Jenis Data & Variabel : Metodologi Penelitian

Summary

This video discusses the types of variables commonly used in research, categorized by their values and functions. It covers discrete and continuous variables, independent and dependent variables, as well as moderating, intervening, control, and extraneous variables. Additionally, it explores different types of data, including primary and secondary data, and their collection methods, such as surveys, observations, and archival research. The video also examines the importance of understanding data based on its time collection, source, and nature, highlighting its role in research analysis and decision-making.

Takeaways

• 😀 Discrete variables are obtained through counting or categorization, such as population count or gender classification, while continuous variables are measured, like height, weight, and attitudes.
• 😀 Independent variables (or predictors) influence dependent variables (or outcomes), where the independent variable affects the dependent one in research studies.
• 😀 Moderating variables influence the relationship between independent and dependent variables, strengthening or weakening the correlation between them.
• 😀 Intervening variables explain the indirect relationship between independent and dependent variables, influencing outcomes through another factor, like motivation in a learning study.
• 😀 Control variables are factors controlled by the researcher to ensure they don't affect the relationship between independent and dependent variables in an experiment.
• 😀 Extraneous variables are factors beyond the researcher’s control that may introduce confusion in an experiment, like environmental changes or individual health conditions.
• 😀 Data can be categorized into primary (collected directly from sources) and secondary (obtained through intermediaries), where primary data involves direct interaction with subjects or events.
• 😀 Primary data includes observations, surveys, and interviews, while secondary data comes from previous studies, archival records, or published reports.
• 😀 Cross-sectional data is collected at one point in time to analyze a specific condition or phenomenon, offering a snapshot of a population at that moment.
• 😀 Time-series data is collected over multiple time periods to track developments or changes, such as tracking inflation or population growth over the years.
• 😀 Panel data combines cross-sectional and time-series data, collecting data from multiple subjects over several periods, offering a broader perspective of trends over time.

Q & A

What are the two main types of variables based on their values?

The two main types of variables based on their values are discrete variables and continuous variables. Discrete variables are obtained through counting or categorization, while continuous variables are obtained through measurements.

Can you explain the difference between discrete and continuous variables with examples?

Discrete variables are those that can be counted or categorized, such as the number of people or equipment, and include categorical variables like gender or occupation. Continuous variables, on the other hand, are measured, like height, weight, or academic performance (e.g., GPA).

What are the four types of variables categorized by their function?

The four types of variables based on their function are independent variables, dependent variables, moderating variables, and intervening variables.

What is an independent variable, and how does it affect dependent variables?

An independent variable, also known as the predictor or stimulus, is a variable that influences or causes a change in other variables, known as dependent variables. For example, in a study on technology use, the independent variables could be the ease of use and perceived risks, while the dependent variable is the interest in using the internet as a learning tool.

How does a moderating variable influence the relationship between independent and dependent variables?

A moderating variable affects the strength or direction of the relationship between an independent and a dependent variable. It can either strengthen or weaken the relationship or even change it from positive to negative or vice versa. For example, the attitude of a teacher can moderate the relationship between student motivation and learning outcomes.

What is an intervening variable, and how does it influence research outcomes?

An intervening variable, or mediating variable, explains the mechanism through which an independent variable affects a dependent variable. It is located between the independent and dependent variables. For example, in a study on teaching methods, motivation might be an intervening variable that explains how teaching methods affect student learning outcomes.

What are control variables, and why are they important in experiments?

Control variables are factors that researchers deliberately keep constant to prevent them from influencing the relationship between independent and dependent variables in an experiment. For example, if a company wants to test the effect of packaging design on sales, they must control factors like store location, shopping hours, and advertising to ensure a fair test.

How do extraneous variables affect research outcomes, and how can their impact be minimized?

Extraneous variables are variables that cannot be controlled but may still impact the results of an experiment. These variables can create confusion or skew the results. To minimize their impact, researchers can eliminate or limit their effects by controlling conditions and focusing on typical subjects or objects.

What is the difference between primary and secondary data?

Primary data is data collected directly from the source by the researcher through methods like surveys, observations, or interviews. Secondary data, on the other hand, is data collected by someone else and used by researchers for their own studies, often found in historical records, reports, or archives.

What are the types of data based on time of collection, and how are they different?

The three types of data based on the time of collection are cross-sectional data, time-series data, and panel data. Cross-sectional data is collected at one point in time, time-series data is collected at different time intervals, and panel data combines both cross-sectional and time-series data, allowing for more comprehensive analysis across time and subjects.