Week 9: Data Analysis and Presenting Findings

Data Analysis Purpose

To answer the research question effectively.

Quantitative Data Analysis

Analyzing numerical data to draw conclusions.

Descriptive Statistics

Visual arrangement of data for understanding.

Measure of Central Tendency

Summarizes dataset by identifying typical value.

Levels of Measurement

Determines applicable statistics for data analysis.

Categorical Data

Data reflecting characteristics or categories.

Nominal Data

Categorical data without inherent order or rank.

Ordinal Data

Ranked categories without exact differences.

Numerical Data

Data based on numbers or quantities.

Interval Data

Numerical scales with equal intervals, no zero.

Ratio Data

Numerical data with true zero and equal intervals.

Frequency Distribution

Organizes data by frequency of values.

Histogram

Graphical representation of frequency distribution.

Mean

Average value of a dataset.

Survey

Method for collecting categorical data.

Questionnaire

Tool for gathering data through questions.

Statistical Testing

Analyzing data to validate hypotheses.

Natural Ordering

Meaningful arrangement of categories in data.

Satisfaction Levels

Ordinal data reflecting degrees of satisfaction.

Education Level Survey

Collects data on highest education attained.

Data Visualization

Graphical representation to enhance understanding.

Mean

Average of all data points in a dataset.

Median

Middle value when data is ordered.

Mode

Most frequent value in a dataset.

Unimodal

Dataset with one mode only.

Bimodal

Dataset with two modes present.

Multimodal

Dataset with multiple modes identified.

Normal Distribution

Symmetric probability distribution around the mean.

Bell Curve

Graph shape of normal distribution.

Standard Deviation (SD)

Measure of data spread around the mean.

Skewness

Asymmetry of data distribution around the mean.

Positive Skew

Data concentrated on the right side.

Negative Skew

Data concentrated on the left side.

Range

Difference between highest and lowest scores.

Semi-Interquartile Range

Difference between 75th and 25th percentiles.

First Quartile (Q1)

Value below which 25% of data points fall.

Third Quartile (Q3)

Value below which 75% of data points fall.

Variance

Mean squared difference from the average.

Empirical Rule

Percentage of values within standard deviations.

68%

Percentage of values within one SD of mean.

95%

Percentage of values within two SDs of mean.

99.7%

Percentage of values within three SDs of mean.

Inferential Statistics

Using sample statistics to infer about population.

Probability Samples

Data obtained from randomly selected participants.

Power Analysis

Estimates necessary sample size for study.

Sample Statistics

Mean or SD calculated from sample data.

Parametric Statistics

Used for normally distributed numerical data.

Nonparametric Statistics

Used for non-normal distributions and categorical data.

Hypothesis Testing

Objective decisions based on statistical analysis.

Research Hypothesis (H1)

Predicts a relationship between variables.

Null Hypothesis (H0)

States no relationship exists between variables.

Directional Hypothesis

Specifies positive or negative relationship direction.

Non-directional Hypothesis

Indicates relationship existence without direction.

Simple Hypothesis

Predicts relationship between one independent and one dependent variable.

Complex Hypothesis

Predicts relationships involving multiple variables.

Type 1 Error

Rejecting true null hypothesis incorrectly.

Type 2 Error

Accepting false null hypothesis incorrectly.

Alpha Level

Probability of making a Type 1 error.

P-value

Probability result is statistically significant if < alpha.

Confidence Interval (CI)

Estimated range indicating certainty about sample findings.

Qualitative Data Analysis

Understanding and synthesizing studied phenomena.

Data Management

Organizing data for analysis and interpretation.

Data Reduction

Simplifying and organizing data into meaningful clusters.

Thematic Analysis

Identifying and recovering emerging themes from data.

Data Display

Organized information for drawing conclusions.

Phenomenological Analysis

Extracting significant statements from qualitative data.

Ethnographic Analysis

Interpreting cultural patterns and themes from data.

Grounded Theory Analysis

Comparative method for developing categories from data.

Presenting Findings

Sharing research results and interpretations.