How to do Research 如何做研究

本文探讨了构成优秀研究问题的要素,包括可测量性、清晰的方法论、聚焦性、创新性、基于事实、清晰、可行性、趣味性和时效性。文章还概述了不同类型的研究,如描述性、相关性、探索性、评估和行动研究,以及各种来源类型,包括学术出版物、专业贸易来源、书籍、会议记录等,并提供了评估这些来源的标准。


Acknowledgment: Anne Goodchild Lecture

What makes a research question good

Good is a sujective word.

  1. Measurable - you have to know what question is answered.
  2. Methodology clear - e.g. Is there a correlation between… ? / Emiriclal / simulation
  3. Focused - scope, scale
  4. Needed - Innovative, unique
  5. Based on established facts
  6. Clear
  7. Achievable with resources ( time / money)
  8. Interesting
  9. Timely
  10. Complex

Types of research

  1. Descriptive
  2. Correlational
  3. Exploratory
  4. Evaluation
  5. Action

Sources

link-sources.

Types of sources

Scholarly publications (Journals)
Popular sources (News and Magazines)
Professional/Trade sources.
Books / Book Chapters.
Conference proceedings.
Government Documents.
Theses & Dissertations.

Criteria for evaluating sources

Authority( Institutions & Authors ) trustworthiness—Quality / number of citations
Timeliness—currency
Relevancy-popularity
Bias – Accuracy–popularity
Data Quality
Assumptions
Methodology described / references given----scholarly
Limitation
Quality control—reviewers
Primary or secondary
Objectivity( data / evidence; advertising, sponsorship)
Impact factor

To do IPTW by R, you can follow these steps: 1. Import your data into R and create a new variable to indicate treatment status (0 for control, 1 for treatment). 2. Create a new variable to hold the inverse probability weights (IPW). 3. Use R to estimate the propensity score for treatment using logistic regression. This will give you a predicted probability of receiving treatment for each observation. 4. Calculate the IPW for each observation by taking the reciprocal of the propensity score for treated observations, and the reciprocal of (1 - propensity score) for control observations. 5. Apply the IPW to your outcome variable using the survey package in R. Specifically, use the svyglm function to fit a generalized linear model with the IPW as weights. This will give you the weighted estimate of the treatment effect. Here's an example code: ``` # Load the survey package library(survey) # Import your data data <- read.csv("your_data_file.csv") # Create a new variable for treatment status data$treatment <- as.factor(data$treatment) # Estimate the propensity score using logistic regression ps_model <- glm(treatment ~ covariate1 + covariate2 + covariate3, data = data, family = "binomial") propensity_score <- predict(ps_model, data, type = "response") # Calculate the IPW ipw <- ifelse(data$treatment == 1, 1 / propensity_score, 1 / (1 - propensity_score)) # Apply the IPW to your outcome variable outcome_model <- svyglm(outcome ~ treatment, design = svydesign(ids = ~1, weights = ipw, data = data)) summary(outcome_model) ``` Note that this is just a general overview, and the exact steps may vary depending on your specific research question and data. It's important to consult with a statistician or other expert to ensure that you are using the appropriate methods and interpreting the results correctly.
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