Data Analysis Quiz Overview

Univariate analysis is the study of individual variables in a data set, examining their distributions and characteristics.

Frequencies represent the number of times a specific outcome occurs in a dataset, giving you a count of how many individuals have answered with a particular response.

Relative frequency represents the proportion of individuals in a sample who have responded with a specific outcome. It's calculated by dividing the frequency by the total sample size.

Sphinx Campus uses the term "Flat analysis" to refer to "Univariate analysis." Remember that Sphinx uses French terms.

The "New Analysis" option in Sphinx Campus allows you to explore different types of analysis. It will display a table and chart for the selected variable.

The command panel in "New Analysis" allows you to customize the presentation of your data, adjust table and chart parameters, and select different types of analysis.

Missing data can significantly impact your analysis, especially when calculating proportions. Remove missing data before analyzing to ensure accuracy.

Tables and charts can effectively visualize frequencies. Both can be customized in the "Flat Table" section of Sphinx Campus.

A type of question that allows the respondent to provide their own unique and detailed answer, usually providing space for a written response. They often focus on opinions, experiences, or motivations.

A type of question that restricts the respondent's answer choices to a limited set of options. These questions are often used to gather factual information or measure preferences.

A type of question that requires respondents to select from a set of ordered categories, indicating their level of agreement, satisfaction, or frequency. These scales usually consist of a set of statements with a range of options, like 'strongly disagree', 'disagree', 'neutral', 'agree', 'strongly agree'.

A variable that represents categories or labels, where the order of the categories doesn't have any inherent meaning. Examples include gender, marital status, or favorite color.

A variable that represents categories with a meaningful order, but the differences between the categories are not necessarily equal. Examples include education level (high school, college, graduate), or agreement levels (strongly disagree, disagree, neutral, agree, strongly agree).

A variable that represents numerical data that can be counted. It's discrete because the data can only take on specific, separate values. Examples include the number of siblings in a family, or the number of times someone visits a website.

A type of question in which only one answer is possible from a set of given options. This is characteristic of closed-ended questions.

The process of collecting and organizing data into tables and charts, making them easy to understand and interpret. It is a fundamental step in data analysis, helping to visualize patterns, trends and relationships within the data.

A type of chart that is best suited for visualizing nominal variables with a limited number of outcomes (5-6). It represents portions of a whole using slices of a circle.

A graphical representation of data using bars of varying heights or lengths to show the magnitudes of different categories.

Visualizations should focus on answering specific questions, rather than presenting all data at once.

For nominal variables, the order in which categories are presented matters.

When visualizing ordinal variables, like Likert scales, maintain the natural order of the categories.

Tables and charts should be designed to improve readability and clarity.

Non-responses are generally excluded from visualizations to avoid skewing the results.

Grouping similar categories or modalities can improve the clarity of visualizations.

Charts should always have a clear title that reflects the content being presented.

Clearly indicate the outcomes (categories) of the variable being visualized.

Charts are used to simplify the reading and understanding of data.

The sample description in a survey report must always present the characteristics of the participants involved in the study.

Key demographic variables like gender, age, and education level are crucial for understanding the sample.

Other variables relevant to the research topic should also be included in the sample description, like car ownership for an automotive survey.

To analyze an individual variable within a group, click on the plus icon (+) to expand and view detailed responses.

The various types of equipment included in a car, categorizing into "Exterior", "Interior", or "Other", to group and analyze responses.

The data is presented in a way that reflects the inherent order of the response categories, preserving the progression of the values.

A presentation technique that allows you to view both a summary of aggregated data and the individual responses for each variable within a group.

A presentation technique that shows only the summarized data, hiding the individual responses for each variable.