Social Statistics

Assignment 
01
Tables  





Welcome to Social Statistics lecture, here below lecture outline that is going to be conducted at 23^rd of Sep. 2013 at K3 209, Faculty of Social Science.

Data and Data Collection

What is Data  and Why Data ?

How they can be collected ?

Data vs. Information

Attributes, Variables, and Cases

*We observe characteristics of the entities we are studying. For example, we observe that a person is female and we refer to that characteristic as an attribute of the person.

*A logical collection of attributes is called a variable; in this instance, the variable would be gender and would be composed of the attributes female and male

*It is convenient to refer to the variables we are especially interested in as response variables.

*The data that we want to analyze can be displayed in a rectangular or matrix form, often called a data sheet

*To simplify matters, the individual persons, things, or events that we get information about are referred to generically as cases

*Traditionally, the rows in a data sheet correspond to the cases and the columns correspond to the variables of interest.

*The numbers or words in the cells then correspond to the attributes of the cases

*The choice of a data analysis method is affected by several considerations, especially the level of measurement for the variables to be studied;

*The unit of analysis; the shape of the distribution of a variable, including the presence of outliers (extreme values);

These are the relevant links for the lecture

http://sociology-data.sju.edu/02039/02039-Codebook.pdf

http://sociology-data.sju.edu/

http://library.uoregon.edu/govdocs/datasets-sociology.html

http://www.ndacan.cornell.edu/datasets/pdfs_user_guides/051user.pdf

https://www.google.lk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=25&cad=rja&ved=0CFcQFjAEOBQ&url=http%3A%2F%2Ffaculty.ccc.edu%2Faberger%2F201.04%2520Sociological%2520Research%2520Methods.ppt&ei=d849UruKPMqUrAfuyYHYCQ&usg=AFQjCNF8uMZ-jf4OAN3bwJ1zfx_i1zoerg&sig2=nnaAkHvWB8cqTlRTZufeew&bvm=bv.52434380,d.bmk

Social Statistics lecture, here below lecture outline that is going to be conducted at 30^th of Sep. 2013 at K3 209, Faculty of Social Science. (8.00 am - 10.00 am)

Basic statistics

Why We Need Statistics

Statistics is an objective way of interpreting a collection of observations

Types of statistics

1.Descriptive

Central tendency

Variability

2.Correlational

3.Inferential

Differences within or between groups

Basic Taxonomy of Statistics Field

•Descriptive statistics

–Allow a researcher to describe or summarize their data

–Example:  descriptive statistics for a study using human subjects might include

•the sample size,

•mean age of participants,

•Percentage of males and females,

•range of scores on a study measure, ..

–Presented usually at the beginning of the “Results” chapter in your thesis

Basic Taxonomy (Inferential Statistics)

 •Inferential statistics

•Most important part of a dissertation’s statistical analysis; use allows to make statistical inferences about the data;

•Most of your dissertation results chapter will focus on presenting the results of inferential statistics used for your data

•Estimation statistics – used to make estimates about population values based on sample data

–Confidence intervals – allow us to establish a range that has a known probability of capturing the true population value.

•Example: different confidence interval formulas, e.g., for estimating the population mean, the percentage of a characteristics in the population;

–Parameter estimation statistics  – allow us to make inferences about how well a particular model might describe relationship between variables in a population.

•Example: linear regression model, a logistic regression model, the Cox regression model

Basic Taxonomy (Inferential Statistics)

•Hypothesis testing statistics – allow us to use statistical data analysis to make statistical inferences about whether or not the data we gathered support a particular hypothesis.

•Example: there are many hypothesis testing procedures : z-test, T-Test, Chi-Square, ANOVA

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