Data Management Overview

Data Management

• Data are raw information or facts that become useful information when organized meaningfully.
• Data can be qualitative or quantitative.
• Data management involves looking after, checking, preparing, and documenting data and metadata.
• Data management ensures analysis uses high-quality data, enabling correct conclusions.

Importance of Data Management

• Good data management leads to improved data quality.
• It improves data processing efficiency and the meaningful interpretation of data.
• It enables re-use of data in future studies and integration with other research.

Methods of Data Collection

• Census: A systematic survey of all members of a population. (Rarely used due to high cost and population dynamism.)
• Sample Survey: A selection of a subset of a population to provide knowledge about the entire population. (Lower cost, faster collection, and more accurate due to smaller data sets).
• Experiment: A study controlled variables for an intended effect on other variables; replication is possible. Examples are medical treatments.
• Observation Study: A study without controlled variables where replication is impossible. Example is correlation between smoking and lung cancer. Data collection consists of observations.

Planning and Conducting Surveys

• Characteristics of a well-designed survey:

  • Representative of the population
  • Uses a random selection method (e.g., random number generator).
  • Neutral and carefully worded questions
  • Controls for potential errors or biases (e.g. possible sources of bias, wording, limited samples)
  • Appropriate sampling frame (a subset of potential subjects that could be measured)

• Sampling Methods:

  • Nonprobability Sampling: Sampling method where the elements of the populations have no chance of selection or their probability cannot be determined. Selection criterion is not random.
  • Probability Sampling: Determination of the sampling units and their probability of selection. Examples include:
    • Simple Random Sampling (SRS): Selection of sampling units with equal probabilities.
    • Systematic Sampling: Selecting every nth unit from a list (more efficient in large datasets).
    • Stratified Sampling: Dividing the population into strata and selecting a random sample from each.
    • Cluster Sampling: Dividing population into clusters, then selecting a random sample from those clusters.

Planning and Conducting Experiments

• Characteristics of a well-designed experiment:
  • Clearly state the research purpose (includes estimates of treatment effects and experimental variability).
  • Design using blocking and randomized treatment assignments.
  • Includes control groups
  • Random assignment to minimize biases
  • Replication to verify results
• Experiments should control:
  • Placebo Effect: An effect that occurs when administering a fake treatment.
  • Confounding Variables: Extraneous factors correlating with both the dependent and independent variables

Statistical Analysis (Chi-Square Tests)

• Goodness-of-Fit Test: Checks if a sample matches an expected distribution.
• Test of Independence: Assesses if two variables are related.
  • Assumptions: Random sample, independent observations, no expected values less than 5.
  • Procedure: Calculate expected frequencies, compute the test statistic, compare it to a critical value to determine if there is a sufficient statistical evidence to reject the null hypothesis.