Question

explain how experimental design, analysis of variance, and chi square test are used in research.

Answer 1

Experimental design are used in research

Experimental research designs are the primary approach used to investigate causal (cause/effect) relationships and to study the relationship between one variable and another. This is a traditional type of research that is quantitative in nature. In short, researchers use experimental research to compare two or more groups on one or more measures. In these designs, one variable is manipulated to see if it has an effect on the other variable. Experimental designs are used in this way to answer hypotheses. A hypothesis is a testable statement that is formulated by the researcher to address a specific question. The researcher designs an experimental study which will then support or disprove the hypothesis.

To further the discussion of experimental research in future modules, it is important to understand the basic terminology related to experimental research. Following is a list of key terminology:

• Independent Variable – This is the variable that will be manipulated, the “cause” or treatment variable. This variable may be an activity or characteristic that the researcher believes will make a difference.
• Dependent Variable – This variable is the “effect” or outcome of manipulating the independent variable. The only constraint is that the outcome must be measurable.
• Experimental Group – The group that receives the treatment being investigated.
• Control Group – The group that remains the same in order to have something to compare the experimental group against.

Experimental research is based on a methodology that meets three criteria that are important if the results are to be meaningful. These criteria are as follows:

• Random Assignment – Test subjects must be randomly assigned to the treatment groups to control for creation of groups that may systematically differ in another way that impacts the outcome of the treatment.
• Experimental Control – All aspects of the treatments are identical except for the independent variable. If all other factors are controlled and kept constant, then if measurable differences are found in the outcomes, the researcher can be assured that the difference is due the independent variable (treatment).
• Appropriate Measures – The measures or outcomes must appropriate for testing the hypothesis. The outcome measured must represent the idea being tested in the hypothesis in order for the results to be valid.

Considering the definitions and criteria from above, it is now to time to explore an example of experimental research using those concepts.   Let’s say that a researcher wanted to investigate the effects of using flipped classroom teaching techniques in an American history course. The hypothesis being tested is that the flipped classroom teaching style will result in higher test scores among the students. The researcher will begin by randomly assigning students into two different sections of the course. The first section will be taught using the traditional lecture format. The second section will be taught used flipped classroom teaching techniques. The learning objectives and content for both sections will be identical. Both sections will be given identical exams throughout the semester and the scores between the two sections will be compared to assess student learning. The flipped classroom teaching style is the independent variable. The dependent variable is the test scores. The experimental group is the section of the course where the flipped classroom technique is being used and the control group is the section that continues to utilize the traditional lecture format. This is a classic example of the use of experimental research design.

Analysis of variance are used in research

Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. Researcher use the ANOVA test to determine the influence that independent variables have on the dependent variable in a regression study.

The ANOVA test is the initial step in analyzing factors that affect a given data set. Once the test is finished, a researcher performs additional testing on the methodical factors that measurably contribute to the data set's inconsistency. The researcher utilizes the ANOVA test results in an f-test to generate additional data that aligns with the proposed regression models.

The ANOVA test allows a comparison of more than two groups at the same time to determine whether a relationship exists between them. The result of the ANOVA formula, the F statistic (also called the F-ratio), allows for the analysis of multiple groups of data to determine the variability between samples and within samples.

If no real difference exists between the tested groups, which is called the null hypothesis, the result of the ANOVA's F-ratio statistic will be close to 1. Fluctuations in its sampling will likely follow the Fisher F distribution. This is actually a group of distribution functions, with two characteristic numbers, called the numerator degrees of freedom and the denominator degrees of freedom.

A researcher might, for example, test students from multiple colleges to see if students from one of the colleges consistently outperform students from the other colleges. In a business application, an R&D researcher might test two different processes of creating a product to see if one process is better than the other in terms of cost efficiency.

The type of ANOVA test used depends on a number of factors. It is applied when data needs to be experimental. Analysis of variance is employed if there is no access to statistical software resulting in computing ANOVA by hand. It is simple to use and best suited for small samples. With many experimental designs, the sample sizes have to be the same for the various factor level combinations.

ANOVA is helpful for testing three or more variables. It is similar to multiple two-sample t-tests. However, it results in fewer type I errors and is appropriate for a range of issues. ANOVA groups differences by comparing the means of each group and includes spreading out the variance into diverse sources. It is employed with subjects, test groups, between groups and within groups.

Chi square test are used in research

The Chi-Square test is a statistical procedure used by researchers to examine the differences between categorical variables in the same population.

For example, imagine that a research group is interested in whether or not education level and marital status are related for all people in the U.S.

After collecting a simple random sample of 500 U.S. citizens, and administering a survey to this sample, the researchers could first manually observe the frequency distribution of marital status and education category within their sample.

The researchers could then perform a Chi-Square test to validate or provide additional context for these observed frequencies.

Chi-Square calculation formula is as follows:

The Chi-Square test is most useful when analyzing cross tabulations of survey response data.

Because cross tabulations reveal the frequency and percentage of responses to questions by various segments or categories of respondents (gender, profession, education level, etc.), the Chi-Square test informs researchers about whether or not there is a statistically significant difference between how the various segments or categories answered a given question.

Important things to note when considering using the Chi-Square test

(i) Chi-Square only tests whether two individual variables are independent in a binary, “yes” or “no” format.

Chi-Square testing does not provide any insight into the degree of difference between the respondent categories, meaning that researchers are not able to tell which statistic (result of the Chi-Square test) is greater or less than the other.

(ii) Chi-Square requires researchers to use numerical values, also known as frequency counts, instead of using percentages or ratios. This can limit the flexibility that researchers have in terms of the processes that they use.