12 Questions
Which of the following is NOT a step in the data processing phase?
Hypothesis generation
Which type of data analysis focuses on discovering new features in the data and suggesting new hypotheses?
Inferential analysis
What is the main purpose of the coding step in data processing?
To assign numerical values or symbols to the data
Which type of data analysis is focused on confirming or falsifying existing hypotheses?
Confirmatory analysis
What is the main purpose of the tabulation step in data processing?
To summarize the raw data in a compact form
Which of the following is NOT a step in the data analysis phase?
Coding
What is the main purpose of inferential statistics?
To draw conclusions about the population based on sample analysis
Which of the following is a type of qualitative data?
Nominal data
What is the difference between a parameter and a statistic?
Parameters describe the whole population, while statistics describe a sample
Which of the following is an example of a graphical representation in descriptive statistics?
Frequency distribution
What is the main purpose of data processing in the data analysis process?
To clean and organize the data for analysis
Which of the following is a characteristic of descriptive statistics?
It is used to organize, analyze, and present data in a meaningful way
Study Notes
Data Processing
- 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
Data 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
Data Interpretation
- 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
Statistics
- 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 and Inferential Statistics
- 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
Learn about the key processes involved in data processing for analysis, including editing, coding, and classification. Understand how raw data is transformed to make it suitable for analysis.
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