# identify and control extraneous variables and to mitigate their effects on the study (Statistics, 2015)

1-If I may also add to your post with regards to situational variables. According the the Association for Customer Research “Numerous studies have shown that the longer the time interval between  measures of intention and behavior, the greater the inconsistency in behavior. Empirical  evidence is presented to support the theory that this time effect on behavior  inconsistency is partially a function of unexpected situational variables. Unexpected  situational variables were also shown to affect changes in intentions over time”. (Joseph A. Cote Jr. and John K. Wong (1985). This appears to affect the outcome more than some of the other variables.

4)Statistical Control: When using Extraneous variables are present in nearly all studies and influence results. They include any variable which is not directly being studied. It is the goal of researchers to identify and control extraneous variables and to mitigate their effects on the study (Statistics, 2015). There are four ways researchers can intercede on these undesirable variables, either :

1.) Randomization: In large samples, the variable being studied or observed is disbursed randomly throughout the groups. This method does not control the extraneous variables, however it allows for equal influence of the extraneous variable throughout the group. This way each sample is effected equally.

2)Matching: This involves creating subgroups of those with the same confounding variables. These are variables which effect both the independent and dependent variables. These groups can be highly specific or generalized, for example age and sex versus health history.

3)Experimental designs: The design of the experiment itself can be influential on the damage caused by extraneous variables. For example, ineffective sampling criteria or studying too broad of a population (Dissertation the above interventions and the influence of extraneous variables does not improve, researchers can use a tool called ANOVA (Analysis of Variance). This method Among the various statistical tools and techniques, Analysis of Covariance ( ANOVA) helps in reducing the impact of the extraneous factors on the study. This is done by comparing the means of several independent groups and analyzing them for similarities and differences to extract the effects of the undesirable variables. (Laird Statistics, 2018)

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