Exam
enla
comm3023_ch12p1_2020.pptx
Chapter 12
Quantitative Data Analysis: Part 1
Process of Quantitative Data Analysis
Steps in Data Processing
Coding
Transforming data into numbers
Entering
Input data into a data file with rows for cases and columns for variables
Cleaning
Detecting and resolving errors in the data file
Verification of data entries
Wild-code and consistency checking
Data Inspection and Modification
Look for extreme values or outliers in your data.
Prevalence of missing values
Listwise deletion
Imputation
How data may be modified by recoding and by combining two or more variables
Descriptive and Inferential Statistics
Descriptive statistics
Help us organize and summarize data with the goal of making them more intelligible.
Inferential statistics
Help us to estimate population characteristics based on sample data and test hypotheses.
Calculating Descriptive Statistics
Involves producing frequency and percentage distributions
Involves calculating a variety of univariate statistics
Measures of central tendency
Measures of dispersion
Measures of Central Tendency
Mean = It is an unbiased estimate of the population mean.
Median = It is the 50th percentile value in an ordered distribution
Mode = Most frequently occurring score
Measures of Variability or Dispersion
Range = Difference between the highest and lowest values in a distribution of values
Variance = average deviation of scores from the mean
Standard deviation = standardized average deviation from the mean.
Reflects the shape of the distribution for a set of data
Describing Data Distributions
Data distributions can broadly be described as having a
Normal distribution
Skewed distribution
Mean > median or mode (positive)
Mean < median or mode (negative)
Data distributions can also be described based on height (kurtosis value):
Platykurtic
Mesokurtic
Leptokurtic
Normal Distribution and Standard Deviation
10
Concluding Thoughts on Descriptive Statistics
Type of descriptive statistics calculated for a given variable depends on the level of measurement for the variable.
Nominal or ordinal level of measurement
Interval or ratio level of measurement
Descriptive statistics can be summarized visually using a variety of formats such as:
Tables
Graphs (Histogram, pie chart)
