Introduction to Social Research Statistics

A book about statistical methods, aimed at students with varying math backgrounds, focused on social research applications.

Students with diverse mathematical backgrounds in social research.

A statistical method for combining results of multiple studies.

A measure of relative variability.

Statistical test for mean comparison and effect size calculation in a single dataset.

Non-parametric tests for comparing groups.

Statistical method for predicting outcomes with a binary response.

Text sections summarizing the overall research process, highlighting the context.

Someone who studies human society and behavior, often using scientific methods.

Predictions or assumptions about the future based on knowledge and experience.

To provide evidence or information that agrees with an initial belief.

The results or effects that occur when our predictions are right or wrong.

Situations where we make assumptions about future outcomes, like investing, voting, or dating.

The possibility that our predictions might not be accurate and we might face unexpected results.

The different outcomes or results possible when we make predictions or choices.

The reality that our daily assumptions about the future are not always confirmed by experience.

The extent to which female and male characters are portrayed in media, including their roles and characteristics.

Representing a group of people in a simplified and often inaccurate way, based on their gender, race, or other characteristics.

A research method used for studying media, where researchers systematically analyze the content of the media to identify patterns and themes.

A research method where researchers actively participate in the lives of the people they are studying, observing and interacting with them in their natural setting.

Educational programs designed for students who are not able to attend traditional schools, often due to behavioral or academic challenges.

Young people who are more likely to experience negative outcomes, such as dropping out of school, drug use, or involvement in violence.

The impact that media such as television, movies, and video games have on the behavior, attitudes, and values of children.

The typical ways that male and female characters are portrayed in media, often reflecting traditional societal expectations.

A variable that can take on any value within a given range, like height, weight, or age. It can be measured with great precision.

A variable that can only take on specific, separate values, usually whole numbers, like the number of siblings or the number of cars owned.

Different ways to measure variables, categorized by the type of information they provide. The four main scales are nominal, ordinal, interval, and ratio.

The simplest scale that classifies data into categories without any order or rank. Examples include gender, ethnicity, or marital status.

A scale that ranks data in a meaningful order, but the differences between ranks are not uniform. Examples include rankings like finishing positions in a race or levels of satisfaction.

The inevitable difference between a sample's characteristics and those of the whole population it represents.

The extent to which findings from a sample can be applied to the larger population.

Larger samples are more reliable for generalizing to the population than smaller samples.

Using samples to test a prediction about a population.

The level of certainty that a sample accurately reflects the population.

A demonstration of sampling error using student grades to show that sample averages rarely match the population average.

Statistics help us make decisions with more confidence by using samples to generalize to larger populations.

Understanding someone's true nature requires more than just surface impressions.

A difference between groups or samples that is unlikely to have occurred by chance alone. It implies that the observed difference is probably a real effect.

Statistics help us make informed decisions about populations based on sample data. They allow us to determine if observed differences are truly significant or simply due to random chance.

It's a mathematical procedure used to analyze data and determine if observed differences between groups or samples are statistically significant. It helps us decide whether the results are likely due to chance or a real effect.

A statistical test used to analyze categorical data. It helps determine if there's a significant relationship between two variables. For example, is there a relationship between gender and marijuana use?

When analyzing data, we need to decide whether the observed difference between groups or samples is a real effect or just due to random chance variation.

This means that if we reject the null hypothesis (no difference between populations), there is a 5% chance that we might be wrong. This is often expressed as a p-value of 0.05.