Sociological Objective

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Sociological Objective

• Answer Questions
• How will people vote?
• What is it like to be homeless?
• What causes kids to use Tina?
• Does the scared straight program work?
• Generate and test theories to use for
  explanations and predictions about the social
  world

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Types of Research

• Descriptive (qualitative or quantitative)
• Researchers simply attempt to describe phenomena
  among a group or population
• Exploratory (usually qual, ideo, inductive)
• Knowing little about a group, researchers
  attempt to understand how they get along, what
  concerns them, and how they define experiences
• Explanatory (usually quant, nomo, deductive)
• Researchers attempt to identify causal
  relationships or test theories or predictions
• Evaluation (qualitative or quantitative)
• Researchers describe or identify the impact of
  policies, activities, or programs, typically for
  clients that must justify their effectiveness

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Quantitative vs. Qualitative

• Qualitative Research
• Designed to capture social life as participants
  experience it
• Emphasis on subjective, meanings
• Quantitative Research
• Designed to measure social variables
• Emphasis on numerical precision

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Inductive vs. Deductive

• Inductive Research
• Collect data in order to develop a theory that
  can explain those data
• Generating causal statements
• Deductive Research
• Start with a theory, collect data and use it to
  see if the theorys predictions hold true in the
  data
• Testing causal statements

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The Research Circle
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Sampling

• To do any of the previous types of research, one
  must have people to study.
• Sampling refers to selecting cases, or plain and
  simple, getting data.
• Whenever we attempt to make statements about a
  set of people in general using a smaller group of
  people (generalization), the data we are using is
  a sample.
• Sample vs. Census
• Census A complete count of an entire
  population
• So why dont we always do a census?
• Depending on how the sample was generated, there
  are limits on how much we may generalize.

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Sampling

• Types of Samples
• Case Studies Persons in Field Studies Contexts
  Observed
• Archival Data Experiment Participants Survey
  Data
• Depending on how the sample was generated, there
  are limits on how much we may generalize.
• Given limits on generalizability, the purpose of
  your research will help determine the type of
  sampling you do.

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Sampling

• Sampling Techniques
• Nonprobability Sampling methods that do not let
  us know in advance the likelihood of selecting
  for the sample each element or case from a
  population
• vs.
• Probability Sampling methods that allow us to
  know in advance how likely it is that any element
  of a population will be selected for the sample
• Knowing the chance of selection allows one to
  control sampling bias (under or
  overrepresentation of a population characteristic
  in a sample)

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Sampling

• Sampling Techniques
• Nonprobability
• (Very common in psychology, medicine, sociology)
• Availability Sampling, convenience
  samplingselection of cases based on what is
  easiest to get
• Experiments
• Exploratory and Qualitative research
• Avoid this if you can
• Quota SamplingKnowing something about your
  target population, you select your availability
  sample to ensure that it looks similar to your
  population

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Sampling

• Sampling Techniques
• Nonprobability
• Snowball SamplingRespondent-driven sampling
  where initial respondents refer others to the
  researcher
• Usually used with hard-to-discover populations
• Bias introduced by structured nature of
  affiliation
• Can be improved with incentives to subjects to
  recruit a certain number of new respondents
• Purposive Samplingtargeting select people for a
  sample because of their unique position
• Helps get understanding of systems or processes
  or information on a target population
• Not representative of population in general

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Sampling

• Sampling Techniques
• Nonprobability
• Nonprobability samples have limited
  generalizabilityyou can never be sure the sample
  represents the population
• But, researcher can work to establish what the
  sample represents
• Why use nonprobability samples?
• Well-suited for exploratory and evaluation
  research
• Nonprobability does not mean intentional attempt
  to make sample nonrepresentative
• We cannot all be identified by sampling frames,
  sometimes making nonprobability sampling more
  accurate
• More Efficiency
• Social and social psychological processes can
  be effectively studied and described
• No project is ever enough anyway, community of
  scholars can add information through other
  researchcollections of projects can create a
  complete picture

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Sampling

• Sampling Techniques
• Probability Sampling methods that allow us to
  know in advance how likely it is that any element
  of a population will be selected for the sample
• Goal A representative sample of a target
  population
• Probability sampling begins with a sampling
  frame, or a list of all elements or other units
  containing the elements in a population.
• E.g., Phone book, All Universities, Known
  Addresses, Subscribers to a magazine.
• If a sampling frame is incomplete (which they
  usually are) then the accuracy of the sample is
  compromised. The researcher has the burden of
  assessing the sampling error or bias.
• Correction for CS, p. 89

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Sampling

• Sampling Techniques
• Probability
• Simple Random Samplingcases are identified
  strictly on the basis of chance.
• Random number table (AF, p. 19) to select from
  sampling frame
• Random digit dialing
• Equal probability of selection
• Systematic Random Samplingusing a list, the
  first case is selected randomly, then subsequent
  cases are selected at equal intervals.
• Typically the same as Simple Random Sampling
• Be aware of periodicity

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Sampling

• Sampling Techniques
• Probability
• Cluster Samplingused when sampling frames of
  individuals are difficult to obtain, but clusters
  are identifiable. Randomly select clusters, then
  use the clusters sampling frames to select
  cases.
• E.g., There is no national list of independent
  Baptists, but almost all independent Baptist
  churches can be identified.
• Select down to smaller number of clusters, then
  do the difficult work of identifying elements
  (persons to participate)
• Generally better to maximize the number of
  clusters and minimize number of cases from each
  cluster because clusters tend to be homogeneous
• Often called multistage sampling. When one uses
  two or more successive sampling steps one is
  doing multistage sampling.
• Each stage produces sampling error more stages,
  more error

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Sampling

• Sampling Techniques
• Probability
• Stratified Random Samplingsampling frame is
  divided into strata of interest, cases are drawn
  from each stratum on the basis of chance.
• Small subpopulations of interest may yield too
  few cases in simple random sampling. To
  compensate, the researcher draws samples from
  each subpopulation independently.
• E.G., Hispanic population of Mobile County is
  around 2. A random sample of 100 would produce
  1 3 Hispanicstoo few to generalize to Mobile
  County Hispanics.
• Do independent sampling from each stratum.

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Sampling

• Sampling Techniques
• Probability
• Stratified Random Sampling
• Proportionate Stratified Samplingselect cases in
  a way that ensures the same proportion from each
  stratum in the sample as exists in the
  population.
• Population 2 Hispanic, 5 Asian, 30 black,
  63 white
• Sample of 1,000 20 Hispanic, 50 Asian, 300
  black, 630 white
• Disproportionate Stratified SamplingProportion
  selected from each stratum is not the same as in
  the population.
• Population 2 Hispanic, 5 Asian, 30 black,
  63 white
• Sample of 1,000 250 Hispanic, 250 Asian, 250
  black, 250 white
• Idea is to get a lot of cases in each stratum
• When combining all cases into one sample, use
  weighted averages

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Sampling

• Sampling Techniques
• Probability
• Just because a sample is random, that does not
  mean that it is representative or that the
  research is good.
• Limited Sampling Frame
• Think of presidential phone polls
• Who is at home? Type of person, day of polling,
  etc.
• Who has a land line?
• Sample large enough?
• Problems of non-responserandom non-response
  okay, but systematic non-response is biasing
• Phone surveys typically do not report response
  rate. They are often below 30
• How were questions worded Measurement error
• Problems of misspecified models Leads to not
  asking the right questions

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Sampling

• Sampling Techniques
• Probability
• Issues of Sample Size
• Larger samples produce less sampling error
• Too large is a waste of money
• Big is good, but accurate and appropriate are
  better
• Fraction of population sampled does not increase
  accuracy unless fraction is very large
• The more heterogeneous the population, the larger
  the sample needed.
• The more variables of interest, the larger the
  sample needed.
• The weaker the effects, or the smaller the
  differences between groups, the larger the sample
  needed to see effects or differences between
  groups.
• TO SUM MORE COMPLEXITY REQUIRES LARGER SAMPLES