Different Variables in Quantitative Research~GM Lectures

Summary

This lecture delves into the concept of variables in research, explaining how they can influence outcomes. It distinguishes between numeric and categorical variables, with the former including continuous, interval, and discrete types, and the latter encompassing ordinal, nominal, dichotomous, and polytomous categories. The lecture further explores experimental variables, detailing independent and dependent variables, and the roles of control, moderating, and extraneous variables. It concludes by emphasizing the importance of understanding variable classifications for meaningful research analysis.

Takeaways

• 🔍 Variables are factors that can change or affect the results of a study and are crucial for understanding differences in research.
• 📊 Numeric variables describe measurable quantities and can be either continuous (e.g., time, age, weight, height) or discrete (e.g., class attendance, number of establishments, number of children in a family).
• 🏷 Categorical variables describe qualities or characteristics and include ordinal (e.g., clothing size, academic ranking), nominal (e.g., learning styles, blood type, language spoken), dichotomous (e.g., yes/no, true/false, gender), and polytomous variables (e.g., performance level, educational attainment).
• 🔧 Experimental variables are used to determine causal relationships and include independent variables, dependent variables, control variables, moderating variables, and extraneous variables.
• ⚖️ Independent variables are manipulated to observe their effect on dependent variables, which are the outcomes being measured.
• 🎚 Control variables are held constant to isolate the effects of independent variables on dependent variables.
• 🔄 Moderator variables influence how the relationship between independent and dependent variables changes under different conditions.
• 🌐 Extraneous variables are existing variables that can influence study outcomes and should be controlled to avoid confounding results.
• 📈 Non-experimental variables are not manipulated by researchers and include predictor variables, which affect criterion variables in non-experimental studies.
• 💡 Recognizing and classifying variables is essential for researchers to understand how they interact and affect each other, leading to more meaningful study outcomes.

Q & A

What is a variable in the context of research?

A variable is an entity that can take on different values. It represents an aspect of a theory that can change or vary and can affect the results of a study.

What are some examples of variables mentioned in the script?

Examples of variables include age, gender, IQ level, lifestyle, temperature, and medical treatment used.

What is the difference between continuous and discrete variables?

Continuous variables can assume any value between certain real numbers and include finer measurements like time, weight, or height. Discrete variables only take whole numbers, such as the number of children in a family or class attendance.

How are categorical variables classified?

Categorical variables are qualitative and are divided into ordinal, nominal, dichotomous, and polycautious variables.

What distinguishes ordinal variables from nominal variables?

Ordinal variables can be logically ordered or ranked (e.g., clothing size, academic ranking), while nominal variables cannot be logically ordered and are used for classification or identification (e.g., blood type, plate numbers).

What is a dichotomous variable, and how does it differ from a polycautious variable?

A dichotomous variable represents only two categories (e.g., yes/no, true/false), while a polycautious variable has many possible categories (e.g., educational attainment, performance level).

What is the role of independent and dependent variables in experimental research?

Independent variables are presumed to cause changes in another variable and are manipulated during experiments. Dependent variables change as a result of the independent variable's manipulation.

What is the function of control and moderator variables in an experiment?

Control variables are held constant to help identify differences in outcomes, while moderator variables delineate how relationships change under different conditions or circumstances.

What are extraneous variables, and why is it important to control them?

Extraneous variables are variables that already exist during an experiment and can influence the study’s results. It’s important to control them because they might cause alternative results not related to the variables being studied.

How do non-experimental variables differ from experimental variables?

Non-experimental variables cannot be manipulated by the researcher and are used in non-experimental studies. They include predictor variables, which affect other variables, and criterion variables, which are influenced by predictor variables.