Psych GRE - Research Design, Statistics, Tests, and Measurements

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Psych GRE - Research Design, Statistics, Tests, and Measurements
2011-04-03 01:11:37
Research Design Statistics Tests Measurement

Research Design, Statistics, Tests, and Measurement
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  1. William Wundt (271)
    • 1879
    • founded first psychology laboratory
  2. Hermann Ebbinghaus (271)
    showed that higher mental processes could be studied using experimental methodology
  3. Oswald Kulpe (271)
    • disagreed with Wundt fundamentally - Wundt believed that whenever you thought of something an image of that thing formed in your mind (there could no thought without a mental image)
    • performed experiments to prove that there could be imageless thoguht
  4. James McKeen Cattell (272)
    studied under Wundt; introduced mental testing to the U.S.
  5. Binet (272)

    of Binet-Simon
    • in 1905, collaborated with Simon to publish the first intelligence test
    • introduced the concept of "mental age" or the age level at whic a person functions intellectually, regardless of his actual chronological age
  6. Binet-Simon test (272)
    • first intelligence test
    • use to assess the intelligence of French school children to ascertain which children were too mentally retarded to benefit from ordinary schooling
  7. William Stern (272)
    developed an equation to compare mental age to chronological age (which came to be known as the intelligence quotient, or IQ)
  8. Lewis Terman (272)
    in 1916, he revised the Binet-Simon test for use in the U.S. - it was called the Stanford-Binet Intelligence test
  9. hypothesis (272)
    • the first setp in research design involves indentifying a problem to study and forming this
    • tentative and testable explanation of the relationship between two or more variables
  10. variable (272)
    characteristic or property that varies in amount or kind, and can be measured
  11. operational definitions (272)
    how the researcher plans to define the varilables in the experiment so that the variables are measurable
  12. independent variable (273)
    variable whose effect is being studied and is the variable that the experimenter manipulates
  13. dependent variable (273)
    the response that is expected to vary with differences in the independent variable
  14. 3 types of research types (273)
    • true experiments
    • quasi-experiments
    • correlational
  15. corellational study (275)
    does not alter the independent variable (IV)
  16. true experiment (274)
    researcher uses random assignment and manipulates the IV
  17. quasi-experimental design (274)
    • does not use random assignment
    • lacks sufficient control over the variables, and therefore, definitive statements on causal factors cannot be made
  18. naturalistic observation (274)
    • researcher does not intervene at all in what is being studied, but observes what occurs naturally
    • AKA: field study
  19. sample (275)
    subset of the population
  20. random selection (275)
    each member of the population has an equal chance of being selected for the sample
  21. stratified random sampling (275)
    assures that each subgroup of the population is randomly sampled in proportion to its size
  22. representative sample (276)
    miniature version of the population
  23. 3 types of research designs (276)
    • between-subjects
    • matched-subjects
    • within-subjects (AKA repeated measures)
  24. between-subjects design (276)
    each subject is exposed to only one level of each indpendent variable
  25. matched-subjects design (276)
    • experimenter matches subjects on the basis of a varilable that he/she wants to control (if he/she wants to control for intelligence - he will pick the top 2 most intelligent, split them up, and split the pairs in this fashion until all subjects are assigned to groups)
    • pairing ensures that both groups are approximately equal on the matching variable
  26. within-subjects design (277)
    • each subject is exposed to all the conditions (give all subjects all levels of IV)
    • this allows the researcher to separate the effects of individual differences of a variable from the effects of the IV
    • problem: people may just do better on the second test because they are more familiar with the test format - or they may do worse on the second test because of boredom
  27. what is counter-balancing and why do we use it? (278)
    • half of the subjects are assigned to group 1 and then group 2; the other half are assigned to group 2 and then 1
    • this is done because of the problems with within-subjects design (people may just do better on the second test because they are familiar with format or they may do worse on the second test because of boredom)
    • all subjects will experience both levels in different orders
  28. confounding principles (278)
    unintended independent variables
  29. control group design (279)
    treating both groups equally in all respects except that the control group gets no treatment; the experimental group receives treatment
  30. nonequivalent group design (279)
    the control group is not necessarily similar to the experimental group since the researcher does not use random assignment
  31. experimenter bias (279)
    due to his/her expectations the experimenter might inadvertently treat groups of subjects differently, experimenter might let his/her expectations affect how the results are interpreted
  32. double-blinding (279)
    • one way to control for experimenter bias
    • neither the researcher who interacts with the subject nor the subjects themselves know which groups received the IV or which level of the IV
  33. single-bind experimenter (279)
    subjects do not know whether they are in the treatment or control group
  34. demand characteristics (279)
    refer to any cues that suggest to subjects what the researcher expects from them - if subjects have an idea what the researcher expectes, they will perform as expected
  35. placebo effect (280)
    • a kind of demand characteristic
    • when people are given a drug, they usually expect that the drug will be effective
  36. Hawthorne effect (280)
    tendency of people to behave differently if they know they are being observed
  37. External validity (280)
    how generalizable the results of an experiment are
  38. how can you control for experimenter bias? (280)
  39. how can you control for placebo effect? (280)
    control groups
  40. how can you control for Hawthorne effect?
    control groups
  41. how can you control for demand characteristics? (280)
  42. 2 basic types of statistics (280)
    • descriptive
    • inferential
  43. inferential statistics (280, 288)
    • generalize beyond actual observations
    • allow us to use a relatively small batch of actual observations to make conclusions about the entire population of interest
  44. descriptive statistics (280)
    organizing, describing, quantifying, and summarizing a collection of actual observation
  45. frequency distribution (281)
    graphic represenations of how often each value occurs
  46. measures of central tendency (281)
    • mode
    • median
    • mean
  47. mode (281)
    • value of the most frequent observation in a set of scores
    • if all values occur with equal frequency, there is no _____
  48. bimodal (281)
    when the data has two modes
  49. median (282)
    • the middle value when observations are ordered from least to most or from most to least
    • not the halfway point of numerical values
    • number in the middle of the ranking
  50. mean (282)
    the numerical halfway point between the highest score and the lowest score, the arithmetic average
  51. outlier (282)
    • extreme scores
    • the mean is the measure of central tendency that is most sensitive to these
  52. measures of variablity (282)
    • range
    • standard deviation
    • variance
    • AKA dispersion of scores
    • range (282)simply t
  53. range (282)
    simply the smallest number in the distribution subtracted from the largest number
  54. standard deviation (282)
    • provides a measure of the typical distance of scores from the mean
    • square root of variance
  55. variance (282)
    • standard deviation 2
    • description of how much each score varies from the mean
  56. normal distribution (283)
    symmetrical bell-shaped curve
  57. percentile (283)
    percentage of scores that fall at or below a particular score
  58. percentages of the normal distribution (283)
    • between -1 and +1 SD - 68% (34% between -1 and 0; and between 0 and +1)
    • between -2 and +2 SDs 96% (14% between -2 and -1; and between +1 and +2)
    • 100% between -3 and +3 SDs (2% from -2 to -3; and between +2 and +3)
  59. z-score (284)
    • another way of calculating how many standard deviations above or below the mean your score is
    • formula = score - mean / SD
  60. if you converted every score in a distribution to a z-score........(285)
    • if you have a distribution of z-scores and calculate the mean and standard distribution, the mean of the distribution of z-scores will always be zero andthe standard deviation will always be 1
    • this is true regardless of the mean and the standard deviation of the original distribution
  61. t-scores (285)
    t-score distribution has a mean of 50 and a standard deviation of 10
  62. correlation coefficients (286)
    • a descriptive statistic that measures to what extent, if any, two variables are related
    • two variables are related if knowing the value of one variable helps you predict the value of the other variable
    • this helps us understand the relationship and degree of association between two variables
    • allows us to mathematically specify how well we can predict the value of the second variable given the corresponding value of the first variable
    • can only be between -1.00 and +1.00
  63. positive correlation (286)
    a change in value of one of the variables tends to be associated with a change in the same direction of the value of the other variable
  64. negative correlation (286)
    change in the value of one of the variables tends to be associated with a change in the opposite direction of the other variable
  65. scatterplot (287)
    graphical representation of correlational data
  66. factor analysis (288)
    attempts to account forthe interrelationships found among various variables by seeing how groups of variables "hang together"
  67. significance test (289)
    • a tool researchers use to draw conclusions about populations based upon research conducted on samples
    • helps the researcher decide whether the research hypothesis or the null hypothesis is true
  68. null hypothesis (289)
    the population mean is the same as the sample mean
  69. statistically significant (290)
    if we reject the null hypothesis, the observed difference is ______
  70. criterion of significance (290)
    • the researcher decide what probability represents statistical significance before collecting data by establishing this...
    • by convention, psychologists usually use 5% (most are willing to reject the nulll hypothesis only if they are very sure that observed differences are not due solely to chance)
    • AKA alpha level
  71. Type I error (291)
    • rejecting the null hypothesis when it is true (there is no difference, but the researcher thinks there is a difference)
    • significant results are due to chance
  72. Type II error (291)
    • accepting the null hypothesis when it is false (thinking there is not a difference when there is)
    • AKA Beta
  73. types of significance tests (292)
    • t-test
    • ANOVA
    • chi-square test
  74. t-tests (292)
    used to compare the means of two groups
  75. ANOVA (292)
    used to compare the means of more than two groups
  76. chi-square (292)
    tests the equality of two frequencies or proportions
  77. meta-analysis (294)
    • statistical procedure that can be used to make conclusions on the basis of data from different studies
    • can be used to combine the results of these studies and come up with a more general conclusion
  78. norm-referenced testing (295)
    • assessing an individual's performance in terms of how that individual performs in comparion to others
    • one problem: the population to whom the tests will be administered can, and often does, change
  79. domain-referenced testing (295)
    • AKA criterion-referenced testing
    • concerned with the question of what the test tasker knows about a specified content domain
  80. reliability (295)
    • consistency with which a test measures whatever it is that the test measures.
    • if it is high, the test measures are dependable, reproducible, and consistent
    • standard error
  81. standard error of measurement (295)
    • an index of how much, on average, we expect a person's observed score to vary from the score the person is capable of receiving based on actual ability
    • the smaller this is, the better
  82. how do we assess reliability? (296)
    • test-retest
    • alternate-form
    • split-half
  83. test-retest method (296)
    • same test is administered to the same group of people twice
    • estimates the inter-individual stability of test scores over time
  84. alternate-form method (296)
    • tests reliability
    • examinees are given two different forms of a test that are taken at two different times
  85. split-half reliability (296)
    • test takers take only one test, but that one test is divided into equal halves
    • scores on one half are correlated with the scores on the other half
  86. validity (296)
    concerned with the extent to which a test actually measures what it purports to measure
  87. types of validity (296)
    • content
    • face
    • criterion
    • cross validation
    • construct - AKA convergent
    • discriminant
    • predictive
  88. content validity (296)
    test's coverage fo the particular skill or knowledge area that it is supposed to measure
  89. face validity (296)
    whether or not the test items appear to measure what they are supposed to measure
  90. criterion validity (297)
    how well the test can predict an individual's performance on an established test of the same skill or knowledge area
  91. cross validation (297)
    testing the criterion validity of a test on a second sample, after you demonstrated validity on an initial sample
  92. construct validity (297)
    • how well performance on the tests fits into the theoretical framework related to what it is you want the test to measure
    • AKA convergent validity
    • in order to show a test has this , researchers also have to show that performance on the test is not correlated with other variables that the theory predicts that test performance should not be related to (discriminat validity)
  93. discriminant validity (297)
    • in order to show a test has this , researchers also have to show that
    • performance on the test is not correlated with other variables that the
    • theory predicts that test performance should not be related to.