factor

(noun)

The explanatory, or independent, variable in an experiment.

Related Terms

Examples of factor in the following topics:

  • 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.

  • Randomized Design: Single-Factor

    • Completely randomized designs study the effects of one primary factor without the need to take other nuisance variables into account.
    • For completely randomized designs, the levels of the primary factor are randomly assigned to the experimental units.
    • For example, if there are 3 levels of the primary factor with each level to be run 2 times, then there are $6!
    • 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)

  • Randomized Block Design

    • However, there are also several other nuisance factors.
    • 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.
    • An example of a blocked design, where the blocking factor is 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.

  • Factors Affecting Power

    • Several factors affect the power of a statistical test.
    • Some of the factors are under the control of the experimenter, whereas others are not.
    • The following example will be used to illustrate the various factors.
    • In this section, we consider factors that affect the probability that the researcher will correctly reject the false null hypothesis that the population mean is 75.
    • In other words, factors that affect power.

  • Confounding

    • In risk assessments, factors such as age, gender, and educational levels often have impact on health status and so should be controlled.
    • Beyond these factors, researchers may not consider or have access to data on other causal factors.
    • Confounding by indication occurs when prognostic factors cause bias, such as biased estimates of treatment effects in medical trials.
    • Controlling for known prognostic factors may reduce this problem, but it is always possible that a forgotten or unknown factor was not included or that factors interact complexly.
    • A reduction in the potential for the occurrence and effect of confounding factors can be obtained by increasing the types and numbers of comparisons performed in an analysis.