Anonymous
Oct 19
causal-comperative non experimental design?
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ChatGPT
Oct 19
A causal-comparative non-experimental design is a research design that involves comparing two or more groups that have already been exposed to different levels of an independent variable. This type of design is used to investigate the relationship between variables and determine if there is a causal relationship between them. Unlike experimental designs, causal-comparative designs do not involve manipulating the independent variable, but rather observe and compare existing differences between groups. This type of design is often used in situations where it is not feasible or ethical to conduct an experiment, such as studying the effects of smoking on lung cancer in humans.
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Google Gemini AI
Oct 19
Causal-Comparative Non-Experimental Design
A causal-comparative non-experimental design is a research design used to investigate the relationship between a presumed cause (independent variable) and an effect (dependent variable) without manipulating the independent variable.
Characteristics:
- Non-experimental: The researcher does not manipulate the independent variable.
- Causal (Comparative): The design aims to establish a causal relationship between the variables, but the causality is inferred through comparison.
- Retrospective: Data is collected after the presumed cause has occurred.
- Observational: The researcher observes the natural variation in the independent and dependent variables.
Advantages:
- Provides information about the relationship between variables without costly or unethical experimentation.
- Can be used to explore complex relationships in natural settings.
- Suitable for studying historical events or variables that cannot be manipulated.
Disadvantages:
- Limited control over confounding variables, which can threaten internal validity.
- Cannot prove causality with absolute certainty.
- May be subject to selection bias or other forms of bias.
Steps:
1. Identify the presumed cause (independent variable) and the effect (dependent variable).
2. Collect data on both variables from two or more groups.
3. Compare the groups to determine if there are significant differences in the dependent variable.
4. Control for confounding variables by using matching, stratification, or statistical adjustments.
5. Draw conclusions about the causal relationship based on the comparisons and the control measures.
Example:
A researcher wants to investigate the relationship between cigarette smoking (cause) and lung cancer (effect).
- Step 1: Identify smoking as the independent variable and lung cancer as the dependent variable.
- Step 2: Collect data on smoking habits and lung cancer rates from two groups: smokers and non-smokers.
- Step 3: Compare the groups and find a significantly higher rate of lung cancer among smokers.
- Step 4: Control for confounding variables (e.g., age, socioeconomic status) using statistical adjustments.
- Step 5: Conclude that smoking is a likely cause of lung cancer, based on the association and the control measures.
A causal-comparative non-experimental design is a research design used to investigate the relationship between a presumed cause (independent variable) and an effect (dependent variable) without manipulating the independent variable.
Characteristics:
- Non-experimental: The researcher does not manipulate the independent variable.
- Causal (Comparative): The design aims to establish a causal relationship between the variables, but the causality is inferred through comparison.
- Retrospective: Data is collected after the presumed cause has occurred.
- Observational: The researcher observes the natural variation in the independent and dependent variables.
Advantages:
- Provides information about the relationship between variables without costly or unethical experimentation.
- Can be used to explore complex relationships in natural settings.
- Suitable for studying historical events or variables that cannot be manipulated.
Disadvantages:
- Limited control over confounding variables, which can threaten internal validity.
- Cannot prove causality with absolute certainty.
- May be subject to selection bias or other forms of bias.
Steps:
1. Identify the presumed cause (independent variable) and the effect (dependent variable).
2. Collect data on both variables from two or more groups.
3. Compare the groups to determine if there are significant differences in the dependent variable.
4. Control for confounding variables by using matching, stratification, or statistical adjustments.
5. Draw conclusions about the causal relationship based on the comparisons and the control measures.
Example:
A researcher wants to investigate the relationship between cigarette smoking (cause) and lung cancer (effect).
- Step 1: Identify smoking as the independent variable and lung cancer as the dependent variable.
- Step 2: Collect data on smoking habits and lung cancer rates from two groups: smokers and non-smokers.
- Step 3: Compare the groups and find a significantly higher rate of lung cancer among smokers.
- Step 4: Control for confounding variables (e.g., age, socioeconomic status) using statistical adjustments.
- Step 5: Conclude that smoking is a likely cause of lung cancer, based on the association and the control measures.
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