Data Processing in Data Analysis

Study Notes

Refers to concentrating, recasting, and dealing with the data so that they are responsive to analysis
Involves four stages:
- Editing: checking raw data for errors, omissions, legibility, and consistency
- Coding: assigning numerals or symbols to answers to categorize responses
- Classification: dividing data into homogenous groups based on common characteristics
- Tabulation: summarizing raw data and displaying it in a compact form for further analysis

The process of systematically applying statistical and/or logical techniques to describe and illustrate, condense, and recap, and evaluate data
Involves four types of data analysis:
- Descriptive: summarizing data to understand patterns and trends
- Inferential: discovering new features in the data and suggesting new hypotheses
- Confirmatory: confirming or falsifying existing hypotheses
- Predictive: building predictive models using available data

Refers to understanding what the research findings really mean and identifying the underlying generalization
Involves understanding the differences between qualitative and quantitative data
- Qualitative data: uses texts to represent data, includes nominal and ordinal data
- Quantitative data: uses numbers to represent data, includes discrete and continuous data

The science concerned with developing and studying methods for collecting, analyzing, interpreting, and presenting empirical data
Key concepts:
- Population: the complete set of individuals that share a common characteristic
- Sample: a smaller, manageable version of a population
- Parameter: a numerical characteristic of a population
- Statistic: a number or value that describes a sample

Descriptive Statistics:
- Quantitatively describes the characteristics of data
- Includes frequency distributions, graphical representations, summary statistics, and ordinal level data
- Measures of central tendency (mean, median, mode) and dispersion (standard deviation, standard error of the mean)
Inferential Statistics:
- Draws inferences about the larger group and makes assumptions about the whole population
- Includes correlation, t-tests/ANOVA, chi-square, and logistic regression