nuisance factors

(noun)

Variables that may affect the measured results, but are not of primary interest.

Related Terms

Examples of nuisance factors in the following topics:

  • Randomized Block Design

    • However, there are also several other nuisance factors.
    • Nuisance factors are those that may affect the measured result, but are not of primary interest.
    • All experiments have nuisance factors.
    • When we can control nuisance factors, an important technique known as blocking can be used to reduce or eliminate the contribution to experimental error contributed by nuisance factors.
    • The basic concept is to create homogeneous blocks in which the nuisance factors are held constant and the factor of interest is allowed to vary.

  • Randomized Design: Single-Factor

    • Completely randomized designs study the effects of one primary factor without the need to take other nuisance variables into account.
    • In the design of experiments, completely randomized designs are for studying the effects of one primary factor without the need to take into account other nuisance variables.
    • All completely randomized designs with one primary factor are defined by three numbers: $k$ (the number of factors, which is always 1 for these designs), $L$ (the number of levels), and $n$ (the number of replications).
    • $L$: 4 levels of that single factor (called 1, 2, 3, and 4)
    • Discover how randomized experimental design allows researchers to study the effects of a single factor without taking into account other nuisance variables.

  • Experimental Design

    • Factorial experiments: Use of factorial experiments instead of the one-factor-at-a-time method.
    • These are efficient at evaluating the effects and possible interactions of several factors (independent variables).
    • Analysis of experiment design is built on the foundation of the analysis of variance, a collection of models that partition the observed variance into components, according to what factors the experiment must estimate or test.
    • To control for nuisance variables, researchers institute control checks as additional measures.

  • When Does the Z-Test Apply?

    • Nuisance parameters should be known, or estimated with high accuracy (an example of a nuisance parameter would be the standard deviation in a one-sample location test).
    • In practice, due to Slutsky's theorem, "plugging in" consistent estimates of nuisance parameters can be justified.

  • Analysis of Variance Designs

    • Be able to identify the factors and levels of each factor from a description of an experiment
    • Determine whether a factor is a between-subjects or a within-subjects factor
    • Therefore, "Type of Smile" is the factor in this experiment.
    • If an experiment has two factors, then the ANOVA is called a two-way ANOVA.
    • The factors would be age and gender.

  • Between- and Within-Subjects Factors

    • When different subjects are used for the levels of a factor, the factor is called a between-subjects factoror a between-subjects variable.
    • When the same subjects are used for the levels of a factor, the factor is called a within-subjects factor or a within-subjects variable.
    • It is common for designs to have more than one factor.
    • This design has two factors: age and gender.
    • Complex designs frequently have more than two factors and may have combinations of between- and within-subjects factors.

  • Factorial Experiments: Two Factors

    • Such an experiment allows the investigator to study the effect of each factor on the response variable, as well as the effects of interactions between factors on the response variable.
    • For the vast majority of factorial experiments, each factor has only two levels.
    • The strings have as many symbols as factors, and their values dictate the level of each factor: conventionally, $-$ for the first (or low) level, and $+$ for the second (or high) level .
    • The factorial points can also be abbreviated by (1), $a$, $b$, and $ab$, where the presence of a letter indicates that the specified factor is at its high (or second) level and the absence of a letter indicates that the specified factor is at its low (or first) level (for example, $a$ indicates that factor $A$ is on its high setting, while all other factors are at their low (or first) setting). (1) is used to indicate that all factors are at their lowest (or first) values.
    • It is relatively easy to estimate the main effect for a factor.

  • Statistical Literacy

    • Assume the data were analyzed as a two-factor design with pre-post testing as one factor and the three drugs as the second factor.
    • It would be the interaction of the two factors since the question is whether the effect of one factor (pre-post) differs as a function of the level of a second factor (drug).

  • Two-Way ANOVA

    • Factorial experiments are more efficient than a series of single factor experiments and the efficiency grows as the number of factors increases.
    • We define a factorial design as having fully replicated measures on two or more crossed factors.
    • Each level of one factor is tested in combination with each level of the other(s), so the design is orthogonal.
    • The use of ANOVA to study the effects of multiple factors has a complication.
    • Testing one factor at a time hides interactions, but produces apparently inconsistent experimental results.

  • ANOVA Design

    • The more complex experiments share many of the complexities of multiple factors.
    • ANOVA generalizes to the study of the effects of multiple factors.
    • Factorial experiments are more efficient than a series of single factor experiments, and the efficiency grows as the number of factors increases.
    • The use of ANOVA to study the effects of multiple factors has a complication.
    • This occurs when the various factor levels are sampled from a larger population.