Random Factor Analysis  Explained
What is Random Factor Analysis?
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Table of Contents
What is Random Factor Analysis?How does Random Factor Analysis Work? How it Works [Example]Random and Fixed Factor AnalysisFixed Effect FactorRandom Effect FactorAcademic Research on Random Factor AnalysisWhat is Random Factor Analysis?
Random factor analysis refers to a statistical technique that is used to identify the origin of the randomly collected data. Random factor analysis is applied when you want to determine whether the underlying trend is the cause of the outlying data or the event occurs randomly. Generally, it tries to explain the seemingly random data. Note that in order for random factor analysis to interpret data that is accurate, multiple variables are applied.
Key Points:
 Random factor analysis refers to a statistical technique that is used to identify the origin of the randomly collected data.
 Random factor analysis is usually used to assist firms to actually focus their plans on real problems.
 The analysis of variance can either be random or fixed effects. The appropriateness of each type is dependent on the problems context, means of gathering the data, as well as the question of interest.
 If a factor is incorrectly specified, then the analysis results will probably be incorrect too.
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How does Random Factor Analysis Work?
Generally, random factor analysis is usually used to assist firms to actually focus their plans on real problems. For instance, if an underlying event causes random data, then it requires that the trend be addressed so that appropriate remedies are arrived at.
How it Works [Example]
Lets assume that there is a random event like an eruption of a volcano. The breathing masks sales will definitely increase. In this case, one can just refer to the data of the sales over the years. Note that this data can be attributed to the volcano eruption random events.
Random and Fixed Factor Analysis
There are several types of factors when it comes to the analysis of variance, and this includes other methodologies. However, there are only two types of analysis of variance; random effects factor or fixed effects factor. The appropriateness of each type is dependent on the problems context, means of gathering the data, as well as the question of interest.
Fixed Effect Factor
This is where an investigator only collects data of all factor levels he or she is interested in. This means he or she will engage in selecting specific data that he or she feels is vital, leaving out the rest. In other words, a fixed factor is where the levels of the factor are controlled by the investigator.
Random Effect Factor
Random fixed factor involves the investigator doing random sampling of the factor levels. Meaning that the collection of factor is conducted randomly from the target population. Note that a random factor consists of many possible levels, where all of them are of interest to the investigator. Nonetheless, since the investigator cannot use all the factor levels, he or she is allowed to randomly select some of the samples. The random selection here means that there are no specific criteria for selection, neither are there factor levels in particular, which the investigator is targeting.
Example Lets assume that an operator factor has eight levels. If an investigator does select all of the eight operators intentionally, where he or she wants results to be from these specific operators, then the factor becomes fixed. On the other hand, if the eight operators are randomly sampled from a number of operators that is large, and the investigator wants the results to be from all of these operators, this then becomes a random factor. In general, an investigator will require different kinds of analyses when working on the two types of factors mentioned above. However, you need to know that if you happen to specify an incorrect factor level, then the analysis results will probably be incorrect too. This means that you need to be careful when selecting specific factor levels if you have to get the correct analysis.
Academic Research on Random Factor Analysis
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