91Ó°ÊÓ

The report "Comparative Study of Two Computer Mouse Designs" (Cornell Human Factors Laboratory Technical Report RP7992) included the following description of the subjects used in an experiment: Twenty-four Cornell University students and staff (12 males and 12 females) volunteered to participate in the study. Three groups of 4 men and 4 women were selected by their stature to represent the 5 th percentile (female \(152.1 \pm 0.3 \mathrm{~cm}\), male \(164.1 \pm 0.4 \mathrm{~cm})\) 50th percentile (female \(162.4 \pm 0.1 \mathrm{~cm}\), male \(174.1 \pm\) \(0.7 \mathrm{~cm}\) ), and 95 th percentile (female \(171.9 \pm 0.2 \mathrm{~cm}\), male \(185.7 \pm 0.6 \mathrm{~cm}\) ranges \(\ldots\) All subjects reported using their right hand to operate a computer mouse. This experimental design incorporated direct control and blocking. a. Are the potential effects of the extraneous variable stature (height) addressed by blocking or direct control? b. Whether the right or left hand is used to operate the mouse was considered to be an extraneous variable. Are the potential effects of this variable addressed by blocking or direct control?

Step by step solution

01

Understand Blocking Versus Direct Control

First, it's crucial to understand the difference between blocking and direct control. Blocking is when you compile experimental units into groups known as blocks because they're akin relating to an extraneous variable that could affect the experiment. In contrast, direct control adjusts the level of the extraneous variable to counter its potential effect on the experiment.

02

Address the Extraneous Variable Stature

The stature or height of the individuals is organized into different percentile ranges. This is a situation of blocking since the experiment block participants into groups according to their stature. Hence, any potential effects from the height variable have been addressed by blocking.

03

Address the Extraneous Variable of Mouse Operation

The statement specifies that all subjects reported using their right hand to operate the computer mouse. This is a situation of direct control because irrespective of the subjects' natural hand preference, all of them were regulated to use only the right hand. Thus, any potential effects from the variable of which hand operates the mouse have been addressed by direct control.

Unlock Step-by-Step Solutions & Ace Your Exams!

• Full Textbook Solutions

  Get detailed explanations and key concepts

• Unlimited Al creation

  Al flashcards, explanations, exams and more...

• Ads-free access

  To over 500 millions flashcards

• Money-back guarantee

  We refund you if you fail your exam.

Over 30 million students worldwide already upgrade their learning with 91Ó°ÊÓ!

Key Concepts

These are the key concepts you need to understand to accurately answer the question.

Blocking in Experiments

The concept of blocking in experiments is like grouping participants in a school photo by height—those of similar stature stand together. It is applied within experimental design to control for the influence of an extraneous variable by grouping subjects into blocks based on that specific variable. For instance, in a study evaluating computer mouse designs, researchers might separate subjects by stature—into short, average, and tall blocks. This ensures that any effect stature might have on the ease of using a mouse is equally distributed across these blocks.

When subjects are divided in such a way—like the 5th, 50th, and 95th percentile ranges mentioned in the exercise—they can be compared within those groups rather than against the entire diverse population. This approach limits the variation within each block, making it easier to detect differences caused by the experimental treatment rather than by the extraneous variable of height.

Direct Control in Experiments

Direct control is like setting a universal dress code for that same school photo—everyone wears blue to create consistency. Applied to experiments, direct control involves setting the level of an extraneous variable to the same value for all experimental units. In the context of our computer mouse study, only the right hand was used by all participants, regardless of their usual preference. This enforces uniformity and mitigates the potential influence of hand dominance on the experiment's outcome.

By enforcing this consistency, researchers can focus on the actual variables of interest (such as the design of the computer mouse) without worrying about the noise introduced by the extraneous variable of hand preference. Direct control simplifies the analysis because it removes a layer of variability from the experiment.

Extraneous Variables

Extraneous variables are those uninvited guests at a party—they're not the focus, but they can certainly influence the mood. In experimental research, these are variables other than the independent variable that could cause changes in the dependent variable, potentially skewing results. For example, in an experiment comparing computer mouse designs, variables like stature and hand preference could unintentionally affect outcomes.

To ensure that findings are attributable to the manipulated variables rather than these uninvited guests, researchers employ strategies such as blocking and direct control. In the case of stature, grouping by height ranges (blocking) would cater to ensuring that its influence does not confound the results. Identifying and addressing these extraneous variables is crucial for the integrity of an experiment.

Percentile Ranges

Percentile ranges are like the bins in which we sort mail according to postal codes—they help organize data points relative to one another. They represent the position of a value within a distribution, showing what percentage of scores fall below it. In the context of the computer mouse experiment, stature is categorized using percentile ranges (5th, 50th, and 95th) to represent typical heights for different groups.

By using these benchmarks, researchers can ensure a representation of varying body sizes without the need for separate trials for each individual size. This approach helps streamline the analysis and interpretation of the data by clustering similar subjects, which in turn assists in minimizing the variability introduced by stature in the experiment's outcomes.