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Block 3 Unit 1 Single Factor Design

1. Define research design and indicate its purposes.

• Research design is the plan, structure and strategy of investigation conceived so as to obtain answers to research questions and to control variance.
• The plan is the overall scheme or program of the research.
• The structure is the outline of the research design, and the scheme is the paradigm of operation of the variable.
• The strategy includes the methods to be used to gather and analyse the data.
• It explains how the research objective will be reached and how the problems encountered in the research will be tackled.

2. Define factors, levels and treatment in research design.

1. Factors: The independent variable(s) of an experiment. An experiment always has at least one factor.
2. Levels: A particular value of an independent variable. An independent variable has at least two levels.
3. Treatment: A particular set of experimental conditions. For example, in a 2x2 factorial experiment, subjects are assigned to 4 treatments.

3. Define Single Factor Design.

When there is only one independent variable, a single factor design is used.

4. Differentiate between between group design and within subject design.

• Between-group design: Subjects are randomly assigned to different treatment conditions, and the effect of different conditions on different groups of subjects are compared.
• Within-subject design: The same individuals are treated differently at different times, and their scores are compared after being subjected to different treatment conditions.

5. Describe more than two randomised group design or Multi group design.

In a more than two randomised group design, also known as a multi-group design, a researcher uses more than two experimental groups and may also include one control group. This type of design is a variation of a between-groups design, where different groups of participants are subjected to different experimental conditions.

• Purpose: The purpose of a multi-group design is to examine the effects of different levels or types of an independent variable on a dependent variable.
• Structure: In this design, participants are randomly assigned to one of several groups, each of which receives a different treatment.
• Examples:
  • An educational experiment might use three experimental groups, each receiving a different schedule of reinforcement, and a control group receiving no reinforcement, to study the effect of reinforcement schedules on the rate of learning a verbal task.
  • An experiment might look at the effect of different teaching methods (Method A, B, C, and D) on student performance, where each group is taught using a different method.
• Random Assignment: As with all between-groups designs, random assignment of participants is critical to ensure that any differences observed between groups are due to the independent variable and not other factors. This means each participant has an equal chance of being assigned to any of the groups, experimental or control.
• Statistical Analysis: When analysing data from a multi-group design, researchers commonly use statistical techniques such as one-way analysis of variance (ANOVA) and Duncan’s range test. These tests determine if there are significant differences between the means of the different groups.
• Control Group: The control group, if included, serves as a baseline for comparison, against which the effects of the experimental manipulations can be assessed. Participants in the control group receive no treatment or a standard treatment.
• Number of Groups: A multi-group design may have three or four experimental groups, or more, depending on the research questions.
• Interpretation: By comparing the outcomes of the different groups, researchers can determine which level or type of the independent variable is most effective or has a different impact on the dependent variable.

In summary, a more than two randomised group design is a type of between-groups design that involves the random assignment of participants to multiple experimental groups (and possibly a control group) to test the effects of different conditions of an independent variable, and it is particularly useful when you have more than two treatment conditions you want to compare.

6. Define within subject design. State the two categories of within subject design.

• Within-subject design, also known as repeated measures design, involves treating the same individual differently at different times and comparing their scores after different treatment conditions.
• The two categories of within-subject design are:
  • Two conditions within-subject design: This is the simplest design where all subjects experience two conditions.
  • Multiple conditions within-subject design: In this design, all subjects experience more than two conditions.

7. Describe what is 2 conditions and Multiple conditions in “within subject design”? Give suitable examples.

• Two conditions within-subject design: This design compares two conditions, where all subjects experience both. For example, a researcher might compare reaction times to red and green colours, where each participant is exposed to both.
• Multiple conditions within-subject design: This involves more than two conditions, with all subjects experiencing each. For example, a researcher might examine the effect of different colours (red, green, and yellow) on reaction time, where each participant is exposed to all three.

8. What do you mean by research design?

Research design is the plan, structure, and strategy of an investigation to answer research questions and control variance. It includes the overall scheme of the research from hypothesis to data analysis.

9. Discuss in detail the functions of research design?

• The main function of research design is to explain how to find answers to the research question. It also involves controlling variance.
• A research design provides a framework for the investigator to develop a strategy to find an answer to a research question.

10. Discuss with example when to use between subject research design?

A between-subjects design is used when you want to compare the effects of different treatments or conditions on separate groups of subjects. For example, when a researcher wants to compare the effectiveness of a new teaching method against a traditional method, they would randomly assign different students to each group and then measure their performance at the end.

11. When to use within subject research design? Explain with examples.

A within-subject design is used when you want to compare the effects of different conditions on the same group of subjects. For example, a researcher may want to examine the effect of different levels of noise on task performance by exposing each participant to multiple levels of noise and measuring their performance at each level. This design is useful when you have a small number of subjects available for extended experimentation.

12. Differentiate between within subject and between subject experimental design.

• In a between-subject design, different groups of subjects are exposed to different treatments, and the researcher compares the effects between these groups.
• In a within-subject design, the same group of subjects is exposed to all treatments, and the researcher compares the effects within the same group of subjects. The between group design requires more participants because each participant is only in one group, and the within subject design requires fewer participants because each participant is in all groups..

13. What are the functions of a research design.

Functions of a Research Design

• To provide answers to research questions. This means a research design should enable the researcher to answer their research question as validly, accurately, and economically as possible. Research problems are stated in the form of hypotheses, and the research design guides the researcher in collecting data to test these hypotheses.
• To control variance. This involves using the MAXMINCON principle:
  • Maximise systematic variance: This refers to the variability in the dependent measure due to the manipulation of the independent variable. A good design ensures the independent variable varies enough to separate its effect from the overall variance.
  • Control extraneous variance: This involves managing variables that may confound the results of the experiment. Methods like randomisation, elimination and matching are used for this purpose.
  • Minimise error variance: This refers to the variance that occurs due to uncontrollable variables.

14. What is Matched group design?

Matched Group Design

• A matched group design, also known as a randomised block design, is a type of between-subjects design.
• In this design, all subjects are first tested on a common task. Then, they are formed into groups based on their scores on this task, creating equivalent groups.
• Different values of the independent variable are introduced to each group, and the mean scores of the dependent variable are compared.
• The matching variable is usually different from the variable under study, but is generally related to it.
• The two groups are not required to be of the same size, but large differences should be avoided.
• The most important aspect of using a matched group design is identifying the variables on which matching will be done. The matching variable should have a high correlation with the dependent variable.
• Sometimes, the dependent variable itself is used as the matching variable, or an independent measure related to the dependent variable may also be used.

15. How do we match in Matched Group Design?

How Matching is Done in Matched Group Design

There are two primary ways of matching in a matched group design:

• Matching by pairs:
  • Subjects are matched in pairs, ensuring that each person in the first group has a match in the second group. For instance, if a researcher wants to study two different teaching methods, they might first administer an academic achievement test to all participants. Then, they would match subjects with similar scores into pairs, and place one member of each pair in one of the two groups.
• Matching by Mean and Standard Deviation:
  • When matching subjects individually is impractical, groups can be matched in terms of their mean and standard deviation on the matching variable.
  • For example, if intelligence is the matching variable, the researcher would obtain the mean and standard deviation of intelligence scores for both groups to ensure they are comparable.
• The matching variable is selected because it is related to the dependent variable. Matching can be done based on variables such as age, educational level, or learning ability. However, choosing appropriate matching variables is crucial.

Important Points

• The research design is used to control error variance.
• When we have more than one independent variable we can't use single factor design.
• Order effect occur in within group design.
• Matched group technique is used in between subject design.
• In the within subject design each subject receive number of treatment.
• In matched group technique the variable selected for matching should be related to dependent variable.
• In two randomised group design ‘t’ test is most commonly used.
• In between subject design we have two groups one is experimental group and other is control group.