Athena slides

• Research methodology

  1. Problem statement

  2. Research question and factors

  3. Sub-research questions

  4. Conceptual model

  5. Hypotheses

  6. Operational and conceptual definitions

  7. Indicators
• Problem statement

  Short clear explanation of the current problem
• Research question

  Fact-oriented and information-gathering
• Sub-research questions

  Derived by singling out the individual factors influencing the independent variable
• Conceptual model

  Visual model using the sub-research questions, including theoretical concepts and control variables
• Hypotheses

  Formulated from the sub-research questions
• Operational and conceptual definitions

  Clearly define any concept touched on in the conceptual model
• Indicators

  Determined for each variable to measure the concepts
• If the combination of indicators covers all aspects of a concept, it is said to be high in content validity
• Unit of analysis

  The level at which the research is performed, and which object are researched
• The unit of analysis is derived from the research question
• Sampling

  Selecting some of the elements in a population to draw conclusions about the entire population
• Population element

  The subject on which the measurement is being taken
• Representative samples are only a concern in quantitative studies rooted in a positivistic research approach. Qualitative studies rooted in interpretivism usually do not attempt to generalize their findings to a population
• Census

  A count of all the elements in a population
• Reasons for sampling

  • Lower cost
  • Greater accuracy of results
  • Greater speed of data collection
  • Availability of population elements
• Population

  The group we want to say something about
• Sampling frame

  The group you can say something about
• Sample

  Your selection from the sampling frame
• The advantages of sampling over census studies are less compelling when the population is small and the variability within the population high. Two conditions are appropriate for a census study: when the population is small and when the elements are quite different from each other
• Characteristics of a correct sample

  • Accuracy: no systematic bias
  • Precision: values in the sample like values in the population
• Accurate sample

  One in which the under estimators and the over estimators are balanced among the members of the sample
• Sampling error

  The numerical descriptors that describe samples may be expected to differ from those that describe populations because of random fluctuations inherent in the sampling process
• Sample types

  • Probability sampling
  • Non-probability sampling
• Probability sampling

  The members of a sample are selected on a probability basis or by another means, ensuring each population element has a known non-zero chance of selection
• Simple random sampling

  The simplest form of probability sampling where each population element has a known and equal chance of selection
• Simple random sampling is often impractical as it requires a population list that is often not available, fails to use all the information about a population, and may be expensive to implement
• Population parameters

  Summary descriptors (mean, variance) of variables of interest in the population
• Sample statistics

  Descriptors of the relevant variables computed from sample data, used as estimators of population parameters
• Systematic sampling

  Every Kth element in the population is sampled, determined by dividing the sample size into the population size
• Advantages of systematic sampling

  • Simplicity and flexibility
• Stratified sampling

  The population is divided into several mutually exclusive sub-populations or strata, and the sample is constrained to include elements from each segment
• Reasons to choose stratified random sampling

  • To increase a sample's statistical efficiency
  • To provide adequate data for analyzing the various sub-populations
  • To enable different research methods and procedures to be used in different strata
• Cluster sampling

  The population consists of clusters of elements which are close to each other
• Data types determine which statistical technique you can choose to analyze your data
• Data types

  • Ratio
  • Interval
  • Ordinal
  • Nominal
• Ratio

  Order in numbers is important, intervals between numbers are fixed, meaningful zero point
• Interval

  Order in the numbers is important, interval between numbers is fixed
• Ordinal

  Order in the numbers is important, intervals between numbers are not fixed
• Nominal

  Numbers indicate categories, order is not meaningful