Variables-in-Quantitative-Research-An-Overview.pptx

The types of Qualitative Variables in Practical Research 2

1. Variables in Quantitative Research: An Overview Welcome to an exploration of variables in quantitative research. This presentation will guide you through the essential types of variables, how they function, and their critical role in ensuring rigorous and reliable research outcomes. Understanding these distinctions is fundamental for designing effective studies, analyzing data accurately, and drawing valid conclusions. M

2. Core Quantitative Variables: Independent, Dependent, and Control Independent Variable (IV) The element manipulated or changed by the researcher to observe its effect. For instance, testing different drug dosages (e.g., 10mg, 20mg) on patients.

3. Core Quantitative Variables: Independent, Dependent, and Control Dependent Variable (DV) The measured outcome that is expected to be affected by the IV. An example would be the observed reduction in blood pressure (e.g., 10mmHg decrease) after administering a drug.

4. Core Quantitative Variables: Independent, Dependent, and Control Control Variable Factors held constant to prevent them from influencing the DV, ensuring that observed effects are due to the IV. For example, maintaining patient age within a narrow range (e.g., 30-40 years).

5. Influential Variables: Extraneous and Confounding In research, variables beyond the IV and DV can significantly impact results. Extraneous Variable Uncontrolled factors that might influence the Dependent Variable, but are not the focus of the study. An example is a participant's sleep quality affecting their reaction time in a cognitive test.

6. Influential Variables: Extraneous and Confounding In research, variables beyond the IV and DV can significantly impact results. Confounding Variable An extraneous variable that correlates with both the Independent Variable and the Dependent Variable, potentially distorting the true relationship. For instance, exercise levels when studying the impact of diet on weight.

7. Descriptive Variables: Constant and Attribute Constant Variable A characteristic that remains fixed and does not vary across subjects or conditions. For example, the gravitational constant used in physics calculations.

8. Descriptive Variables: Constant and Attribute Attribute Variable Pre-existing characteristics of research subjects that cannot be manipulated by the researcher. This includes factors such as socioeconomic status, educational background, or gender (e.g., 50% male participants in a study).

9. Measurement Variables: Continuous and Dichotomous 1 Continuous Variable Can take any value within a given range, including fractions and decimals. Examples include temperature (e.g., 25.3°C) or time (e.g., 1.5 hours). These variables allow for precise measurement. 2 Dichotomous Variable Possesses only two distinct categories or values. Common examples are Yes/No, Pass/Fail, or Male/Female. These are often used for binary outcomes.

10. Observational Variables: Latent and Manifest Latent Variable Abstract concepts that cannot be directly observed or measured. Examples include intelligence, motivation, or stress level. Researchers infer these through observable indicators. Manifest Variable Variables that are directly observable and measurable. These are the indicators used to assess latent variables, such as test scores (for intelligence), hours worked (for motivation), or heart rate (for stress).

11. Causal Variables: Exogenous and Endogenous 1 Exogenous Variable Variables that influence others within a model but are not influenced by any other variable within that model. For instance, education level affecting income. 2 Endogenous Variable Variables that are influenced by other variables within the same model. An example is income, which is influenced by factors like education level and work experience.