1. a) In this design, we have two groups, one is the experimental group and the other is the control group. The number of subjects is decided, and same number of subjects are equally and randomly placed in both groups. The subjects should be matched on age, sex education, etc in both the groups (Myers & Hansen, 2012). The independent variable is decided, this independent variable is controlled and manipulated by the researcher, and the effect of this is called the dependent variable. The experimental group is administered the independent variable, while the control group is not administered the independent variable, instead a placebo is given to them. After this intervention, tests are administered to subjects of both the groups. Scoring of tests are done, and can be analyzed by conducting Independent Samples t Test.

(b) In this design, we have three independent variables or three interventions. This design is best explained using an example. For Ex. A teacher who wants to see the effectiveness of various teaching methods, uses three different methods, viz. i. Lecture Method, ii. Interactive Method, iii. Student led Teaching Method. Now, the second independent variable can be duration of the class, viz. i. 25 minutes ii. 45 minutes. And last independent variable can be the gender, viz. i. girls ii. boys. So, once intervention is given in all these conditions, scoring should done. Analysis can be done using three-way anova.

2. An experiment involving an experimental group and a control group

Conclusions can be drawn from between-subject experiments by making comparisons among the behaviors of various groups of subjects. In an experiment involving an experimental group and a control group, the experimenter would have to choose more than one or two subjects for each treatment condition. The experimenter would apply a particular value of the independent variable to the subjects and then measure the dependent variable. In a control group, the experimenter will carry out the same exact procedures in an experimental condition. The only difference is the manipulation of the experiment (Myers & Hansen, 2012).

A factorial design with three independent variables with three and two levels separately

A laid out factorial design example would be a researcher wanting to investigate what can help increase ACT scores. The three independent variables would be 1) an ACT intensive class 2) an ACT prep class and 3) extra homework. The independent variables for the two levels would be answering yes or no to each variable.

3. A between-subjects design has different participants that take part in each condition of the experiment (Myers & Hansen, 2012). We can draw conclusions from a between-subjects’ experiments by making comparisons of different group subjects. A two-experiment group design is used to create more accurate information (Myers & Hansen, 2012).

An experimental group contains subjects that are under experimental conditions and a control group has subjects under control conditions. Control conditions involve determining a value a dependent variable with no experimental manipulation of an independent variable (Myers & Hansen, 2012). There are typically two treatments involved and, in some experiments, there is a no-treatment variable. This measures participants response without trying to alter them (Myers & Hansen, 2012). This control condition can be used to compare and we can see whether participants performance in the experiment conditions was better (Myers & Hansen, 2012).

A factorial design is one in which we study two or more independent variables at the same time (Myers & Hanse, 2012). The independent variables are called factors and each factor has two or more values or levels (Myers & Hansen, 2012). The main effect is the action of one independent variable in an experiment (Myers & Hansen, 2012). We should look for interactions of one independent variable changing across levels of another independent variable (Myers & Hansen, 2012). Each factor should be named and the basic components should be diagramed (Myers & Hansen, 2012). A design matrix is preferable. To calculate the results, one should use a three-way anova.

4. Per the article between subjects designs are invaluable in certain situations, and give researchers an experiment with very little contamination by extraneous factors (Explorable.com, 2020). This type of design is often called an independent measures design because every participant is only subjected to a single treatment. This lowers the chances of participants suffering boredom after a long series of tests or, alternatively, becoming more accomplished through practice and experience, skewing the results (Explorable.com, 2020). Per the e-book, between subjects design is a design in which different subjects take part in each condition of the experiment (Myers & Hansen, 2012). This design has multiple variables or levels of variables that can be tested simultaneously, and with enough testing subjects, a large number of tests can be tested. And two or more separate groups can be compared using the berween subjects design.

6. Between-subjects designs are used when different subjects participate in each condition of the experiment. There are several advantages to using between-subjects designs such as 1) it is usually possible to assign one individual to one of the several treatments during the experiment 2) the differences between groups are caused by the differing treatments rather than to other treatment factors that can occur when the same individual is measured more than once 3) different people can be tested by each condition, therefore, each individual will be exposed to a single user interface (Webcourse, n.d.).Between-subjects designs are easier to set up, especially when a person has multiple independent variables and has shorter sessions than within-subjects which makes the sessions less tiring (Budiu, 2018). Also, the between-subjects designs can be completed quickly and multiple treatments and treatment levels can be tested at the same time (Statistics How To, n.d.).