TOPIC INFO (UGC NET)
TOPIC INFO – UGC NET (Psychology)
SUB-TOPIC INFO – Research Methodology and Statistics (UNIT 2)
CONTENT TYPE – Detailed Notes
What’s Inside the Chapter? (After Subscription)
1. Introduction
2. ANOVA
2.1. ONE WAY ANOVA
2.2. TWO WAY ANOVA
2.3. MANOVA
2.4. Factorial ANOVA
3. Randomized Block Designs (RBD)
3.1. Introduction
3.2. Concept and Structure of RBD
3.3. Purpose of Randomized Block Design
3.4. Assumptions of RBD
3.5. Statistical Model of RBD
3.6. ANOVA Table Structure
3.7. Steps in Conducting a RBD
3.8. Advantages of RBD
3.9. Limitations of RBD
3.10. Comparison with Other Designs
3.11. Applications of RBD
4. Measures Design
4.1. Repeated Measures
5. Latin Squares
6. Cohort Studies
6.1. Framingham Cohort Study.
6.2. Swiss HIV Cohort Study.
6.3. The Danish Cohort Study of Psoriasis and Depression
6.4. Types of Cohort Studies
6.5. Strengths of a Cohort Study.
6.6. Limitations of Cohort Study.
7. Time Series
7.1. Interrupted Time-Series Design
7.2. Use of Time Series in Design
7.3. One-Group Pretest-Posttest Design
7.4. Time-Series Design
8. MANOVA
8.1. Introduction to MANOVA
8.2. How MANOVA Assesses the Data
8.3. Benefits of MANOVA
8.4. Multivariate Nature of MANOVA
8.5. Goal of MANOVA
8.6. Conducting the MANOVA
8.7. Steps in Applied Multivariate Research
9. ANCOVA
9.1. Introduction to ANCOVA
9.2. Role of Covariates
9.3. Comparison with Repeated Measures ANOVA
9.4. Difference Between ANOVA and ANCOVA
9.5. ANCOVA and Regression
9.6. SPSS Implementation Differences
10. Single Subject Designs
10.1. Introduction
10.2. Nature and Classification of Designs
10.3. Relevance to Evidence-Based Practice
10.4. Scientist-Practitioner Model
10.5. Clinical Application Process
10.6. Role of Observation and Data Collection
10.7. Examples of Target Behaviors
10.8. ABA and AB Designs
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Experimental Designs in Psychology
UGC NET PSYCHOLOGY
Research Methodology and Statistics (UNIT 2)
Introduction
Design of experiment means how to design an experiment in the sense that how the observations or measurements should be obtained to answer a query in a valid, efficient and economical way. The designing of the experiment and the analysis of obtained data are inseparable. If the experiment is designed properly keeping in mind the question, then the data generated is valid and proper analysis of data provides the valid statistical inferences. If the experiment is not well designed, the validity of the statistical inferences is questionable and may be invalid. It is important to understand first the basic terminologies used in the experimental design.
Experimental Unit:
For conducting an experiment, the experimental material is divided into smaller parts and each part is referred to as an experimental unit. The experimental unit is randomly assigned to treatment. The phrase “randomly assigned” is very important in this definition.
Experiment:
A way of getting an answer to a question which the experimenter wants to know.
Treatment:
Different objects or procedures which are to be compared in an experiment are called treatments.
Sampling Unit:
The object that is measured in an experiment is called the sampling unit. This may be different from the experimental unit.
Factor:
A factor is a variable defining a categorization. A factor can be fixed or random in nature. A factor is termed as a fixed factor if all the levels of interest are included in the experiment. A factor is termed as a random factor if all the levels of interest are not included in the experiment and those that are included can be considered to be randomly chosen.
Design of experiment:
One of the main objectives of designing an experiment is how to verify the hypothesis in an efficient and economical way. In the context of the null hypothesis of equality of several means of normal populations having the same variances, the analysis of variance (ANOVA) technique can be used. Note that such techniques are based on certain statistical assumptions.
If these assumptions are violated, the outcome of the test of a hypothesis may also be faulty and the analysis of data may be meaningless. So the main question is how to obtain the data such that the assumptions are met and the data is readily available for the application of tools like ANOVA. The designing of such a mechanism to obtain such data is achieved by the design of the experiment.
After obtaining sufficient experimental units, the treatments are allocated to the experimental units in a random fashion. Design of experiment provides a method by which the treatments are placed at random on the experimental units in such a way that the responses are estimated with the utmost precision possible.
ANOVA
An ANOVA test is a way to find out if survey or experiment results are significant. In other words, it helps you determine whether to reject the null hypothesis or accept the alternate hypothesis. Basically, you’re testing groups to see if there’s a difference between them. Examples of when you might want to test different groups:
- A group of psychiatric patients are trying three different therapies: counseling, medication and biofeedback. You want to see if one therapy is better than the others.
- A manufacturer has two different processes to make light bulbs. They want to know if one process is better than the other.
- Students from different colleges take the same exam. You want to see if one college outperforms the other.
- What Does “One-Way” or “Two-Way” Mean?
One-way or two-way refers to the number of independent variables (IVs) in your Analysis of Variance test.
One-way ANOVA has one independent variable (with 2 or more levels). For example: brand of cereal. - Two-way ANOVA has two independent variables (it can have multiple levels). For example: brand of cereal and calories.
What are “Groups” or “Levels”?
Groups or levels are different groups within the same independent variable. In the above example, your levels for “brand of cereal” might be Lucky Charms, Raisin Bran, Cornflakes — a total of three levels. Your levels for “Calories” might be: sweetened, unsweetened — a total of two levels.
Let’s say you are studying if an alcoholic support group and individual counseling combined is the most effective treatment for lowering alcohol consumption. You might split the study participants into three groups or levels:
• Medication only,
• Medication and counseling,
• Counseling only.
Your dependent variable would be the number of alcoholic beverages consumed per day. If your groups or levels have a hierarchical structure, then use a nested ANOVA for the analysis.
What Does “Replication” Mean?
It refers to whether you are replicating (duplicating) your tests with multiple groups. With a two-way ANOVA with replication, you have two groups and individuals within each group perform more than one condition. If you only have one group taking multiple tests, you would use without replication.
Types of Tests:
There are two main types: one-way and two-way ANOVA. Two-way tests can be with or without replication.
- One-way ANOVA (between groups): used when you want to test two or more groups to see if there’s a difference between them.
- Two-way ANOVA without replication: used when you have one group and you are testing it under different conditions (e.g., before and after medication).
- Two-way ANOVA with replication: used when there are multiple groups and each group is exposed to multiple conditions.