COMM 410 Exam II

Stimulus, research manipulated, causes the Dependent Variable

Effect, not researcher manipulated, caused by another variable.

Measurement of DV among subjects prior to experiment

Remeasuring of DV after being exposed to IV, after the experiment

Group that will receive experimental stimulus

Group that will not receive experimental stimulus

Assigning subjects to experimental and control groups randomly

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

• 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.

Researcher adds a pretest for the experimental group but lacks a control group.

• Researcher does not add pretest to the experimental or control group.
• Has control and experimental group.

Possibility that the conclusions drawn from experimental results may not accurately reflect what went on in the experiment itself.

historical events may occur that will confound the results.

people grow and change which can affect the results.

testing and retesting influences peoples behavior.

different measures of the DV in pretest and posttest can affect the results.

danger that change occurs by virtue of evolution than by the effects of the stimulus.

Comparisons dont have meanings unless the groups are comparable.

subjects may drop out of experiment before its completed.

ambiguity about the time order of the experimental stimulus.

subjects may share information with each other.

pressure to offer some form of compensation to the control group.

subjects deprived of the stimulus might work harder.

subjects deprived of the stimulus might just give up.

Possibility that the conclusions drawn from experimental results may not reflect what went on in the experiment itself

Four groups of subjects, assigned randomly from a pool

• 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.

A person who provides data for analysis by responding to the survey questionnaire

Questionnaires provide a method of collecting data by 1) asking people questions and 2) asking people to agree or disagree with statements

Questions for which the respondent is asked to provide his/her own answers

Questions in which the respondent is asked to select an answer from among a list provided by the researcher

• 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

Questionnaires should be spread out and uncluttered

check boxes adequately spaced out

survey question intended for only some respondents, determined by their responses to some other question

Efficient format for presenting a set of closed-ended questionnaire items that have the same response category

Order of questionnaire items can affect responses

Every questionnaire should have clear instructions and introductory comments where appropriate

Questionnaires should be pretested before being administered to the study sample

• 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.

• 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.

• 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.

Method of studying social behavior without affecting it

• Study of recorded human communication
• Well suited to answer what, to whom, why, how, with what effect?
• ex: books, magazines, poems, songs, letters, etc.

• 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

• 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

Process of transforming raw data into a standardized form.

visible, surface content

Underlying meaning of communications

Studies that aim to determine the existence and extent of problems

Studies that aim to determine whether the results of a program justify its expense

Studies that provide a steady flow of information about something of interest

The determination of whether a social intervention is producing the intended result

Researcher has an insider’s perspective because they are entering a familiar setting.

• Researcher has an outsider’s perspective because they are entering an unfamiliar setting.
• Nothing taken for granted, harder for researcher to access

• 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.

Nonrigorous inquires somewhat resembling controlled experiments but lacking key elements such as pre- and posttesting and/or control group

Design that involves measurements made over some period

Control group that is similar to the experimental group but is not created by the random assignment of subjects

Improved version of the nonequivalent control group design

• 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

• Search for explanatory patterns
• “plausible relationships proposed among the concepts and sets of concepts”
• - Strauss and corbin

seek to discover patters such as changes over time or possible causal links among variables.

• 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?

• Analysis involving an examination of more than one case
• Two strategies: Variable oriented and case oriented.

• 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.

• 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.

• 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.

Observations are compared with one another and with the evolving inductive theory

• Classifying or categorizing individual pieces of data
• -key to the process of discovering patterns among the data.

Must identify a standardized unit of analysis prior to coding

The act of actual coding. May be done manually or on the computer.

initial classification and labeling of concepts

identify the core concepts

identify the central concept that organizes the other concepts that have been identified

Writing memos or notes to yourself and others involved in the project.

• graphic display of concepts and their interrelations
• -can be on a single sheet of paper, on a blackboard, computer, pages etc.

• 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

Analysis of a single variable, for the purposes of descriptions

Description of the number of times the various attributes of a variable are observed in a sample

• 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)

• 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)

• Dividing the sum of the values by the total number of cases
• Example: Age Number

Most frequently occurring attribute

The middle attribute in the ranked distribution of observed attributes

distribution of values around some central value, such as an average

• distance separating the highest from the lowest value
• Example: Indicate that the age range is from 13 to 19

• 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

• variable whose attributes form a steady progression
• Example: age or income: steadily increases with each increment of time

• variable whose attributes are separate from one another
• Example: gender or religious affiliation: jumps from category to category without intervening steps

• Applied branch of mathematics especially appropriate for a variety of research analyses
• Two types: Descriptive and Inferential 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

• Reduction of data from unmanageable details to manageable summaries
• Mean
• Mode
• Median
• Dispersion
• Standard Deviation

• 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

• 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

A perfect linear association between two variables

Impact of two or more Independent Variable on a single Dependent Variable

Effects of one or more variables are held constant

Curved geometric lines instead of straight lines

• 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

estimate the relationships between variables in the population

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

degree of error to the expected for a given sample design

Set of 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.

Identifying all the reasons for a single outcome.

Identifying some of the reasons for a class of situations.

Expands from specific to general.

Reduces from the general to the specific.

• Rich detail, non-numerical data, in depth details of the human experience.
• Field research and interviews.

Numerical data, superficial description. Survey and experiments.