Different Variables in Quantitative Research~GM Lectures

GM Lectures
18 Aug 202017:39
EducationalLearning
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TLDRThis lecture delves into the concept of variables, crucial for understanding research studies. It explains that variables are entities that can change or affect study outcomes, and can be classified into numeric (continuous, interval, discrete) and categorical (ordinal, nominal, dichotomous, polycautimus). The talk further distinguishes between experimental variables (independent, dependent, control, moderating, extraneous) and non-experimental variables (predictor, criterion), emphasizing their roles in establishing causal relationships and controlling for confounding factors. The importance of recognizing and classifying variables is highlighted for meaningful study outcomes.

Takeaways
  • 📚 Variables are essential in research studies to understand differences and can be anything that varies and affects the results.
  • 🔢 Numeric variables describe measurable quantities and are divided into continuous/interval and discrete variables.
  • ⏳ Continuous or interval variables can take any value within a range, such as time, age, weight, and height.
  • 🔢 Discrete variables only take whole numbers, like class attendance or the number of children in a family.
  • 🏷 Categorical variables describe qualities or characteristics and are qualitative, including ordinal, nominal, dichotomous, and polycautimus variables.
  • 🔖 Ordinal variables can be logically ordered or ranked, such as clothing sizes, academic rankings, and satisfaction levels.
  • 🔍 Nominal variables are for identification and cannot be logically ranked, like blood types, learning styles, and languages spoken.
  • 👥 Dichotomous variables have only two categories, such as yes/no or true/false.
  • 🏆 Polycautimus variables have many possible categories, like performance levels and educational attainment.
  • 🔧 Experimental variables determine causal relationships and include independent, dependent, control, moderating, and extraneous variables.
  • 🔄 Independent variables are manipulated to cause changes, while dependent variables change as a result.
  • 🎚 Control variables are held constant to isolate the effects of the independent variable.
  • 📊 Moderator variables affect how the relationship between independent and dependent variables changes under different conditions.
  • 🚫 Extraneous variables are existing variables that could influence the results and should be controlled to avoid confounding the study.
  • 📉 Non-experimental variables cannot be manipulated and include predictor variables that affect criterion variables in non-experimental studies.
Q & A
  • What factors might influence low academic performance according to the teacher's perspective in the script?

    -The factors that might influence low academic performance include students not understanding instructions, sleeping instead of studying, running out of time to finish tests, and being unable to focus during review sessions.

  • What is the definition of a variable in the context of research studies?

    -A variable is an entity that can take on different values, an aspect of a theory that can vary or change, or anything that can change or affect the results of a particular study.

  • How are variables classified in terms of their nature?

    -Variables are classified as numeric, categorical, experimental, or non-experimental, which determines their roles in a particular study.

  • What are the two main types of numeric variables and how do they differ?

    -Numeric variables are divided into continuous or interval variables and discrete variables. Continuous or interval variables can assume any value within a set of real numbers, while discrete variables can only assume whole numbers.

  • Can you provide examples of continuous or interval variables mentioned in the script?

    -Examples of continuous or interval variables include time (hours, minutes, seconds), age (ten and a half, ten and three quarters), weight (grams, milligrams), and height (feet, centimeters, inches).

  • What are discrete variables and provide some examples?

    -Discrete variables focus on whole numbers and do not recognize values in between. Examples include class attendance, number of establishments in an area, and number of children in a family.

  • How are categorical variables defined and what are their subtypes?

    -Categorical variables describe a quality or characteristic of a data unit and are qualitative in nature. They are further divided into ordinal, nominal, dichotomous, and polycautimus variables.

  • What is the purpose of ordinal variables and provide an example?

    -Ordinal variables serve the purpose of classification and ranking. An example would be academic ranking where a person who ranks first is different from someone who ranks second or third.

  • How do nominal variables differ from ordinal variables?

    -Nominal variables are for classification and identification purposes only and cannot be arranged logically in terms of ranking, unlike ordinal variables which can be organized or ranked.

  • What is the role of experimental variables in determining causal relationships?

    -Experimental variables help determine causal relationships by manipulating independent variables to observe changes in dependent variables, while controlling for control variables and considering moderator and extraneous variables.

  • Can you explain the difference between independent and dependent variables?

    -Independent variables are presumed to cause changes in another variable and are usually manipulated in an experiment. Dependent variables change because of another variable and are usually affected by the manipulation of the independent variable.

  • What are control variables and moderator variables in the context of an experiment?

    -Control variables are held constant to identify possible differences in outcomes, while moderator variables delineate how a relationship of interest changes under different conditions or circumstances.

  • What is the role of extraneous variables in a study and why are they important to control?

    -Extraneous variables are existing variables during an experiment that could influence the results. They are important to control because they can offer alternative results and affect the validity of the study.

  • How are non-experimental variables different from experimental variables?

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

  • Why is it important for researchers to recognize and understand the classification and roles of variables in their study?

    -Recognizing variables and understanding their classification and roles helps researchers have a more detailed idea of how the variables in their study interact and affect each other, contributing to a more meaningful discussion regarding the possible outcomes of a study.

Outlines
00:00
🔍 Variables Influencing Academic Performance

This paragraph introduces the concept of variables in the context of a teacher analyzing factors affecting students' low academic performance. It lists potential factors such as misunderstanding instructions, lack of study, time constraints, and lack of focus. The paragraph then defines a variable as an entity that can assume different values and change or vary within a study, emphasizing that any factor that can vary is considered a variable. It concludes by stating the importance of variables in understanding differences in research studies.

05:00
📊 Classification of Variables: Numeric and Categorical

The second paragraph delves into the classification of variables, focusing on numeric and categorical types. Numeric variables, which include measurable quantities, are further divided into continuous or interval variables and discrete variables. Continuous variables can take any value within a range, exemplified by time, age, weight, and height, while discrete variables are limited to whole numbers, such as class attendance and the number of children in a family. Categorical variables describe qualities or characteristics and are qualitative in nature, subdivided into ordinal, nominal, dichotomous, and polycautimus variables. Ordinal variables can be ranked, like clothing size and academic ranking, whereas nominal variables are for identification and classification, such as learning styles and blood types. Dichotomous variables have two categories, like gender, and polycautimus variables have multiple categories, like performance levels.

10:01
🧐 The Role of Variables in Experimental Design

This paragraph discusses the role of variables in experimental studies, distinguishing between experimental and non-experimental variables. Experimental variables determine causal relationships and include independent variables, which are manipulated to cause changes, and dependent variables, which change as a result. The paragraph provides examples to illustrate these concepts, such as studying affecting academic performance and diet and exercise affecting physical fitness. It also introduces control variables, which are held constant to identify differences in outcomes, and moderator variables, which influence how relationships change under different conditions. Extraneous variables, which can affect study results and should be controlled, are also mentioned, with an example of a study on the effect of music on academic performance, where genre of music could be a moderator variable and class duration a control variable.

15:02
🏛️ Non-Experimental Variables and Their Impact

The final paragraph addresses non-experimental variables, which cannot be manipulated by researchers and are used in non-experimental studies. It differentiates between predictor variables, which can affect other variables, and criterion variables, which are influenced by predictor variables. Examples given include management styles affecting employee satisfaction and guidance counseling programs influencing absenteeism and dropout rates among students. The paragraph emphasizes the importance of recognizing and understanding the roles of variables in a study to facilitate meaningful discussions about potential outcomes.

Mindmap
Keywords
💡Variable
A variable is an essential concept in research and statistics, defined as an entity that can take on different values. In the context of the video, variables are aspects that can vary or change and affect the results of a study. The video discusses how variables contribute to understanding differences in research studies, using examples such as a teacher identifying factors influencing students' academic performance.
💡Numeric Variable
Numeric variables are those with values that describe a measurable numerical quantity, answering questions like 'how many' or 'how much'. They are quantitative in nature and are divided into continuous or interval variables and discrete variables. The video provides examples such as time, age, weight, and height to illustrate continuous variables, which can take any value within a range, and class attendance or the number of children in a family to represent discrete variables, which are whole numbers only.
💡Categorical Variable
Categorical variables are qualitative and describe a quality or characteristic of a data unit, answering questions like 'what type' or 'which category'. The video explains that categorical variables are further divided into ordinal, nominal, dichotomous, and polycautimus variables. Examples given include clothing size, academic ranking, and blood type, which help to classify and identify different aspects of data.
💡Ordinal Variable
Ordinal variables are a type of categorical variable that can take values which can be logically ordered or ranked. The video uses examples such as clothing sizes, academic rankings, and levels of satisfaction to illustrate how ordinal variables provide a ranking system, where each value has a specific order or position in a sequence.
💡Nominal Variable
Nominal variables, unlike ordinal variables, have values that cannot be arranged in a logical sequence. They are used for identification and classification purposes. The video gives examples like learning styles, language spoken, blood type, and plate numbers, which are used to identify specific characteristics without implying a rank or order.
💡Discrete Variable
Discrete variables are numeric variables that can only take on specific, separate values, typically whole numbers. The video explains that unlike continuous variables, discrete variables do not include values in between, such as class attendance or the number of children in a family, which are always whole numbers and do not include fractions or decimals.
💡Continuous Variable
Continuous variables are numeric variables that can assume any value within a certain range, reflecting a continuous scale. The video provides examples like time, age, and height, which can have values that include fractions and decimals, indicating a continuous measurement rather than discrete whole numbers.
💡Experimental Variable
Experimental variables are crucial in determining causal relationships within an experiment. The video discusses how these variables are subdivided into independent variables, dependent variables, control variables, moderating variables, and extraneous variables. For instance, in a study on the effect of studying on academic performance, studying is the independent variable presumed to cause changes, while academic performance is the dependent variable that changes as a result.
💡Independent Variable
An independent variable is a variable that is manipulated in an experiment to observe its effect on another variable. In the video, it is referred to as the causal variable. The script uses the example of studying as an independent variable that influences academic performance, which is the dependent variable.
💡Dependent Variable
A dependent variable is one that is expected to change as a result of changes in the independent variable. It is also known as the effect variable. The video explains that in an experiment, the dependent variable is monitored to see how it is affected by manipulations of the independent variable, such as changes in academic performance due to studying habits.
💡Control Variable
Control variables are kept constant in an experiment to ensure that any observed effects can be attributed to the independent variable. The video uses the example of class duration being held constant at 60 minutes, regardless of the type of music played, to isolate the effects of the independent variable (music genre) on the dependent variable (academic performance).
💡Moderating Variable
A moderating variable is one that affects the direction or strength of the relationship between other variables. The video explains that a moderator variable introduces change in the results if the conditions are altered, such as the genre of music played potentially affecting the academic performance of students differently.
💡Extraneous Variable
Extraneous variables are outside factors that could influence the results of an experiment and must be controlled to ensure the validity of the findings. The video gives examples such as noise, ventilation, and lighting, which could affect students' academic performance regardless of the music they are exposed to and thus need to be controlled for in the study.
💡Non-Experimental Variable
Non-experimental variables are those that cannot be manipulated by a researcher and are used in non-experimental studies. The video divides these into predictor variables, which influence other variables, and criterion variables, which are affected by predictor variables. An example provided is management styles as a predictor variable affecting employee satisfaction, the criterion variable.
Highlights

A teacher wants to identify factors influencing low academic performance.

Variables are factors that contribute to a study, such as understanding instructions, studying habits, time management, and focus.

A variable is an entity that can take on different values and affect the results of a study.

Variables can be anything that a researcher is interested in and can vary.

Variables are classified as numeric, categorical, experimental, or non-experimental based on their roles in a study.

Numeric variables describe measurable quantities and can be continuous, interval, or discrete.

Continuous or interval variables can take any value within a range, like time, age, and weight.

Discrete variables only take whole values, such as class attendance or the number of children in a family.

Categorical variables describe qualities or characteristics and are qualitative in nature.

Ordinal variables can be logically ordered or ranked, like clothing sizes or academic rankings.

Nominal variables are for identification and cannot be logically sequenced, like blood types or learning styles.

Dichotomous variables represent two categories, such as yes/no or true/false.

Polycautimus variables have many possible categories, like performance levels or educational attainment.

Experimental variables determine causal relationships and include independent, dependent, control, moderating, and extraneous variables.

Independent variables are presumed to cause changes in another variable and are manipulated in an experiment.

Dependent variables change because of another variable and are monitored in an experiment.

Control variables are held constant to identify differences in outcomes, while moderator variables change the relationship under different conditions.

Extraneous variables are existing variables that can influence study results and should be controlled.

Non-experimental variables cannot be manipulated and include predictor and criterion variables.

Recognizing variables and their classification helps researchers understand interactions and outcomes in a study.

Transcripts
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