Summary: Statistical Methods For The Social Sciences  9780471094531  Alan Agresti, et al
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Read the summary and the most important questions on Statistical Methods for the Social Sciences  9780471094531  Alan Agresti; Barbara F. Agresti

1 Introduction

1.1 Introduction to Statistical Methodology
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The field of statistical science includes methods for three things. Name and explain these.
 Design: Planning how to gather data for a research study.
 Description: Summarizing data. We use descriptive statistics to reduce the data to a simpler and more understandable form.
 Inference: Making predictions based on the data.
 Design: Planning how to gather data for a research study.

1.2 Descriptive Statistics and Inferential Statistics

What is a parameter?
It is a, usually unknown, value which represents a population characteristic (E.g.: the number of women studying psychology). 
1.3 The Role of Computers and Software in Statistics

What is a data file and how is it organized?
Statistical software analyzes data organized in the spreadsheet form: Any row contains the observations for a particular subject in the sample.
 Any column contains the observations for a particular characteristic.

2 Sampling and Measurement

What are validity and reliability?
 Validity is about the accuracy of a measure. Meaning that a measure measures what it is supposed to.
 Reliability is about the consistency of a measure. Meaning that a measure should roughly have the same outcome every time. 
2.1 Variables and Their Measurement

What is a variable?
A variable is a characteristic that can vary in value among subjects in a sample or population. 
What is a quantitive variable and on what scale are they measured?
Quantitative variables are variables which have a numerical value. It is possible to find the average for quantitative variables. They are measured in an interval scale. Examples are annual income, number of siblings or age. 
What is a categorical variable and on what scales are they measured?
Categorical variables are variables which consist of categories. They are also called qualitative variables. It is not possible to find the average for categorical variables. They can be measured in two scales of measurement: Nominal scale  the different categories do not have an order of value, they are equal (e.g.: ways of transport).
 Ordinal scale  the different categories have a natural ordering of values (e.g.: upper, middle and lower social class).

What are discrete and continuous variables?
 Discrete variables are countable to some point. that can not be subdivided. All categorical variables are discrete, however, some quantitive variables are discrete as well. (E.g.: number of siblings).
 Continuous variables literally go on forever to count. Only quantitative variables can be continuous. In practice, we round them when measuring. (E.g.: age or height).
 Discrete variables are countable to some point. that can not be subdivided. All categorical variables are discrete, however, some quantitive variables are discrete as well. (E.g.: number of siblings).

What is a simple random sample and how do we select it?
It is a sampling method in which each subject has an equal chance of being in the sample. To select it, a list of all subjects in the population is needed, a sampling frame. From this list, random numbers are generated. 
2.2 Randomization
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What is a sample survey?
It is a sample selected of people from a population which are interviewed. It observes the outcomes without any experimental manipulation. A way of sampling for a telephone interview is random digit dialling.
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Topics related to Summary: Statistical Methods For The Social Sciences

Sampling and Measurement  Sampling Variability and Potential Bias

Sampling and Measurement  Other Probability Sampling Methods

Descriptive Statistics  Describing Data with Tables and Graphs

Descriptive Statistics  Describing Variability of the Data

Descriptive Statistics  Measure of Position

Descriptive Statistics  Bivariate Descriptive Statistics

Probability Distributions  The Normal Probability Distribution

Statistical Inference: Estimation  Point and Interval Estimation