Summary: Research Methodology In Environmental Science

Read the summary and the most important questions on Research Methodology in Environmental Science

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• Research is systematic, what does that mean?

  Following systematic, recognized procedures for doing research, i.e. A methodical way of arriving at research questions, operationalizing key constructs, collecting data, modelling those data, making inferences from the estimates produced by applying the statistical model to the data. We need to be systematic so that others can do the same. Explicitly justify every step you take.

• Empirical data source

  Knowledge acquired through observation. Based on measurements of variables, selected how? Necessarly also uses non-empirical tools such as logic.

• Non-empirical: knowledge based on logic or definition.

  Models? E.g. Philosophy or mathematics. Logic works with premises and these are at least to some extent informed by observations from the world. There are thus some empirical aspects.

• Fundamental, pure of theory-oriented research

  Gain knowledge for the purpose of improving or expanding the existing knowledge about a specific topic. In the long term this can also be helpful in solving problems.

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• Steps conceptual framework

  1. Select key constructs in your research question
  2. Look at the literature that covers your problem using those constructs (and synonyms)
  3. Identify definitions of the key constructs (and links between key constructs)
  4. Use/adapt those definitions (and links) that make sense for your research

• Combination of the tree tools (path, tree, facet) are informed by..

  Your chosen conceptual framework. You cannot drag things into the diagrams that are not in the conceptual framework

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• Most important reason that we distinguish between the scales

  Determines the statistical procedures you can use in data analysis

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• Construct validity vs predictive validity

  Predictive validity can be regarded as a form of construct validity if it's supported by theory. Construct validity, however, is broader since it doesn't just focus on predicting effects, but on finding any kind of correlation between constructs consistent with your model

• Reliability: definition and how is it assessed?

  An instrument is reliable if it gets the same reuslts in similar citcumstances, ie. If it is consistent. 
  Assessed by:
  Repeated measures of a stable characteristic  
  Measurement by parallel instruments
  Measurement by parallel units (e.g. Replicates)

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• Major problem in causality: over-determination

  if there are two causes of Y, where only one is sufficient to trigger Y, experimentally manipulating any of them without manipulating the other one as well would lead to the mistaken conclusion that none of them are true causes. E.g. Being shot by two guns at the same time. If one shooter is manipulated not to shoot, the victim still dies, but that does not mean that shooting has no influence on dying.