Quantitative Versus Qualitative Research Methods Comparison
Elements of Investigation: Quantitative vs. Qualitative
1. Environment (MARCO)
- Quantitative: Can be developed in a natural environment or a closed laboratory.
- Qualitative: In contact with the object being studied, in a natural setting.
2. Design Structure
- Quantitative: Requires a fixed design established a priori (in advance).
- Qualitative: Has an emergent design; it is not fixed in advance.
3. Goals and Flexibility
- Quantitative: Techniques are pre-set, aiming for technical flexibility.
- Qualitative: The reality
Statistical Test Interpretation and SPSS Decision Rules
Statistical Significance: The Main Rule
The decision rule for hypothesis testing is based on the p-value:
- p < 0.05: Significant → Reject H0 (Null Hypothesis)
- p ≥ 0.05: Not significant → Fail to reject H0
Choosing the Appropriate Test:
- 1 group vs known value → One-Sample T-Test
- 2 groups (different people) → Independent Samples T-Test
- 2 groups (same people before/after) → Paired Samples T-Test
- 3+ groups → ANOVA (+ Tukey Post-Hoc if significant)
- Numeric ↔ Numeric relationship → Correlation
- Predict
Essential Data Science Concepts and Statistical Measures
Foundational Concepts in Data Science and Statistics
Essential Data Science Terminology
The following terms represent fundamental concepts used in data analysis and machine learning:
- Data Science: A field that uses scientific methods, algorithms, and tools to extract knowledge and insights from data.
- Datafication: The process of transforming information, activities, or objects into a data format.
- Population & Sample: The Population is the entire group being studied; the Sample is a representative
Essential Statistical Concepts for Data Analysis
Fundamentals of Business Statistics
Statistics Definition
The field concerning the collection, analysis, interpretation, and presentation of data used for the decision-making process in the business area.
Descriptive Statistics
Involves data collection methods, description, and summary data visualization. Focuses on the data as they are.
Inferential Statistics
The generation of models, inferences, and predictions associated with the phenomenon in question (predicting how the variable will behave).
Populations
Read MoreKey Statistical Concepts: Non-Parametric Tests and Time Series Analysis
Non-Parametric Methods
Non-parametric methods are statistical tests that do not assume the data follows a specific distribution, such as the normal distribution. They are often used when the assumptions of parametric tests are violated.
- Mann-Whitney U Test (Wilcoxon Rank-Sum Test): Used to compare the distributions of two independent groups or samples to determine if they have different medians. It is an alternative to the independent samples t-test.
- Wilcoxon Signed-Rank Test: Used to compare the medians
Statistical Fundamentals and Key Concepts Reference
Hypothesis Testing and P-Values
P-Value Definition
The p-value is the probability of observing a statistic as extreme (or more extreme) as the sample statistic, assuming the null hypothesis (H₀) is true.
Interpretation
- Large p-value: Evidence in favor of H₀ (Null Hypothesis).
- Small p-value: Evidence in favor of Hₐ (Alternative Hypothesis).
Types of Errors
- Type I Error (α): Rejecting H₀ when H₀ is true.
- Type II Error (β): Failing to reject H₀ when H₀ is false.
Study Design Fundamentals
- Sample: