Question

For the 2015 Intel Science Fair, two brothers in high school recruited 47 of their classmates to take part in a two-stage study. Participants had to read two different passages and then answer questions on them, and each person's score was recorded for each of the two tests. There were no distractions for one of the passages, but participants received text messages while they read the other passage. Participants scored significantly worse when distracted by incoming texts. Participants were also asked if they thought they were good at multitasking (yes or no) but "even students who were confident of their abilities did just as poorly on the test while texting." 15 (a) What are the cases? (b) What are the variables? Is each variable categorical or quantitative? (c) If we create a dataset of the information with cases as rows and variables as columns, how many rows and how many columns would the dataset have?

Short answer

(a) The cases in this study are the 47 participants recruited for the experiment.\n(b) The variables are the conditions under which the participants read the passages, their belief in their multitasking ability and the score on each test. The first two are categorical variables and the last one is a quantitative variable.\n(c) The dataset would have 47 rows (for each participant) and 3 columns (for each variable).

Step-by-step solution

Identifying the cases

The cases in this study are the participants that are part of the experiment. In this case, there were 47 participants or classmates recruited by the two brothers.

Identifying the variables and their types

The variables here are the conditions under which the participants read the passages and their own belief in their multitasking ability.\n\n1. Conditions under which they read the passages (with or without distractions) - This is a categorical variable (two categories: with distraction / without distraction). \n2. Their belief in their multitasking ability ('yes' if they believe or 'no' if they don't believe) - This is also a categorical variable (two categories: yes / no).\n3. Their recorded score for each test - This is a quantitative variable as it measures a certain quantity (their scores).

Creation of a dataset

When the dataset is created for this study, each row represents a unique case (participant), and each column represents a variable. Given there were 47 participants (cases) and 3 variables (conditions under which they read the passages, their belief in their multitasking ability, and the score on each test), the dataset would have 47 rows and 3 columns.

Key concepts

Categorical vs Quantitative Variables

Understanding the difference between categorical and quantitative variables is essential in any statistical study, including high school science fair projects. A categorical variable represents characteristics that can be placed into different groups or categories but do not have a natural order. Examples include eye color, type of phone, or in the context of the Intel Science Fair study, whether a participant was distracted by texts or not, and the participant's confidence in multitasking ability.

On the other hand, a quantitative variable is one that is numerical and can be measured. This means it can take on numeric values with meaningful magnitudes and it is possible to perform arithmetic operations with these numbers. In the science fair study, this would be the scores that each participant received on the tests. These scores are clearly quantitative as they measure the quantity of correct answers.

Understanding this distinction is critical when collecting and analyzing data, as the type of variable will determine the types of statistical analyses that can be conducted. For instance, statistical tests like t-tests and ANOVAs require quantitative dependent variables, while chi-square tests are more suited for categorical data.

Data Collection in Experiments

The basis of any scientific experiment is the data collected, and how this data is gathered can significantly impact the quality of the research. In the case of the Intel Science Fair study, the data collection was structured and systematic, with each participant reading passages under two different conditions. One crucial aspect of data collection is consistency, ensuring that the same types of information are collected in the same way from all participants.

For the study in question, the brothers could have ensured consistency by standardizing the texts sent to each participant to control for variability in distraction level. The timing, frequency, and content of the messages could be controlled to impose a consistent level of distraction.

Another key aspect is accurate record-keeping. This involves noting down the scores in a precise and orderly manner. In creating a data set with cases as rows and variables as columns, it becomes easier to identify patterns and make comparisons. The accuracy and organization of this data set are paramount as it serves as the foundation for any subsequent analysis.

Effect of Multitasking on Performance

Multitasking often makes us think that we are achieving efficiency by doing multiple things at once. However, evidence suggests that for most people, multitasking can significantly impair performance. The Intel Science Fair study provides a clear example of this phenomenon, as students showed worse performance on reading comprehension tests when they were distracted by text messages versus when they were not.

The study also highlights the misconception among students who believed they were adept at multitasking, only to find their performance suffered just as much as those who did not consider themselves good multitaskers. This demonstrates how multitasking, specifically involving media like texts, can negatively impact activities that require focus and comprehension.

Educators and students should consider these findings when structuring learning environments and study sessions. Minimizing distractions can be key to maximizing performance, especially in settings where deep understanding and retention of information are the goals.