Explain the difference between multiple independent variables and multiple levels of independent variables. Which is better?
It is important to note that design experiment requires researchers to decide on the type of dependent variable that they should use in their respective study and distinguish the same from the independent variables. However, every research ought to have a single dependent variable, but the independent variables can be many, an aspect that makes the independent variables be considered as parameters. Therefore, for our case, multiple independent variables indicate the use of many parameters in conducting a study. For example, if we take a survey to find the extent of obesity in a given area, and take the weight as the dependent variable, other aspects such as sex, age, and type of food consumed will thus be considered as the multiple independent variables for the study. However, when the study further seeks to understand about the particular independent variables, by including more levels on the issue, it is the referred to as the multiple levels of independent variables. For example, taking sex as an independent variable to understand whether an individual is a male or female indicates other multiple levels of the respective variables (Lee, 2012).
Different scholars have however appreciated the fact that the use of multiple levels in research is better and can be justified by various reasons. The multiple levels of independent variables are vitals as they allow scholars to evaluate each independent variable more comprehensively by placing the aspect under varied conditions. However, the importance of the above approach is that a person can use a single experiment to get a lot of information as compared to other experiments with a single variable. An experiment with multiple levels is more appealing in studies conducted with an assumption that various factors cause an issue under study.
The key advantage of using multiple independent variables is the fact that it offers researchers an opportunity to understand effects of one independent variable on the other, a feature that is known as the existing interaction. This is common in multiple linear regression analysis and uses control variables to justify the reason for any form of variation that may exist in a given survey. However, there are concerns that the use of multiple levels make a study complicated, an aspect that makes the focus on the main variables difficult. Therefore, the choice of the approach to use is based on the nature of research. While multiple independent variables may suit one study, multiple levels will be more efficient in other studies.
What is blocking and how does it reduce "noise"? What is a disadvantage of blocking?
Blocking as a research technique refers to the process through which a given research subject is subdivided into similar classes for purposes of clarity. Noise, as will be used in the study, indicates the unnecessary aspects that hinder the realization of the survey objective. Therefore, through blocking process, the noise is reduced, a feature that enhances the treatment effect felt through the minimization of the variability effect. Nonetheless, blocking is also considered as a methodology through which grouping of experimental runs is done to establish groups with similar aspects. The concept is mostly applicable in design experiments to help reduce the noise factors as it significantly combats and evades the unwanted variability. The approach is mostly used due to its simplicity and the fact that it can be utilized further for inference of purposes as by reducing noise; it enhances the chance of a researcher to detect an effect.
The chance of finding variability amid blocked subjects is reduced significantly, hence obtaining more relevant and precise information. Blocking in experimental design enables researchers to enhance precision through achieving homogeneity among the blocked subjects under study (Thyer, 2012). The primary disadvantage of the above method is that it is not effective in research with extreme variations, for example, in the cases where after the subdivision, the groups fail to show similarity. The behavior, therefore, reduces the intended effect expected by the researcher of finding more precise information from the study. Another disadvantage of blocking is the fact that it complicates experiments and if not properly done, it may result in the realization of wrong findings from a given study.
What is a factor? How can the use of factors benefit a design?
A factor can just be regarded as a variable under study, and in the case of more than a single variable, researchers ought to understand the key effect of each factor as well as the resulting effect realized through the interaction of different variables. Besides, a factor can also be explained by a variable that contains multiple levels. For example, if a case a study has three factors with two primary levels, it is referred as 2*3 factorial designs. The factorial design, therefore, is an approach through which researchers can understand the real world effect of various factors more efficiently. A benefit of the above method is the fact that a greater precision is achieved in establishing the overall main factor effects (Trochim & Donnelly, 2001). Researchers that use the approach can understand the interaction effect amid variables more efficiently, and the use of more factors enhances the validity of the study in making a solid conclusion on the subject study.
Explain the main effect and interaction effect
The main effect refers to the impact realized from a single independent variable in isolation of the others while interaction effect relates to the effect experienced as a result of the interaction with other factors. The aim of the researcher, therefore, is to understand the outcome experienced from each variable and at different levels of the respective factors. Nonetheless, the main effect can be explained to occur when a scholar identifies a difference in the overall mean amid the various levels of a single factor (however, all conditions of the second variable must be considered). However, for the case of the interaction effect, one factor has an influence on the other as it modifies the second aspect, an aspect that makes the scholar focus on the mean difference in just the individual cells (Roberts & Ilardi, 2003).
How does a covariate reduce noise?
The key issues that contribute to the development of noise in the experimental design include the systematic biases or errors that are obtained due to variability amid experimental units. For example, aspects of replication are often experienced through imposing similar treatment to a given experiment, and variability can be measured through conducting repeat measurements. Another source of noise or error in experiments is the use of ineffective instruments or human error that may contribute to variation in the various research experiments conducted. However, scholars assert that while some noise in experiments can be controlled, others cannot, because of different reasons and the fact that not all causes are known. Therefore, the methodologies used in the control of noise in experimental research vary based on a range of issues such as the particular type of noise factor that a study is dealing with.
According to Lee, (2016) the primary approach used to reduce error (noise) is through the application of the blocking strategy, especially for factors that can be easily controlled. This action occurs mainly in noise factors that are known and whose variability can be quickly noted hence embraced the use of regression methods or the formation of blocks. However, for the case of noise factors that are beyond the use of blocking strategy, the use of covariate is advised. The approach enhances the precision of a given study as it develops more covariance for each subject and correlate with the dependent variable (Covariance Designs, 2006). Therefore, through the analysis of the same, the researcher will be in a position to determine the variability causes and effectively control its occurrence.
Describe and explain three trade-offs present in experiments.
It is evident that every research has to use a random approach in assigning subjects to various conditional groups, and the selection of a particular design to use involves the use of trade-offs. The tradeoff in research experiments are caused by either the internal or external factors, which influence validity. However, one aspect that is clear is that all experiments are controlled by managing equal magnitudes of conditions except for the case of the independent variables, which require manipulation. Researchers who succeed in the above mission can obtain a high precision of the internal validity but a low validity in the external setting (Wiley, 2009). This is because, when the conditions are managed in an experiment, they fail to take account of the outside setting where conditions are different.
The first source of trade-off in research design is the number of people that participate in a particular study, considering that varying aspects influence the precision of a given study. The study assumes that complex design uses more participants, and if well managed, it can result in better accurate conclusion than simple design. Another source of trade off in a research study is the use of blocking to reduce noise, but it is faced with a lot of challenges that limit its validity. Another kind of trade-off may result from the conceptual aspect of the study. For example, to adequately understand some factors, there is a need for manipulation, a feature that might interfere with natural setting hence resulting in wrong findings.
Reference
Covariance Designs - Social Research Methods. (2006). Retrieved April 19, 2016, from http://www.socialresearchmethods.net/kb/expcov.php
Lee, J. S. (2012). Research Methods and Statistics: A Critical Thinking Approach. New York: Wadsworth Publishing ; 4th Edition .
Lee, P. H. (2016). Covariate adjustments in randomized controlled trials increased study power and reduced biasedness of effect size estimation. Journal of Clinical Epidemiology.
Roberts, M. C., & Ilardi, S. S. (2003). Design and Analysis of Experimental and Quasi-Experimental Investigations. Handbook of Research Methods in Clinical Psychology, 92.
Thyer, B. A. (2012). Quasi-experimental Research Designs. New York: Oxford University Press.
Trochim, W., Donnelly, J. P., & William M K Trochim y James P (2008). The research methods knowledge base. United States: Atomic Dog Publishing.
Wiley, R. H. (2009). Trade-offs in the design of experiments. Journal of Comparative Psychology, 123(4), 447–449.
