COMM 410 Exam II

  1. Independent Variable
    Stimulus, research manipulated, causes the Dependent Variable
  2. Dependent Variable
    Effect, not researcher manipulated, caused by another variable.
  3. Pretesting
    Measurement of DV among subjects prior to experiment
  4. Posttesting
    Remeasuring of DV after being exposed to IV, after the experiment
  5. Experimental Group
    Group that will receive experimental stimulus
  6. Control Group
    Group that will not receive experimental stimulus
  7. Randomization
    Assigning subjects to experimental and control groups randomly
  8. Matching
    Pairs of subjects are matched on their similarities on one or more variables, and one member of the pair is assigned to experimental group and to the control group
  9. One Shot Case Study
    • Researcher measures a single group of subjects on a dependent variable following the administration of an experimental stimulus
    • Ex: Show video to one group and give questionnaire.
  10. One Group Pretest-Posttest Desgin
    Researcher adds a pretest for the experimental group but lacks a control group.
  11. Static Group Comparison
    • Researcher does not add pretest to the experimental or control group.
    • Has control and experimental group.
  12. Sources of Internal Invalidity
    Possibility that the conclusions drawn from experimental results may not accurately reflect what went on in the experiment itself.
  13. SOII: History
    historical events may occur that will confound the results.
  14. SOII: Maturation
    people grow and change which can affect the results.
  15. SOII: Testing
    testing and retesting influences peoples behavior.
  16. SOII: Instrumentation
    different measures of the DV in pretest and posttest can affect the results.
  17. SOII: Statistical Regression
    danger that change occurs by virtue of evolution than by the effects of the stimulus.
  18. SOII: Selection Biases
    Comparisons dont have meanings unless the groups are comparable.
  19. SOII: Experimental Morality
    subjects may drop out of experiment before its completed.
  20. SOII: Casual Time Order
    ambiguity about the time order of the experimental stimulus.
  21. SOII: Diffusion or imitation of treatments
    subjects may share information with each other.
  22. SOII: Compensation
    pressure to offer some form of compensation to the control group.
  23. SOII: Compensatory Rivalry
    subjects deprived of the stimulus might work harder.
  24. SOII: Demoralization
    subjects deprived of the stimulus might just give up.
  25. Sources of External Validity
    Possibility that the conclusions drawn from experimental results may not reflect what went on in the experiment itself
  26. Solomon-four-group-design
    Four groups of subjects, assigned randomly from a pool
  27. Questionnaire
    • Instrument specifically designed to elicit information that will be useful for analysis.
    • Questions: respondents chooses one answer from a set of responses.
    • Statements: respondent agrees or disagrees. Close ended.
  28. Respondent
    A person who provides data for analysis by responding to the survey questionnaire
  29. Questions and Statements
    Questionnaires provide a method of collecting data by 1) asking people questions and 2) asking people to agree or disagree with statements
  30. Open-ended Questions
    Questions for which the respondent is asked to provide his/her own answers
  31. Close ended Questions
    Questions in which the respondent is asked to select an answer from among a list provided by the researcher
  32. Guidelines for asking questions
    • Choose Appropriate Question Forms
    • Make Items Clear
    • Avoid Double-Barreled Questions
    • Respondents Must Be Competent to Answer
    • Respondents Must Be Willing to Answer
    • Questions Should be Relevant
    • Short
    • Items Are Best
    • Avoid Negative Items
    • Avoid Biased Items and Terms
  33. General Questionnaire Format
    Questionnaires should be spread out and uncluttered
  34. Formats for Respondents
    check boxes adequately spaced out
  35. Contingency Questions
    survey question intended for only some respondents, determined by their responses to some other question
  36. Matrix Questions
    Efficient format for presenting a set of closed-ended questionnaire items that have the same response category
  37. Ordering Items in a Questionnaire
    Order of questionnaire items can affect responses
  38. Questionnaire Instructions
    Every questionnaire should have clear instructions and introductory comments where appropriate
  39. Pretesting the Questionnaire
    Questionnaires should be pretested before being administered to the study sample
  40. Self-Administered Survey
    • respondents are asked to complete the questionnaire themselves
    • Mail Distribution : great for taboo topics, poor return rate, can send follow ups.
    • In-Person: ideal method. Highest respoense rate, researcher has greatest control, grat for taboo topics.
  41. Survey Interviews
    • ask questions orally
    • Face-to-Face: higher rsponse rate, completion rate 80-85%, presence of
    • interview increases the number of I dont knows.
    • Telephone Survey Interviews: cheaper than face to face, RDD elimintes
    • potential dialing, greater control over data.
    • Appearance and Demeanor: researcher must remain neutral in appearance and actions.
    • Familiarity: researcher must be familiar with questionnaire.
    • Exact Wording of question.
    • Recording Responses: record responses accurately.
    • Probing: can use probes to elicit response.
  42. Online Surveys
    • Involves the use of the Internet and the world, wide, well suited for taboo topics.
    • Must be used with caution, respondents may not represent intended population.
  43. Unobstrusive Research
    Method of studying social behavior without affecting it
  44. Content Analysis
    • Study of recorded human communication
    • Well suited to answer what, to whom, why, how, with what effect?
    • ex: books, magazines, poems, songs, letters, etc.
  45. Analysis of Existing Statistics
    • Using data analyses that others have already done
    • Existing data supplemental source of data.
    • Existing statistics can provide a historical or conceptual context within which to locate original study
  46. Comparative and Historical Research
    • Examination of societies over time and in comparison with one another.
    • Using historical methods by sociologists, political scientists, and other social scientists to examine societies over time and in comparison with one another
    • Appropriate topics: Social Class, Capitalism, Religion, Revolution
  47. Coding
    Process of transforming raw data into a standardized form.
  48. Coding: Manifest Content
    visible, surface content
  49. Latent Content
    Underlying meaning of communications
  50. Needs Assessment Studies
    Studies that aim to determine the existence and extent of problems
  51. Cost-benefit Studies
    Studies that aim to determine whether the results of a program justify its expense
  52. Monitoring Studies
    Studies that provide a steady flow of information about something of interest
  53. Program Evaluation
    The determination of whether a social intervention is producing the intended result
  54. EMIC
    Researcher has an insider’s perspective because they are entering a familiar setting.
  55. ETIC
    • Researcher has an outsider’s perspective because they are entering an unfamiliar setting.
    • Nothing taken for granted, harder for researcher to access
  56. Key Informant
    • Important relationships that are developed before or while in the field
    • helps the researcher gain access to information, people, and situations that they wouldn’t be able to get access to.
  57. Quasi-Experimental Design
    Nonrigorous inquires somewhat resembling controlled experiments but lacking key elements such as pre- and posttesting and/or control group
  58. Time-series Designs
    Design that involves measurements made over some period
  59. Nonequivalent Control Groups
    Control group that is similar to the experimental group but is not created by the random assignment of subjects
  60. Multiple Time-series Designs
    Improved version of the nonequivalent control group design
  61. Qualitative Data Analysis
    • Nonumerical examination and interpretation of observations involves discovering meanings and pattersn of relationships most typical in field research and historical research.
    • Purpose of discovering underlying meanings and patterns of relationships
  62. Linking theory and analysis
    • Search for explanatory patterns
    • “plausible relationships proposed among the concepts and sets of concepts”
    • - Strauss and corbin
  63. Discovering Patterns
    seek to discover patters such as changes over time or possible causal links among variables.
  64. Six different ways of looking for patterns in a particular research topic
    • Frequencies: how often does it occur?
    • Magnitudes: examine the degree at which it happens
    • Structures: what types?
    • Processes: is there any order?
    • Causes: what could have caused it?
    • Consequences: how does it affect?
  65. Cross-Case Analysis
    • Analysis involving an examination of more than one case
    • Two strategies: Variable oriented and case oriented.
  66. Variable-oriented analysis
    • Analysis that describes and/or explains a particular variable
    • Aim to achieve a partial, overalla explanation usign relatively few varibles.
    • Similar to the idea of a nomothetic explanation.
    • Ex: predict the decision to attend college. Variables: gender, parental expectations, school performance, peer support.
  67. Case-oriented Analysis
    • Analysis that aims to understand a particular case or several cases by looking closely at the details of each
    • most extensiece and pursue in greater depth
    • similar to the idea of idiographic explanation
    • Ex: political pollster who attempts to explain voting intentions on the basis of 2 or 3 variables. Variables: political party, sociodemographics, gender, education.
  68. Grounded Theory Method
    • Theories are generated solely from an examination of data rather than being derived deductively
    • begins with observations rather than hypotheses
    • seeks to discover patterns and develop theories from the gorund up and inductive approach to research.
  69. Constant Comparative Method
    Observations are compared with one another and with the evolving inductive theory
  70. Coding
    • Classifying or categorizing individual pieces of data
    • -key to the process of discovering patterns among the data.
  71. Coding Units
    Must identify a standardized unit of analysis prior to coding
  72. Coding as a Physical Act
    The act of actual coding. May be done manually or on the computer.
  73. Open coding
    initial classification and labeling of concepts
  74. Axial coding
    identify the core concepts
  75. Selective coding
    identify the central concept that organizes the other concepts that have been identified
  76. Memoing
    Writing memos or notes to yourself and others involved in the project.
  77. Concept mapping
    • graphic display of concepts and their interrelations
    • -can be on a single sheet of paper, on a blackboard, computer, pages etc.
  78. Quantitative Analysis
    • Numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena
    • To conduct quantitative analysis:
    • Researcher must engage in a coding process after the data has been collected
    • Generate codes from your data
    • Search for explanatory patterns
  79. Univariate Analysis
    Analysis of a single variable, for the purposes of descriptions
  80. Frequency Distribution
    Description of the number of times the various attributes of a variable are observed in a sample
  81. Bivariate Analysis
    • Analysis of two variables simultaneously
    • Determining relationships between variables themselves
    • Purpose is usually explanatory
    • Example: Religious attendance Reported by Men and Women in 2004 (i.e. gender and religious attendance)
  82. Multivariate Analysis
    • Analysis of more than two variables simultaneously
    • Used to understand the relationship between two variables more fully
    • Example: Religious Service attendance, gender, and age (i.e. gender, religious attendance, AND age)
  83. Mean
    • Dividing the sum of the values by the total number of cases
    • Example: Age Number
  84. Mode
    Most frequently occurring attribute
  85. Median
    The middle attribute in the ranked distribution of observed attributes
  86. Dispersion
    distribution of values around some central value, such as an average
  87. Range
    • distance separating the highest from the lowest value
    • Example: Indicate that the age range is from 13 to 19
  88. Standard Deviation
    • measure of dispersion around the mean
    • Note: The smaller the deviation, the more tightly the values are clustered around the mean
    • Low-standard Deviation: tightly clustered values
    • High-standard Deviation: spread out values
  89. Continuous Variable
    • variable whose attributes form a steady progression
    • Example: age or income: steadily increases with each increment of time
  90. Discrete Variable
    • variable whose attributes are separate from one another
    • Example: gender or religious affiliation: jumps from category to category without intervening steps
  91. Statistical Analysis
    • Applied branch of mathematics especially appropriate for a variety of research analyses
    • Two types: Descriptive and Inferential Statistics
  92. Descriptive Statistics
    • Statistical computations describing either the characteristics of a sample or the relationship among variables in a sample
    • Used to summarize data
    • Sometimes used to summarize the distribution of attributes on a single variable and sometimes used to summarize the associations between variables
  93. Data Reduction
    • Reduction of data from unmanageable details to manageable summaries
    • Mean
    • Mode
    • Median
    • Dispersion
    • Standard Deviation
  94. Measure of Association
    • Descriptive statistics summarizing the relationship between variables
    • Proportionate Reduction of Error (PRE) :Logical model for assessing the strength of a relationship by asking howmuch knowing values one variable would reduce our errors in guessing values on the other
  95. Regression Analysis
    • Method of data analysis in which the relationships among variables are represented in the form of an equation
    • General formula for describing the association between two variables
    • Y = f(X)
    • “Y is the function of X”
    • “X causes Y”
    • “The value of X determines the value of Y”
    • Can be used
    • to predict the values of a dependent variable on the basis of
    • values of one or more independent variable
  96. Linear regression analysis
    A perfect linear association between two variables
  97. Multiple regression analysis
    Impact of two or more Independent Variable on a single Dependent Variable
  98. Partial regression analysis
    Effects of one or more variables are held constant
  99. curvilinear regression analysis
    Curved geometric lines instead of straight lines
  100. Inferential Statistics
    • The body of statistical computations relevant to making inferences from findings based on sample observations to some larger population
    • Used to estimate generalizability of findings arrived at through the analysis of a sample to the larger population from which the sample has been selected
    • Some estimate a single-variable characteristic of the population
    • Some estimate the relationship between variables in a population
  101. Statistical Significance
    estimate the relationships between variables in the population
  102. Tests of Statistical Significance
    class of statistical computations that indicate the likelihood that the relationship observed between variables in a sample can be attributed to the sampling error only
  103. Sampling error
    degree of error to the expected for a given sample design
  104. Variables
    Set of attributes.
  105. Attributes
    • Characteristics that describe an object, attributes are categories that make up the variables.
    • *social research involves the study of variables and their relationships.
    • * relationship between attributes and variables form the heart of description and explanation in science.
  106. Idiographic Reasoning
    Identifying all the reasons for a single outcome.
  107. Nomothetic Reasoning
    Identifying some of the reasons for a class of situations.
  108. Induction
    Expands from specific to general.
  109. Deduction
    Reduces from the general to the specific.
  110. Qualitative Data
    • Rich detail, non-numerical data, in depth details of the human experience.
    • Field research and interviews.
  111. Quantitative Data
    Numerical data, superficial description. Survey and experiments.
Card Set
COMM 410 Exam II
COMM 410