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GH Data Analysis
Intro to quantitative research (T Pierce)
Health Care

Additional Health Care Flashcards




purpose of quantitative research

seeks to find meaningful relationships between variables

How do you determine relationships with quantitative research
  • statistical analysis via
    • retrospective or prospective studies
    • controlled studies
    • observational studies
  • descriptive statistics
    • presentation, organization, and summarization of data
  • inferential statistics
    • ID relationships that can be generalized from our study population to the larger population it represents
observational data
  • collect observations about subject/topic of interest
    • ex: case control studies, survey studies, HMIS data
controlled experiments
  • data is collected, change is made, data is collected again following change
  • must have control group
correlations vs. causation
  • correlation- two events (A,B) observed to happen together
  • causation- A is the cause of B
  • statistical analysis tells you that A occurs in correlation with B (therefore A causes B is FALSE)
  • statistical correlation alone never proves causation
  • gold standard for determining causation is carefully designed, controlled experiments
establishing causation in observational data
  • strength- numerical strength of correlation
  • consistency- observed in many places at many times by many different observers in different circumstances
  • specificity- effect limited to certain observations in certain specific situations
  • temporality- A must occur before B
  • biological gradient (dose response relation)
  • plausability- scientific credibility of relationship
  • coherence- possibility of causal relationship should not conflict with what is known about natural history and biology of situation
  • experimental evidence- if you take away intevention, does "B" does happen
  • analogy- reason from similar phenomena
statistical significance vs. economic significance
  • statistical- we are 95% sure...
  • economic significance
    • explanatory variable has meaningful and plausible influence on dependent variable (will it make a significance public health difference?)

statistical evidence is necessary but not sufficient for economic significance

importance of good data collection
  • before you can analyze data, first need to collect with an understanding of what you are collecting
  • not understanding the data you have collected, or using the wron data, can lead to incorrect conclusions and drive misguided changes in policy
  • big part of what determines whether data is good is survey design and sampling
scientific method
  • systematic observation- or measurement of various features of behaviors in the world
  • logical explanation- in the form of a theory or model that makes sense according to basic rules of logic and accepted facts
  • prediction- in the form of hypothesis, based on theory, of what we will observe if the theory is true
  • openness- meaning of methods used to produce evidence are clearly documented and made available for review (this allows for replication- repeatin study to see if results hold)
  • skepticism- researchers scrutinize and critique each other's work, a process reviewed to as peer review, in search of possible shortcomings or alternative explanations
designing questions that would be good measures
  • you are interested to answers in the survey, not intrinsically but because of their relationship to something they are suppose to measure (ex: failure to use a condom = risky sexual behavior)
  • questions are only good if they are:
    • reliable- providing consistent measures in comparable situations
    • valid- answers correspond what they are intended to measure
  • measurement refers to the process of systematically observing some feature of characteristic of the world and then recording it
model of measurement
  • start with a construct
  • find a way to measure it
  • error is a part of that measurement
    • ALWAYS have some measurement error in studies
conceptualization and operationalize
  • first step is figure out what you want to measure
  • must be defined carefully and precisely
    • some concepts are not easily defined such as poverty
    • require value judgements
    • manifest vs. latent constructs
    • dimensions
  • once you have the concept, it is time to operationalize
    • ex: what did the US do with poverty?
increasing reliability of answers
  • providing consistent measures in comparable situations
    • when two respondants are in the same situation they should answer the same question in the same way
    • any difference in answers stem from actual differences
  • good questions
    • researcher's side of the question and answer process is entirely scripted so that the questions as written fully prepare a respondent to answer questions
    • questions mean the same thing to every respondent
    • kinds of answers that constitute an appropriate response to the question are communicated consistently to all respondents
    • avoid inadequate wording
    • ensure consistent meaning of all respondents (can do this via pre-testing)
      • need common frame of reference
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