# Two-Factor Data Analysis: Definition and Examples

Table of Contents

## Introduction

The process of setting an experiment implies that every single factor affecting the dependent variable should be identified and that the crucial one should be selected carefully. In a range of scenarios, the key variable is going to be affected by not one, but two or more factors (Groebner, Shannon, & Fry, 2014). For these purposes, a two-factor analysis, or a two-factor experiment, each allowing isolating the effects of two variables on the target one, is applied.

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## Analysis

The term mentioned above is quite self-explanatory. As stressed in the definition, the two-factor experiment incorporates two independent variables that alter the dependent one. For example, in the nursing environment, a student may need to explore the effects that factors such as age and gender have on the progress of a particular disease on patients. In the identified scenario, the disease or the disorder under analysis will be the independent variable, whereas the age and the gender of the participants will be classified as independent ones.

In other words, age and gender are going to be the two factors that will define the course of the experiment. Consequently, the experiment design can be considered as two-factor (Hahs-Vaughn & Lomax, 2013).

As far as the number of levels in the identified example is concerned, the experiment can be considered complex, as each of the variables identified above has its own set thereof. Particularly, the gender-related factor is going to incorporate two key levels (i.e., the male and the female genders). Granted that, from a more recent viewpoint, the number of genders may exceed two (for instance, if including transgender people into the account), it will be reasonable to use the categories that most researchers tend to agree on in order to maintain the integrity of the study (Offerman-Zuckerberg, 2013). The age groups, in their turn, may vary depending on the classification approach chosen by the researcher.

For instance, there may be two primary groups (children and adults), three categories) (e.g., children, adults, and senior citizens), five crucial variables (children, adolescents, young adults, adults, senior citizens), etc. Therefore, it can be assumed that the first variable (gender) has two basic levels, whereas the number of levels in the second one is set by the researcher (Bausell, 2015).

Reasonably enough, the latter factor (age) will measure the responsiveness of the patients toward a particular form of treatment or therapy in relation to their age. In other words, the factor under analysis will permit identifying whether the patients’ age has any significant effect on the efficacy of the treatment or therapy provided, as well as on the progress of the disease or disorder. As far as the former is concerned, gender will help locate whether male and female patients show the same response to the treatment and develop the same symptoms as the disease progresses (Reddy, 2014). In other words, the variables mentioned above will help locate the amount of medicine or the intensity of the therapy administered to the target audience.

## Conclusion

The analysis of a particular phenomenon often requires exposing the subject matter to the impact of a variety of factors (i.e., two or more), which means that the application of a two-factor analysis is often a necessity. By isolating the two independent variables that affect a particular phenomenon, one is likely to retrieve accurate results. Thus, the experiment outcomes can be deemed as credible, reliable, and valuable.

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## Reference List

Bausell, B. (2015). The design and conduct of meaningful experiments involving human participants: 25 scientific principles. Oxford: OUP.

Groebner, D. F., Shannon, P. W., & Fry, F. C. (2014). Analysis of variance. In Business statistics (9th ed.) (pp. 543-546). Upper Saddle River, NJ: Pearson.

Hahs-Vaughn, D. L., & Lomax, R. G. (2013). An introduction to statistical concepts (3rd ed.). New York, NY: Routledge.

Offerman-Zuckerberg, J. (2013). Gender in transition: A new frontier. New York, NY: Springer Science & Business Media.

Reddy, N. V. (2014). Statistical methods in psychiatry research and SPSS. Chicago, IL: CRC Press.

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