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  1. What are the assumptions of science? (5)
    • 1. Nature is orderly
    • 2. Nature is knowable
    • 3. All events have natural explanations
    • 4. Knowledge is based on observation
    • 5. Nothing is self evident
  2. What is rationalism?
    Theory that opinions and actions should be based on reason and knowledge rather than on religious belief or emotional response.
  3. What is empiricism?
    All knowledge is derived from sense-experience
  4. Valid/Invalid arguments?
    Valid: Affirming the antecedent, denying the consequent

    Invalid: Affirming the consequent, denying the antecedent
  5. What is deductive reasoning?
    Moving from the General to the Specific
  6. What is inductive reasoning?
    Moving from the Specific to the General
  7. What is measurement?
    • To assign symbols/use numbers
    • according to a set of rules
  8. What is nominal measurement?
    • Strictly categorical. It is 1) Exhaustive 2) Mutually Exclusive 3)
    • Without Order.

    • Example: “What religion are you? 1 – Catholic 2 – Jewish
    • 3 – Buddhist 4 – Other"
  9. What is ordinal measurement?
    • This has an order. It is 1) Exhaustive 2) Mutually Exclusive 3) Ordered.
    • 4) Cannot measure distance.

    • Example: “x=Highest level of education completed.1 – Less
    • than HS 2 – HS or GED grad 3 – Some College 4 – College Grad 5 – Advanced
    • Degree”
  10. What is interval measurement?
    • This can measure distance, including negative values. It is 1)
    • Exhaustive 2) Mutually Exclusive 3) Ordered 4) Can measure distance 5) CANNOT
    • calculate ratios.

    • Example: A number line ordered from -$10k to $10k, with
    • subjects placed along the line with their stock market gains.
  11. What is ratio measurement?
    • This can measure distance and ratios, but not negative values. It is 1)
    • Exhaustive 2) Mutually Exclusive 3) Ordered 4) Can measure distance 5) can
    • calculate ratios.

    Example: Number line ordered from $0 to $100k, people’s incomes.
  12. Two questions to determine type of measurement:
    • Is there a value order?
    • Could there be negative values?
  13. Is there a value order to the categories?
    • Yes - Ordinal
    • No - Nominal
  14. Could the measurement have negative values or numbers?
    • No – Ratio
    • Yes – Interval
  15. What is measurement validity?
    Are you measuring what you claim to measure?
  16. Types of measurement validity? (4)
    • 1. Face validity/does it look valid
    • 2. Content validity/does it encompass everything?
    • 3. Predictive validity/should be strong correlations with other studies
    • 4. Construct validity/degree to which inferences can be made from operationalizations in your study
  17. What is measurement reliability?
    Does the measure yield consistent and stable results?
  18. Types of measurement reliability? (3)
    • 1. Observer reliability/how observer may have affected data collection
    • 2. Instrument reliability/are your instruments reliable and accurate?
    • 3. Phenomenon reliability/are there any phenomena that have affected the data?
  19. What is an hypothesis?
    tentative answer to a research problem, expressed in the form of a relationship between dependent and independent variables
  20. What is a dependent variable?
    the variable we want to explain or predict
  21. What is an independent variable?
    The variable we conjecture explains or predicts Y (dependent)
  22. What is a control?
    Any other variable that may impact the x+y relationship
  23. What is a sample statistic?
    • Limited number of observations
    • Selected from a
    • population
    • On a systematic or random basis,
    • Which yields generalizations about
    • the population.
  24. What is a population parameter?
    • Numerical expressions
    • summarizing various
    • aspects of the entire population
  25. What is the relationship between sample stats and population parameters?
    We use sample stats to infer/generalize about the population parameter
  26. What is a probability sample?
    a sample that permits specifying the probability that each sampling unit will be included in the sample
  27. Why is it important to have a probability sample?
    So you can make an inference to the population
  28. What is a simple random sample?
    Each individual is chosen randomly and entirely by chance, such that each individual has the same probability of being chosen at any stage during the sampling process, and each subset of k individuals has the same probability of being chosen for the sample as any other subset of k individuals.
  29. Name the four types of probability samples
    • 1. Simple random
    • 2. Systematic random
    • 3. Stratified sample
    • 4. Cluster sample
  30. What is simple random good for?
    small populations
  31. What is systematic random sampling?
    • You pick a number K at random (usually)
    • then every "Kth" number after that
  32. What is a stratified sample?
    Probability sampling design in which the population is divided into homogenous strata within each of which sampling is conducted
  33. What is a cluster sample?
    • The entire population is divided into clusters,
    • And a random sample of these clusters are selected.
    • All observations in the selected clusters are included in the sample.
    • D.C.R.S.S.O.C.I.S.
  34. Three determinants of sample size?
    • 1. Practical factors (time/money)
    • 2. Math factors (acceptable error margin and sophistication of data analysis)
    • 3. Population factors (how diverse/homogeneous is the population?)
  35. What are the 4 types of non-probability sample types?
    • 1. Quota sampling
    • 2. Purposive sample
    • 3. Convenience sample
    • 4. Snowball sample
  36. What is a quota sample?
    Getting a certain quota met, e.g. get 200 construction workers
  37. What are some purposive samples?
    focus groups, precinct exit polls
  38. What are some convenience samples?
    radio call ins, magazine surveys
  39. What is a snowball survey for?
    Hard to reach populations (e.g. crack dealers)
  40. What are the three factors of internal validity (Did x cause y)?
    • 1. Correlation
    • 2. Time order
    • 3. Non-spuriousness
  41. What are the two factors of external validity?
    • 1. Random probability sample
    • 2. Real world data collection environment
  42. What are the elements of a classic experimental design? (4)
    • Stimulus/response relationship with
    • Independent/dependent variables
    • Pre/post testing
    • Control groups
  43. Internal validity of an experimental design
    Internal validity is strong in classic experiment design because you have a large degree of control
  44. External validity of classic experiment design
    • 1. Random population? No probability sample
    • 2. Real world setting? Maybe not
  45. What is a correlational design?
    • Data are used to
    • Examine relationships between properties and dispositions,
    • Establish causal relations between them, or
    • Describe the pattern of relation
  46. How do correlational and classic experimental designs differ?
    correlational designs are not meant to show causation
  47. Internal validity of correlational designs?
    • 1. Correlation? Either there or not
    • 2. Time order? Often straightforward, not always
    • 3. Non spuriousness? A problem for correlational design, hard to prove
  48. External validity in correlational designs?
    By definition, you have a probability sample and real world setting. Strong external validity.
  49. What are quasi-experimental designs? (3)
    • Two or more groups and or measures over time.
    • May/may not have stimulus.
    • May/may not have a probability sample.
  50. Internal validity of quasi-experimental designs?
    • Weaker than true experiments
    • Stronger than correlational
  51. Disadvantages of Cross-sectional and Quasi Experimental
    • Lack of control over rival explanations
    • Causation must be logically inferred
  52. Advantages Cross-sectional, Quasi Experimental
    • It allows researchers to carry out studies in natural settings with probability samples
    • It doesn't require random assignment of individual cases to control groups
  53. Criteria for inferring causation? (4)
    • Comparison
    • Manipulation
    • Control
    • Generalizability
  54. What is a nomothetic explanation?
    It relies on a sample probability or nonprobability
  55. What is an ideographic explanation?
    You can't draw a sample, and instead use narrative elements to explain (an individual or leader's decisions etc)
  56. What is a cross sectional design?
    • It draws a random sample at different points in time;
    • you might ask random samples to respond to a set of questions about their attitudes (e.g. attitudes about nuclear power)
  57. What is a longitudinal design?
    Where you conduct several observations of the same subjects over a period of time, sometimes lasting many years.
  58. Describe the standard normal curve? (3)
    • 1. It is bell shaped
    • 2. The mean is in the middle, with 50% on each side
    • 3. The mean will equal to zero, with a standard deviation equal to one
  59. What is central tendency?
    Measures that reflect a typical characteristic of a frequency distribution (mean, median, mode)
  60. What is dispersion?
    Statistical measures that reflect the degree of spread in a distribution
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terms and ideas for exam 1
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