Test2

• Experimental stimulus
• Cause
• Researcher manipulated

• Not researcher manipulated
• Effect

Measuring of a dependent variable among subjects prior to the experiment and after being exposed.

Receive stimulus

Does not receive stimulus

• One-Shot Case Study
• One Group Pretest-Posttest Design
• Static-Group Comparison

• 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.

• Choose appropriate question forms: open closed
• 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

Process of transforming raw data into a standardized form.

• visible, surface content-
• Ex: count the number of times things blow up, people are being shot at to determine violence in a movie.

• Underlying meaning of communications,
• Ex: overall assessment of how violent the movie was.

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

• 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?

initial classification and labeling of concepts

• identify the core concepts
• Ex: perception of fairness

• identify the central concept that organizes the other concepts that have been identified
• ex: Professor-student relationships.

Numerical representation and manipulation of observations for the purpose of describing and explaining the phenomena

• Researcher must engage in a coding process after the data has been collected
• Generate codes from your data

• Begin with well developed coding scheme: Researcher may use existing coding scheme
• Generate codes from your data: researcher creates his/her own codes

average

Dividing the sum of the values by the total number of cases

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

• 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

The body of statistical computations relevant to making inferences from findings based on sample observations to some larger population

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 general to specific