Statistics for the Social Sciences最新文献_第2页

Formula Guide 配方指南

Statistics for the Social Sciences Pub Date : 2020-12-17 DOI: 10.1017/9781108894319.024

引用次数: 0

Linear Transformations and z-Scores 线性变换和z分数

Statistics for the Social Sciences Pub Date : 2020-12-17 DOI: 10.1017/9781108894319.006

引用次数: 0

z-Table 查表

Statistics for the Social Sciences Pub Date : 2020-12-17 DOI: 10.1017/9781108894319.017

引用次数: 2

Models of Central Tendency and Variability 集中趋势和变率模型

Statistics for the Social Sciences Pub Date : 2020-12-17 DOI: 10.1017/9781108894319.004

引用次数: 0

Levels of Data 数据级别

Statistics for the Social Sciences Pub Date : 2020-12-01 DOI: 10.1017/9781108894319.003

Russell T Warne

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Analysis of Variance 方差分析

Statistics for the Social Sciences Pub Date : 2019-07-31 DOI: 10.1142/9789811200410_0006

R. Bethea, R. Rhinehart

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Probability and the Central Limit Theorem 概率和中心极限定理

Statistics for the Social Sciences Pub Date : 2017-12-01 DOI: 10.1017/9781316442715.007

Russell T Warne

{"title":"Probability and the Central Limit Theorem","authors":"Russell T Warne","doi":"10.1017/9781316442715.007","DOIUrl":"https://doi.org/10.1017/9781316442715.007","url":null,"abstract":"Everything in the preceding chapters of this book has been about using models to describe or represent data that have been collected from a sample – which we learned is a branch of statistics called descriptive statistics. Describing data is an important task. (It would be difficult to learn about data without describing it!) But it is often of limited usefulness because almost all datasets are collected from samples – and most researchers and practitioners in the social sciences are interested in the population as a whole. After all, if a psychologist says, “I have discovered that 35 out of 50 people in my sample got better after therapy,” that isn't very interesting to anyone who isn't a friend or family member of the people in the sample. The vast majority of social science researchers are interested in how their data from their sample applies to a population. But drawing conclusions about an entire population (which may consist of millions of people) based on a sample that consists of a tiny fraction of the population is a difficult logical leap to make. Yet, that leap is not impossible. In fact, the process of how to draw conclusions about a population from sample data was worked out in the early twentieth century, and it is now common in the social sciences to draw these conclusions about populations. This chapter provides the necessary theory of this process. The rest of the chapters in this textbook will discuss the nuts and bolts of actually performing the calculations needed to learn valuable information about a population with just sample data. Learning Goals • Calculate the probability that a particular outcome will occur in a set of events. • Construct a probability distribution based on theoretical probabilities or empirical probabilities and describe why the differences between the two distribution types occur. • Explain the process of generalizing a conclusion based on sample data to the entire population. • Differentiate between a sample histogram, a sampling distribution, and a probability distribution. • Explain the Central Limit Theorem (CLT) and why it permits estimation of the population mean and standard deviation. • Estimate a population mean and standard deviation by taking multiple samples from the population. Basic Probability Statistics is based entirely on a branch of mathematics called probability , which is concerned with the likelihood of outcomes for an event.","PeriodicalId":334587,"journal":{"name":"Statistics for the Social Sciences","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129153678","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Null Hypothesis Statistical Significance Testing and z-Tests 零假设统计显著性检验和z检验

Statistics for the Social Sciences Pub Date : 2017-12-01 DOI: 10.1017/9781316442715.008

Russell T Warne

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引用次数: 0

Unpaired Two-Sample t-Tests 非配对双样本t检验

Statistics for the Social Sciences Pub Date : 2017-12-01 DOI: 10.1017/9781316442715.011

Russell T Warne

{"title":"Unpaired Two-Sample t-Tests","authors":"Russell T Warne","doi":"10.1017/9781316442715.011","DOIUrl":"https://doi.org/10.1017/9781316442715.011","url":null,"abstract":"Social science students naturally have a lot of questions. Are college students majoring in the humanities more social than students majoring in the physical sciences? Are religious societies happier than secular societies? Do children with divorced parents have more behavioral problems than children with married parents? Are inmates who attended school in prison less likely to reoffend than inmates who do not receive education? These questions compare two groups (e.g., children of divorced parents and children of married parents) and ask which group has a higher score on a variable (e.g., number of behavioral problems). These kind of questions are extremely common in the social sciences, so it should not surprise you that there is a statistical method developed to answer them. That method is the unpaired two-sample t-test, which is a comparison of scores from two different unrelated groups. The topic of this chapter is the unpaired two-sample t-test , one of the most common statistical methods in the social sciences. Learning Goals • Explain when an unpaired two-sample t -test is an appropriate NHST procedure. • Conduct an unpaired two-sample t -test. • Calculate an effect size (i.e., Cohen's d) for an unpaired two-sample t -test. • Show how the unpaired two-sample t -test is a member of the general linear model (GLM). • Correctly find and interpret a p -value. Making Group Comparisons Answering the questions at the beginning of the chapter requires comparing two scores. When these groups are unrelated to one another, an unpaired-samples t-test is an appropriate method of analyzing data. You may remember in Chapter 9 we paired scores, the two sets of scores formed pairs across the datasets (e.g., pre-test and post-test data, or scores about a pair of siblings). This allowed us to simplify the two sets of scores into one group of difference scores and to conduct a one-sample t -test with the difference scores. However, in the social sciences our scores are often unrelated to one another. For example, a sociology student interested in the happiness levels in different societies would collect data on happiness levels from people in both religious and non-religious societies. After these data are collected, she would have two sets of scores – but there would be no logical reason to pair individuals from one group with individuals from another group because the people in the two groups had nothing to do with each other.","PeriodicalId":334587,"journal":{"name":"Statistics for the Social Sciences","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122013307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chi-Squared Test 卡方测试

Statistics for the Social Sciences Pub Date : 2014-07-31 DOI: 10.1017/9781108894319.015

A. Culyer

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