Question

An experimenter is attempting to determine the effects of smoking on very low birth weight (VLBW) and of VLBW on IQ. Which of the following statements is correct? I. Smoking is an independent variable. II. Smoking is a dependent variable. III. VLBW is an independent variable. IV. VLBW is a dependent variable. (A) I only (B) II and IV only (C) I, III, and IV only (D) II, III, and IV only

Short answer

The correct answer is (C) I, III, and IV only.

Step-by-step solution

Understanding Variables in the Experiment

Identify which variables are manipulated and which are measured in the experiment. An independent variable is one that the experimenter manipulates, and a dependent variable is one that is measured or affected.

Identifying Smoking as a Variable

Determine whether smoking is manipulated or measured. Smoking is manipulated to observe its effects, making it an independent variable.

Determining the Role of VLBW (Very Low Birth Weight)

VLBW is measured as an outcome of smoking and also serves as a factor to observe its effect on IQ. This dual role indicates that VLBW is a dependent variable when considering the impact of smoking, and an independent variable in relation to IQ.

Cross-Referencing with the Statements

Compare the identified roles of smoking and VLBW with the given statements: I. True, as smoking is an independent variable. II. False, as smoking is not a dependent variable. III. True, as VLBW can act as an independent variable when analyzing its effect on IQ. IV. True, as VLBW is a dependent variable in the context of smoking’s effects.

Selecting the Correct Answer

From the given options, the correct statements are I, III, and IV. Therefore, the correct choice is (C).

Key concepts

Independent Variables

An independent variable is a core concept in experimental design. It is the factor that researchers manipulate to observe its effects on other variables. Think of it as the cause in a cause-and-effect relationship. For example, in the given exercise, smoking is the independent variable because the experimenter introduces or controls this factor to study its impact on Very Low Birth Weight (VLBW). By manipulating the independent variable (in this case, smoking), researchers aim to determine its effect on the dependent variable.

• Always controlled or introduced by the researcher.
• Can have one or more levels or conditions.
• Examples: dosage of a drug, presence of smoking, or type of teaching method.

Dependent Variables

A dependent variable is what researchers measure in an experiment. It is the effect or outcome that is observed and is hypothesized to be influenced by the independent variable. In our exercise, VLBW serves as a dependent variable when exploring the impact of smoking. Researchers measure the VLBW to see if it differs between the smoking and non-smoking groups, reflecting the effect of the independent variable.

• Gets measured by the researcher.
• Shows the possible effect of the independent variable.
• Examples: test scores, levels of stress, birth weights.

Experimental Design

Experimental design refers to the framework that outlines how an investigation is conducted. It ensures that the study provides reliable and valid results. Key components include the selection of independent and dependent variables, how participants are assigned to groups, controls to prevent bias, and procedures to ensure replicability. In our exercise, the experiment design would involve controlling smoking among subjects to observe its effects on VLBW and later on IQ levels.

• Defines how variables will be manipulated and measured.
• Helps in minimizing errors and biases.
• Ensures effective data collection and analysis.

Cause and Effect Relationships

Understanding cause and effect relationships is crucial in experiments. It helps in identifying how one variable influences another. In our example, we look at how smoking (cause) affects VLBW (effect) and how VLBW (cause) further impacts IQ levels (effect). Establishing these relationships helps in understanding real-world phenomena and making predictions based on experimental data.

• Helps in identifying direct connections between variables.
• Crucial for making informed conclusions and decisions.
• Needs control and careful observation to avoid confounding factors.