Randomized Block Design Rationale
The core challenge is to manage a variable that is strongly related (highly correlated) both to the independent variable (IV) and the dependent variable (DV). The goal is to ensure this correlated variable doesn't disproportionately influence the results across different levels of the IV.
Controlling Correlated Variables
The experimental design that best addresses this is the Randomized Block design. Here's why:
- Blocking Mechanism: Subjects are first grouped (or "blocked") based on the variable that is highly correlated with the dependent variable. For example, if age is highly correlated with the outcome, participants might be grouped into age blocks (e.g., 20-30, 31-40, etc.).
- Equal Representation: By forming these blocks, the experimenter ensures that each block (representing a specific level of the correlated variable) contains all the treatment conditions (levels of the independent variable).
- Reduced Error: Random assignment of treatments occurs *within* each block. This procedure isolates the variability due to the blocking variable, allowing for a clearer assessment of the IV's effect on the DV. It effectively controls for the influence of the extraneous correlated variable.
Evaluating Other Designs
Other designs are less suitable for this specific requirement:
- Repeated Measures: Involves using the same participants for multiple conditions, which doesn't inherently control for a pre-existing correlated variable between conditions.
- Randomized Groups: While random assignment helps distribute potential confounding variables, it doesn't specifically guarantee equal representation or control for a *known highly correlated* variable as effectively as blocking.
- Latin Square: Primarily used to control for two extraneous variables simultaneously (e.g., rows and columns), not typically the first choice for controlling a single, known correlated variable across IV levels.
Therefore, the Randomized Block design is the appropriate choice when an experimenter needs to ensure equal representation and control for a highly correlated variable.
Important Questions from Research Design
- Assertion (A): Due to the experiment of Time, Motion and Fatigue studies, a factory does not remain a factory, it is converted into a laboratory
Reason (R): Management succeeds or fails as human relations in business are intelligently or unintelligently handled Given below are two statements
Statement I An explanatory correlational design explains the degree of association among two or more. variables
Statement II In prediction correlational design, the investigator identifies variables that will positively predict an out come
Choose the correct answer.- Select the option based on the Assertion (A) and Reason (R) :
Assertion (A): Extraneous Variables can influence the results of a research study.
Reason (R): Extraneous variables are not controlled by the researcher. - ____________ is to explore the data without prior hypthesis.
- An experimental research design incorporates
(i) Random assignment of participants to treatment conditions
(ii) Effect on a dependent variable recorded
(iii) Observe placebo effect
(iv) Manipulation of independent variable
(v) Increase attrition
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