observed and may even participate in the activities being studied.
k) Trend and panel studies: Trend studies examine changes in a phenomenon or variable over time in different populations or samples. They provide data on how a variable changes across different time points. Panel studies, on the other hand, involve collecting data from the same sample or population over multiple time points. They are useful for studying individual-level changes and tracking the same group of individuals over time.
l) Correlation and causality studies: Correlation studies examine the relationship between two or more variables without establishing causality. They can determine the strength and direction of the relationship. Causality studies, on the other hand, aim to establish a cause-and-effect relationship between variables. They often involve experimental designs or other methods to establish causal relationships.
m) Experimental and nonexperimental research designs: Experimental research designs involve manipulating the independent variable and randomly assigning participants to different conditions to determine cause-and-effect relationships. Nonexperimental research designs, also known as observational or correlational designs, do not involve manipulating the independent variable. They observe and measure variables as they naturally occur and examine their relationships without manipulation.
1 answer