Variables 1

Statistics provides mathematical techniques to organize and analyze data, helping social scientists answer questions and test theories, especially when research involves numerical information.

A systematic process of gathering information to answer questions, examine ideas, or test theories using various methods, like surveys and experiments.

Numerical information gathered for research.

The mathematical methods used to manipulate and analyze data gathered to answer questions and test theories.

Research in the social sciences uses systematic methods to gather data, analyze observations, validate hypotheses, and refine theories based on empirical evidence.

Data, when carefully collected and analyzed, provides strong and objective evidence for building theories and enhancing understanding.

The presence of data doesn't automatically guarantee quality research. Data must be meticulously collected and analyzed.

Data is crucial in social sciences; it's the foundation for theories and understanding social phenomena. Without a solid base of data, the social sciences wouldn't be considered a 'science'.

An explanation for how things relate to each other, trying to understand social problems like prejudice, poverty, or crime.

Information collected to study social problems, often in the form of numbers.

Statistics helps turn data into usable information, organizing, analyzing, and interpreting it.

Without statistics, data is useless. It provides methods to organize, evaluate, and understand the data.

A model showing how theory, research, and statistics work together to advance knowledge in the social sciences.

Theory helps guide research questions, and research findings can modify existing theories.

Data on its own doesn't tell a story. Statistics are needed to organize and analyze it to understand the patterns within the data.

Statistics helps social scientists test ideas, find patterns in data, and come closer to understanding truth in the social world.

Statistics provide essential tools for social scientists to analyze data, test theories, and draw conclusions about social phenomena. It enables them to make informed decisions and advance their understanding.

This book emphasizes the practical application of statistical methods in social science research. It aims to help readers understand and utilize statistics as tools to answer real-world questions.

The book includes basic mathematical concepts to explain the principles behind statistical analysis but prioritizes the application of techniques in social science research.

Learning statistics is essential for social science students because it enables them to understand and critically evaluate research papers, conduct their own research, and succeed in advanced coursework.

Statistical literacy refers to the ability to understand and interpret statistical information, which is crucial for reading and critically evaluating research in social sciences.

Many careers in social sciences and related fields require some level of statistical understanding for reading research, analyzing data, and making informed decisions.

This textbook aims to provide an introduction to statistics as they are used in social science research, equipping readers with the knowledge to effectively understand and use statistical tools.

This book is not intended for aspiring statisticians who need a more thorough and technical understanding of statistical theory and methods.

Using information from a sample (a small group) to make conclusions about a larger population.

The complete group of individuals or things that you are interested in studying.

A smaller group selected from a population, used to represent the whole.

Samples are much cheaper and faster to collect data from compared to studying the entire population.

Inferential statistics aim to make accurate generalizations about a population based on data from a sample.

Using sample data to estimate population characteristics, like the average height of students in a school.

Using sample data to test claims or theories about a population, like whether a new drug improves recovery time.

A real-world example of inferential statistics, where a sample is used to predict the opinions of a large population.

The mathematical nature of a variable, determined by its response categories (possible values). It dictates which statistical analyses are appropriate.

The possible attributes, qualities, or characteristics a variable can take on. They define the variable's level of measurement.

How a variable's response categories are structured mathematically. Different levels of measurement indicate different levels of mathematical complexity.

Variables with numerical scores where you can perform arithmetic operations.

Variables with 'scores' that are actually just labels, not numbers. They only allow limited statistical analysis.

Applying statistical methods that are meant for numerical variables to non-numerical variables can lead to meaningless results.

A common statistic used to summarize numerical variables, representing the typical value.

Variables with non-numerical 'scores' that are labels, not numbers. They can't be used to calculate statistics like the mean.

Data is categorized into named groups without any order or numerical value.

Data is arranged in a specific order, but the differences between categories are not equal.

Data has equal intervals between values, but no true zero point.

Data has equal intervals and a true zero point, allowing for meaningful ratios between values.

Data can only take on specific, separate values, often whole numbers.

Data can take on any value within a range, including decimals.

Descriptive statistics summarize data (e.g., average, frequency), while inferential statistics draw conclusions about populations based on samples.