Quantitative Data Analysis Concepts

Quantitative Data Analysis

• Learning Outcomes: Procedures for quantitative data analysis, objectives for quantitative data analysis, coding systems, tabulation of data.

Step 1: Preparation of the Data

• Data Organization: No data organization leads to unsound data analysis. Preparation involves two preparatory sub-steps.

Step 1.a: Coding System

• Conversion: Words, images, or pictures are converted into numbers.
• Analytical Procedures: This allows for analytical procedures requiring arithmetic and mathematical operations.
• Example (Gender): Number 1 = Male, Number 2 = Female
• Example (Educational Attainment): Number 1 = Elementary, Number 2 = High School, Number 3 = College, Number 4 = Masters, Number 5 = Doctorate

Step 1.b: Tabulation of Data

• Collation: For any classification and distribution of numbers based on a criterion, data is collated using a table.

Step 2: Analyzing the Data

• Step 2.1: Descriptive Statistical Techniques: Quantitative data analysis summarizes data from samples via data-gathering instruments. This reveals aspects of data items.

• Step 2.2: Frequency Distribution: Provides frequency distribution and percentage of the occurrence of items in a data set. (Essentially, counting the number of times each response appears).

• Step 2.3: Measure of Tendency (Mean, Median, Mode): Mean = average of all items/scores. Median = middle score. Mode = most frequent score.

• Step 2.4: Standard Deviation: Standard deviation measures the extent of the data's difference from the mean. This helps understand data similarity/differences between respondents.

Step 2.4a: Computing Standard Deviation

• Step a: Calculate the mean.
• Step b: Calculate the deviation (difference) between each respondent's answer and the mean. Use a + or – sign to indicate if the difference is higher or lower than the mean.
• Step c: Calculate the square of each deviation.
• Step d: Calculate the sum of the squared deviation figures.
• Step e: Divide the sum of squared deviations by the number of data items. This is the variance.
• Step f: Calculate the square root of the variance figure to get the standard deviation.

Experiment (Procedures)

• Experiment (Procedures): Scientific method of collecting data where subjects receive a treatment or condition. The evaluation determines how this condition affected the subjects.
• Examples (Types):
• Treatment - Evaluation
• Pre-Test - Treatment - Post-Test
• Pre-Test - Multiple Treatments - Post-Test
• Pre-Test - Treatment - Immediate Post-Test - 6 Months - Post-Test - 1 Year - Post-Test

Content Analysis

• Definition: A quantitative data collection technique. It involves examining several oral or written communications to find answers to research questions.