mmc 4609 - communication research strategy - exam 2 review

8/6/2019 MMC 4609 - Communication Research Strategy - Exam 2 Review

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Section I: Quantitative research methods

Quick

Inexpensive

Efficient

Accurate

Advantages:

Survey Instrument problems

Sampling problems

Each type has disadvantages of their own

Disadvantages:

- Advantages and disadvantages of survey research:

- Survey errors (what each error means and how to reduce these errors)

o Random sampling error: Statistical errors that occur because of chance variation in the elements selected for the sample (the selection of the sam ple is too narrow

Self selection bias: person who don't respond due to indifference.

Nonresponse error: The statistical differences between a survey that includes only those who responded and a perfect survey that would also includewho failed to respond. Mail and internet surveys, usually).

-Deliberate falsification: when they give false answers to conform their perception of average person.

- Unconscious misrepresentation: when the way you ask the question, conditions the answer.

Acquiescence bias: the one resulting from respondents who only answer favorably.

Extremity bias: bias from respondents who answer with extremes.

Interviewer bias: bias from presences of interviewers.

Social desirability bias: results from respondents' desires to gain social prestige.

- Types of response bias:

Response bias: when respondents consciously or subconsciously lie on their answers.

Respondent error: A category of sample bias resulting from some respondent action or inaction. Divided in:

Data-processing error: Incorrect data entry, programming, or other errors during data analysis.

Sample selection error: Improper sampling design or procedure execution.

Interviewer error: mistakes to record responses correctly.

Interviewing cheating: interviewers fill fake answers or falsifying questionnaires

Administrative error:

o Systematic error/Nonsampling error: Errors resulted from some imperfect aspect of the research design or from a mistake in the execution of the research.

- Measurement issues (to ask the right questions in a right way): Questionnaire design

Don't ask long and confusing questions.

Complexity

- Leading: when question suggest certain answer.

- Loaded: question suggests socially desirable answer or emotion.

Leading and loaded questions

Ambiguity: be as specific as possible.

Double barreled questions: a question that may induce bias because covers two issues at once.

Assumptions ("Should Macy's continue its excellent gift wrapping program?")

- Unaided recall: "do you recall any commercials on that program?"

- Aided recall: when you give a list of items and ask a question that relates.

Avoid questions that makes respondents to recall information.

Allow I don't know or I don't recall.

o Avoiding mistakes:

Order Bias: bias caused by the influence of earlier questions.

Funnel technique: asking general questions before specific ones to get unbiased responses.

Filter Question: question that screens out respondents if they're not qualified for second question.

Pivot question: a filter question used to determine which version of second question will be asked.

o Question sequence

- Types of survey

High participation

Visual aids

Feedback

Probing complex answers

Length

Completeness

- Advantages:

Lack of anonymity

Cost

Interviewer influence

- Disadvantages:

Door to door and callbacks

Types of Personal Interviews:-

Personal interviews: Interviewer vs respondent face to face.

Interactive survey approaches: e.g., survey conducted with:o Interactive vs. noninteractive survey approaches: All kinds of surveys discussed in slides and textbook! (Advantages, disadvantages, procedures, special issues)

Quantitative research: Survey (Chapters 8, 9 & part of Chapter15)

Exam 2 - Checklist ReviewMonday, June 20, 2011

8:30 AM

Exam 2 Review Page 1


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Mall interception

More families are cutt ing the land lines

No call legislation limits calling to certain hours.

Disadvantages:

Landline interviews-

Opt in: no telemarketing to cellphones

Distraction of respondents

Area codes not matching geography

Hardware of phones is different

Disadvantages:

Mobile interviews-

Speed of data collection

Geographical flexibility

Respondent cooperation

Versatility of questioning

Length

Medium item non response

Average misunderstanding

Moderate interviewer influence

Supervision of interviewers

Moderate anonymity

Ease of callback and follow up

Low cost

Advantages:-

Telephone interviews

Geographical flexibilityCost

Respondent convenience

Anonymity of respondent

Absence of interviewer

Standardized questions

Time is money (six to eight weeks)

Length of mail questionnaire

Advantages:-

Persons who complete questionaire vs people who doesn't.

Person filling survey is not the intended subject.

Response rate: the number of quizzes returned or completed divided the number of eligible people asked to participate.

Disadvantages:-

Mail questionnaires:

Speed and Cost effectiveness

Visual appeal and interactivity

Participation and cooperation Accurate real time data capture

- Advantages:

Callbacks

Personalized and flexible questioning

Anonymity

Response rates

Security

Response quality

- Disadvantages

Internet survey: a self administered quiz on a website.

Noninteractive survey approaches: e.g.,

Tracking study: uses successive samples to compare trends and identify changes in variables.

Consumer panels: survey of the same sample to record their attitudes, behavior or purchasing habits over time. Expensive. Two staged process.

Longitudinal: survey of respondents at different times. Analysis of response continuity allowed and it charges over time.

Cross-sectional a study of various segments of population and data is collected at a single moment in time.

- Temporal classification

- Experiment: method that tries to establish causal inference.

- Experimental subjects: sampling units for experiment.

- Experimental variables: Independent variable (manipulated) and dependent variable.

- Experimental conditions: one of the possible levels of an experimental variable manipulation.

Experimental treatment: It explains how the experimental variable is manipulated.-

-

Main effect: The difference of the dependent variable on each experimental variable.

interaction effect: is due to a specific combination of independent variables.

Effects:

Experimental group: a group of subjects to whom an experimental treatment is applied.

Control group: do not receive experimental treatment. Used for comparison with experimental group.

Several experimental treatment levels: different treatment levels. (more than 2).

Basic Experimental Design: One independent variable.

- Experimental design

Quantitative research: Experiment (Chapter 11 & part of Chapter 12)



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Factorial experimental design: more than one independent variable.

Define your concept correctly

Define questions

Avoid mistakes

Selection of dependent variables

What does that mean? The assignment of a subject and treatments to groups randomly.

Generates equivalent groups

Facilitates comparison by creating similar groups

Why random assignment is favored?

Randomization: pick subjects freely.

Matching is when you cluster subjects based on characteristics.

Randomization vs. matching

o Selection and assignment of test units: Random assignment

- Disadvantages: involves repeated measures because with each treatment the same subject is measured.

Within subjects:

Can reduce demand characteristics (element that might give away the research hypothesis)

- Advantages:

Costly

Disadvantages:-

Between subjects design:

Advantages and disadvantages of each design

Laboratory experiment: The researcher has more complete control over the research setting and extraneous variables

Field experiment: Research projects involving experimental manipulations that are implemented in a natural environment.

Rule out extraneous variables: extraneous variables (aka: experimental confounds) show that there's an alternative explanation beyond the variables for any

differences on dependent variables.

o Within-subjects design vs. between subjects design

- Internal experimental validity: exists to the extent that an experimental variable is responsible for any variance in the dependent variable.

o Manipulation check: a validity test to make sure that manipulation does produce differences in the independent variable.

History: occurs when some change other than experimental treatment occurs during experiment and changes the dependent variable.

Maturation: time and natural events that coincide with growth and experience.

Testing: when initial measurements alerts subjects in a way that affects their response.

Instrumentation: a change in a wording of questions, change of interviewers or procedures causes a change in dependent variables.

Selection: sample selection error.

Mortality: when subjects withdraw the experiment without concluding it.

o Other factors that would reduce internal validity (read textbook)

- External experimental validity: the accuracy with which results can be generalized outside the subjects.

- Test-market (read textbook)

o What does it mean?

o Why should we conduct test-market?

Section II. Measurement (Chapters 13 & 14)

Concept: A generalized idea that represents something of meaning. Some are concrete (number of children) and some abstracts (love). Abstracts are hard to dConstruct: Concepts measured with multiple variables.

Conceptual definition (meaning of concept)Operational definition (translate conceptual definitions into measurement scalesMeasures

- What to measure

Nominal: values are assigned to an object for id or class ification purposes.

Ordinal: ranking scales arranging them based on how much of some concept they have.

Interval: has nominal and ordinal properties and captures differences in quantities from one observation to the next.

Ratio: has all the properties of intervals and can represent absolute quantities.

Levels of measurement

Discrete measures: measure that takes on only one of a finite number of values.

Continuous measures: Measures that reflect the intensity of a concept by assigning values.

Index measures: assigns a value based on how much of the concept being measured is associated with an observation.

Composite measures: Assign a value to an observation based on a mathematical derivation of multiple variables

- How to measure

- Three criteria for good measurement

o Reliability: An indicator of a measure's internal consistency (which represents a measure's homogeneity)

Face validity: A scales content logically appears to reflect what was intended to be measured Criterion validity: The ability of a measure to correlate with other standard measures of similar constructs of established criteria.

Convergent validity: Another way of expressing internal consistency, highly reliable scales contain convergent validity.

Discriminant validity: Represents how unique or distinct is a measure.

Construct validity: Exists when a measure reliably measures and truthfully represents a unique concept

o Validity: The accuracy of a measure or the extent to which a score truthfully represents a concept

o Sensitivity: A measurement instruments ability to accurately measure variability in stimuli or responses.

- Attitude measurement

Category scales: a rating scale of several categories.

The Likert scale: allow respondents to rate how they agree.

Semantic differential scale: scale on which respondents describe their attitude using a series of bipolar rating scales (good, bad)

Stapel scale: a scale where there's one adjective in the center and even num bers on its side.

Constant-sum scale: Divides points between characteristics to indicate their relative importance.

Graphic rating scale: allows to rate an object by choosing any point along a graphic continuum.

Rating scales: a measurement task that requires respondents to estimate the magnitude of characteristics of an object.

Ranking scales: measurement task that requires to rank order objects.

o Frequently used scales

Measurement is the process of describing some property of a phenomenon



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Sampling: take a sample of the population to study and then generalize the results to the whole population.-

- population, any complete group of entities.

- census, Investigation of all individual elements that make a population.

- sample, a subset of a larger population.

- sampling frame: a list of elements that defines the sample that'll be picked.

- sample units: each unit of the sample.

- Defining your target population Selecting the appropriate sampling frame (avoid sampling frame error) Probability vs. non-probability sample (Selecting the m

appropriate sampling method) Determining sample size Selecting actual sampling units

- Probability vs. non-probability sampling

Simple random sampling: random selection. Systematic sampling: a starting point is randomly selected, and from there, every n number on the list is selected.

Stratified sampling: Divides sample in strata of individuals who share similar characteristics.

Cluster sampling: the sampling unit is a large cluster of elements.

Multistage sampling: sampling that involves the use of two or more sampling techniques.

o Probability sampling: every member of population has nonzero chance of selection.

Convenience sampling: obtaining those people or units that are most conveniently available.

Judgment sampling: an experienced individual selects the sample based on personal judgment

Quota sampling: ensures that various subgroups of a population will be represented on pertinent characteristics to the exact extent that the investigator de

Snowball sampling: initial respondents are selected by probability methods and additional respondents are obtained from information provided by the initia

respondents.

o Non-probability sampling: units are selected on basis of personal judgment or convenience.

- Sampling error

o Random sampling error: the difference between the sample result and the result of a census conducted with identical procedures.

o Systematic sampling error: occurs if the sampling units in an experimental cell are different than the units in another cell, and this difference affect s the dependen

Section III. Sampling (Chapter 16)

Section IV. Data input and cleaning (Chapter 19)

1. Data integrity

The notion that the data file actually contains the information that the researcher promised the decision maker he or she wo uld obtain, meaning in part that the data hav

edited and properly coded so that they are useful to the decision maker.

a. What are data files? The way a data set is stored electronically in spreadsheet-like form in which the rows represent sampling units and the columns represent v

b. Raw data: The unedited responses from a respondent exactly as indicated by that respondent

c. Values, labels: unique labels assigned to each poss ible numeric code for a response.

String characters: represent formatting a variable using a series of alphabetic characters.

Numeric Variables: needs only one character to form a field.

d. String vs. numeric variables

i. Plug value: An answer that an editor plugs in to replace blanks or missing values to perm it data analysis.

List-wise deletion: A method of handling missing data in which the entire record for a respondent that has left a response missing is excluded from use

statistical analyses"

Pair-wise deletion: "A method of handling missing data in which only the actual variables for a respondent that do not contain information are eliminate

use in statistical analyses

ii. List-wise deletion vs. pair-wise deletion

e. Missing data

f. Reverse coding

Means that the value assigned for a response is treated oppositely from the other items

2. Creating data files

Section V. Descriptive Statistics (Note: *indicates the measure that you need to know how to calculate) (Chapters 17 & 20)

- Descriptive: To describe characteristics of the population or sample, such as central tendency, distribution, and variability.

- Inferential: To make inferences about a whole population from a sample.

1. Descriptive vs. inferential statistics

2. Sample statistics (measures computed from sample) vs. population parameters (measures from a whole population)

3. Frequency distribution: A set of data organized by summarizing the number of times a particular value of a variable occurs

4. Measures of central tendency: mean (Average)*, median (the value below which half the values in a dist ribution fall)*, mode (the value that occurs more oft en)*

Skinny vs. fat dispersion: the more skinny the dispersion, the more accurate.

a. What does dispersion mean? It describes how far away individual responses are from the center.

Range: the distance between the smallest and the largest values of a frequency distribution.

Deviation: calculates how far any observation is from the mean. Variance: Its square root is the standard deviation.

Standard deviation: The average of the amount of variance for a distribution.

b. Measures:

5. Measures of dispersion:

6. Normal distribution: characteristics

7. Cross-tabulation: Many of your research questions or hypotheses refer to comparing frequencies among different group.

8. Comparing mean differences: When the variables that you look at between groups are at interval/ratio level.

9. Being able to read SPSS output, such as descriptive statistics tables, frequency tables and histogram and choose the most appropriate statistics to report (see example 1

Section VI. Communicating research results (Chapter 25)

1. Structure of basic marketing research report

a. Introduction section

b. Research methodology section

i. Use charts effectively (i.e., pie charts, bar charts, histogram)

ii. Report appropriate statistics

c. Results section

2. What content should be covered in each section:



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1. When should we report means and standard deviations?

2. When should we report frequencies and percentages?

a. When should we use cross-tabulation?

b. When should we report mean differences?

3. Comparison

d. Conclusions and recommendation section


mmc 4609 - communication research strategy - exam 2 review

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