Survey Research and Causation: Exploring Brady’s Approaches
Survey Research and Causation
Alignment with Brady’s Approaches
Survey research aligns best with the Quantitative/Probability approach to causation described by Brady. This approach emphasizes probability sampling, where random samples are drawn from a population to make inferences about the larger group. The goal is to establish external validity, ensuring the findings can be generalized beyond the sample.
Key characteristics of the Quantitative/Probability approach include:
- Deductive reasoning
- Emphasis
Statistics Terminology and Concepts
Basic Terminology
Data Types
Sample: A portion of the population units sampled to gather information.
Target Population: The entire group of individuals or objects that the researcher is interested in studying.
Continuous Data: Data that can take on any possible value within a range.
Discrete Data: Data that have built-in restrictions on decimal places, such as whole numbers.
Categorical Measurements
Measurements where a unit is placed into a category based on an observed attribute or quality.
Nominal Labels:
Read MoreQueueing Theory and Operations Management Problems
Section A: Multiple Choice
Instructions:
Please write your answer in the underlined space in front of each problem.
Question 1:
Which of the following will increase the waiting time in a call center in which the incoming call gets assigned to the first available server?
- A) Add more servers
- B) Increase the service time coefficient of variation
- C) Increase the average service time
- D) Decrease the average inter-arrival time
- E) Both (a) and (c)
- F) Both (b) and (c)
- G) All (b), (c), and (d)
Answer: (G). Recall the
Read MoreStatistics Cheat Sheet: Formulas, Definitions & Excel Functions
Statistics Cheat Sheet
Key Concepts and Definitions
Data Types
- Categorical Data: Data that can be grouped into categories.
- Numeric: Represented by numbers (e.g., zip codes).
- Non-numeric: Represented by words (e.g., colors).
- Quantitative Data: Numerical data that represents measurements or counts.
- Interval: Differences between values are meaningful (e.g., temperature).
- Ratio: Has a true zero point (e.g., height).
Descriptive Statistics
- Mean (x̄): The average of a data set. Excel: =AVERAGE(data)
- Median: The
Parameter Estimation and Model Analysis in Mathematical Modeling
Parameter Estimation and Model Analysis
Testing Estimation Algorithms
It is crucial to test estimation algorithms with both “noise-free” and noisy data. Noise-free data, often obtained through model simulation, provides a baseline for accurate parameter estimation. Testing with noisy data, which mimics real-world experimental data, reveals the algorithm’s robustness and ability to handle uncertainties.
Evaluating Optimal Estimates
Due to the complexity of parameter spaces, local minima can lead to different
Read MoreUnderstanding Future Values, Compound Interest, and Bond Valuation
CH5 Future Values and Compound Interest
Key Concepts:
- Future Value: The amount an investment will grow to after earning interest.
- Compound Interest: Interest earned on both the initial investment and accumulated interest.
- Simple Interest: Interest earned only on the original investment.
Examples:
- Simple Interest: $100 at 6% for 5 years earns $6 per year, resulting in a final balance of $130.
- Compound Interest: $100 at 6% compounded annually for 5 years grows to $133.82.
Future Value Formula:
FV = PV(1+r)
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