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APRD 2004 > EXAM 2 > Flashcards

EXAM 2 Flashcards

1
Q

Qualitative Research

A

Research that seeks to gain insight and depth on a topic
- Evaluates, theoretical, interprets

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2
Q

Quantitative Research

A

Research based on the systematic calculation of data
- Counts/measures, statistical, processes data

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3
Q

Exploratory Research

A

investigating, exploring, or attempting to figure out a new, innovative thread of knowledge (can be both qualitative and quantitative)
- Poll aggregation websites

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4
Q

Descriptive Research

A

Allows researchers to focus on describing a phenomenon or understanding the details about people’s experiences (generally qualitative)
- Native Advertising

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5
Q

Explanatory Research

A

Focuses on explaining the reasons behind a phenomenon, relationship, or event (can be qualitative and quantitative)
- Influence of age on e-commerce site users

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6
Q

Cross-Sectional Research

A

Data is collected only once; a snapshot of data collected at one point in time

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7
Q

Advantages of Cross-Sectional Research

A

Convenient, inexpensive and quick

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8
Q

Disadvantages of Cross-Sectional Research

A

Prone to various types of error

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9
Q

Longitudinal Research

A

Data is collected multiple times; helps the data to be more accurate and avoid or minimize errors like inaccurate responses

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10
Q

Panel Designs (Type of Longitudinal Research)

A

Data is collected from the same people at multiple collection points
- Aggressive thoughts at 10 years old, 15 years old, and 20 years old

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11
Q

Trend Studies (Type of Longitudinal Research)

A

Data is collected from different people (all drawn from the same population) at multiple collection points
- Registered voters’ approval of the president at Y1, Y2, Y3, and Y4 of the presidential term

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12
Q

Advantages of Longitudinal Research

A

Helps address error found in cross-sectional research, flexible, can help researchers identify time based trends

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13
Q

Disadvantages of Longitudinal Research

A

Expensive, Time consuming, data can be difficult to interpret

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14
Q

Variable

A

Something that varies
- APRD 2004 student age

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15
Q

Constant

A

Something that is fixed/does not change
- APRD 2004 students’ status as CU enrolee

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16
Q

Quantitative measurement

A

The use of numbers to describe a property of an object or an event

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17
Q

Measurement

A

The use of numbers to describe something that happens (or does not happen) in the world

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18
Q

Examples of “measured” things

A

Lecture attendance
Temperature
Clicks on a website
Product units sold
Brand reputation perceptions

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19
Q

Examples of measurement acts

A

Taking a baby’s temperature
Asking question on a survey
Counting the number of likes on a tweet
Counting the number of candy bars sold in a single day
Asking employees to rate their job satisfaction

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20
Q

4 Levels of measuring Variables

A

Nominal
Ordinal
Interval
Ratio

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21
Q

Nominal Variables

A

1/4 Levels of measuring Variables
“categorical” variables - numbers serve as tags or labels; numbers are NOT placed on a meaningful scale; membership is both all inclusive and mutually exclusive
EX: Biological Sex [1=Male, 0=Female]

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22
Q

Ordinal Variables

A

2/4 Levels of measuring Variables
Possible values are meaningfully ordered; they do not establish the numeric difference between data points- they indicate only that one data point us ranked higher or lower than another.
EX: a student may be asked to rate the teaching effectiveness of a college professor as excellent (5), good (4), average (3), poor (2), or unsatisfactory (1).

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23
Q

Interval Variables

A

3/4 Levels of measuring Variables
“integer-level data”” -is measured along a scale in which each position is equidistant from the other scale points; measurement intervals are equally spaced
EX: Temperature: 81 degrees Fahrenheit is exactly 1 degree Fahrenheit greater than 80 degree Fahrenheit

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24
Q

Ratio Variables

A

4/4 Levels of measuring Variables
Ratio variables are interval variables with a natural zero point; a natural zero point simply means that zero means “none of something”
EX: Advertisement clicks
A banner advertisement can receive 0 clicks
A banner advertisement can receive 5 million clicks

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25
Q

Measurement Error

A

When the data we collect does not represent reality- is always present at some degree

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26
Q

Random Measurement Errors

A

Measurement errors that are small, non-systematic (i.e., there is no discernable pattern), and do not threaten the overall validity of our data
EX: A small number of survey participants misread a survey question

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27
Q

Systematic Measurement Errors

A

An error in measurement in which the tool does not accurately measure the concept and is perceived incorrectly by most or all of the participants; not a big deal
EX: A question on a survey is very confusing, causing most/all participants to answer it in an incorrect manner

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28
Q

Reliability

A

Pertains to a measurement approach’s ability to yield consistent results
Reliability refers to the level of clarity in the tool; Reliability is the consistency in our measurement.

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29
Q

Validity

A

Refers to a measurement approach’s ability to measure what it is supposed to
The ability or the potential of our data collection tool to capture and measure the construct or the phenomenon that we are interested in measuring; Are our questions/tests/other measures reflecting the real meaning of the concept under consideration?

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30
Q

Sampling - Population

A

the entire group of people that are the focus of a study

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31
Q

Sampling - Sample

A

A subset of the population; a small part of the population Ideally is a representative of all the characteristics of a population

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32
Q

Why do we sample?

A

It is often impossible or counterproductive to collect data from all members of the population

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33
Q

Census

A

an official count or survey of a population, typically recording various details of individuals

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34
Q

2 Types of sampling

A

Probability Sample
Non-Probability Sample

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35
Q

Probability Sampling

A

Every element of the population has a known (though not necessarily equal) chance of being selected for inclusion

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36
Q

Types of Probability Sampling

A

Simple Random Sampling
Stratified Random Sampling
Disproportionate Random Sampling

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37
Q

Simple Random Sampling

A

TYPE OF PROBABILITY SAMPLING
All members of a population have an equal chance of being selected for the sample; members of a population are selected at random for inclusion in the sample

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38
Q

Stratified Random Sampling

A

TYPE OF PROBABILITY SAMPLING
A population is divided into subgroups (or strata); a random sample is subsequently drawn from each strata
EX: A population has 3 strata of interest:
S1=5,000
S2=3,000
S3=2,000
we would select:
Sample S1= 50
Sample S2=30
Sample S1= 20

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39
Q

Disproportionate Random Sampling

A

TYPE OF PROBABILITY SAMPLING
Like proportional random sampling but sample portions are not equivalent to the population proportion

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40
Q

Non-Probability Sampling

A

Not all elements (ie. people) of a population have an opportunity to be included in the sample; Does not allow us to make inferences about a population!!!

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41
Q

Types of Non-Probability Sampling

A

Convenience Sampling
Snowball sampling
Purposive Sampling
Quota Sampling

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42
Q

Convenience Sampling

A

TYPE OF NON-PROBABILITY SAMPLING
Sample is drawn from those that are available or easy to collect data from

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43
Q

Snowball sampling

A

TYPE OF NON-PROBABILITY SAMPLING
Generate a convenience sample of respondents and ask sampled respondents to recommend others who might be interested in providing data

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44
Q

Purposive Sampling

A

TYPE OF NON-PROBABILITY SAMPLING
Researchers purposefully select from a group of people of theoretical interest:
Experts
Extreme cases
Typical cases

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45
Q

Quota Sampling

A

TYPE OF NON-PROBABILITY SAMPLING
Generation of a sample that has attributes proportional to a given population
Ex: we know that users of an internet platform are:
45% Caucasian (incl. Hispanic/Latinx)
25% Asian-American
20% African-American
10% Other Race
Using these attributes, we can use convenience sampling techniques to construct a sample with proportional race attributes

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46
Q

Inference

A

a conclusion that is formed because of known facts or evidence

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47
Q

Are convenience samples okay?

A

Clearly, they restrict the ability to make population-level inferences
At the same time, they are time and cost efficient
Not all convenience samples are created equal

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48
Q

Surveys

A

Collection of data from a sample of elements (e.g., adult women) drawn from a well-defined population (e.g., all adult women living in the United States) through the use of a questionnaire

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49
Q

Self-report data

A

SURVEYS RELY ON THIS!!
Data provided by a study respondent without interference on the part of the researcher
Respondents tell the researcher what they think, how they feel, and how they behave

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50
Q

The process of conducting a survey:

A
  1. Specify the research problem
  2. Select a survey design
  3. Select a sampling strategy
  4. Generate questionnaire
  5. Generate data
  6. Analyze data
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51
Q

Berger says an experiment does 3 things

A

Demonstrates whether something is true
Examines the validity of a hypothesis or theory
Attempts to discover new information

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52
Q

Hypothesis

A

Educated guess about the way things will be

53
Q

Casual relationship between variables

A

Refers to the researchers attempts to determine that one or more variables (the IVs) have caused the changes in another variable (the DV)

54
Q

3 Conditions for one variable to cause an effect on another variable

A

Correlation
Time Order
Non-Spuriousness

55
Q

Correlation

A

1/3 Conditions for one variable to cause an effect on another variable
Variables A and B are related to one another

56
Q

Time Order

A

2/3 Conditions for one variable to cause an effect on another variable
Changes to Variable A result in changes to Variable B

57
Q

Non-Spuriousness

A

3/3 Conditions for one variable to cause an effect on another variable
The relationship between Variable A and Variable B must not be explained by a third variable (Variable C)

58
Q

Spuriousness

A

A relationship between variables that seems real, but is in fact explained by the presence of another variable
EX:
A seaside resort town observes that ice cream sales (Variable A) are positively associated with drowning deaths (Variable B)
More ice cream sales = more drowning deaths
Do ice cream sales cause drowning deaths?
No! A third variable (Variable C) explains the relationship between Variables A and B
Variable C = number of tourists

59
Q

Causation

A

States that A causes B

60
Q

Association

A

States that A and B are associated (correlated) with one another

61
Q

Independent Variable

A

Independent variables are varied by researchers

62
Q

Dependent Variable

A

Dependent variables are presumed to be affected by independent variables

63
Q

Random Assignment

A

ALL TRUE EXPERIMENTS REQUIRE THIS!!
research subjects are randomly placed in experimental groups

64
Q

Quasi-experiments

A

Does not use random assignment; cannot provide conclusive evidence of causation

65
Q

Random Sampling

A

Random selection of individuals from a larger population

66
Q

Control Group

A

A group of participants who do everything the experimental group members do, but are not given any test, drug, intervention, or manipulation

67
Q

Treatment/Experimental Group

A

The group of participants who undergo a form of experimentation, such as training, taking a test or drug, or another type of intervention

68
Q

Strengths of Experimental Research

A

Only method that can definitively show causality
Can be replicated (i.e., repeated by other researchers)

69
Q

Weaknesses of Experimental Research

A

Often, the study context is artificial (i.e., study scenario doesn’t translate to the real world)
Cross-sectional designs don’t speak to long-term effects
In some research scenarios, experiments can raise ethical questions

70
Q

Pretest

A

Measurement(s) taken before delivery of the experimental (or manipulated) stimuli

71
Q

Posttest

A

Measurement(s) taken after delivery of the experimental (or manipulated) stimuli

72
Q

The Solomon four-group experimental design

A

protects the study from the biases of pretest because it uses two experimental groups and two control groups
-> only one experimental and one control group take the pretest; The differences in results can clearly show whether the pretest is influencing the results of the study

73
Q

Factorial Design

A

Factorial experiments involve the manipulation of more than one independent variable

74
Q

A/B Testing

A

Away to compare two versions of something to figure out which performs better; When conducting A/B tests there should (generally speaking) only be 1 thing different across versions A and B

75
Q

Types of questions appropriate

A

Does changing the location of a design element increase website clicks?
Does changing our website font increase time on page?
Does changing the color of a design element increase clicks?
Does adding an interactive element decrease bounce rate?

76
Q

Some common A/B metrics (or measures) include

A

Clickthrough rate
Number of clicks/number of visitors
Time on page
Time spent on a given webpage
Bounce rate
Number of people who don’t click on page/number of visitors

77
Q

Why is A/B testing essentially a RCT?

A

Version A (old/current version) is the control stimuli
Version B (new/proposed version) is the manipulated stimuli

78
Q

Content Analysis

A

systematic review of media materials (TV shows, movies, magazine ads, journal articles, etc) for patterns

79
Q

3 components of content analyses

A

Objective
Systematic (quantitative)
Focused on manifest content

80
Q

Manifest Content

A

Content that is observable (not inferred or assumed)

81
Q

Measurable scoring units

A

figuring out your basic or standard unit of measurement

82
Q

What are measurable scoring units?

A

Words
Phrases
Minutes
Images
Entire documents (newspaper articles, TV commercials, TV show episodes, social media posts, etc.)

83
Q

Content analysis - sampling

A

Content analysis often require us to sample from a populations → it would be very difficult to assess every news article published in the last 5 years

84
Q

Constitutive Definitions

A

Definitions you find in dictionaries are known as constitutive definitions

85
Q

Operational Definitions

A

Tells how you will measure something and forces you to explain how you understand or interpret a concept - it tells us HOW we will measure something

86
Q

Codebook

A

researchers use operational definitions to generate a code book- It is the guide that coders use to code media content - like a set of instructions

87
Q

Qualitative Content Analysis

A

require the use of at least two coders

88
Q

Intercoder reliability

A

the extent to which two or more independent coders agree on the coding of the content of interest with an application of the same coding scheme

89
Q

Measurement

A

The most straightforward means of coding content involves assessing the degree to which something is present or absent

90
Q

Advantages of content analysis research

A

Unobtrusive
Relatively inexpensive
Deals with current events and topics of present-day interest
Uses material that is relatively easy to obtain and work with
Yields data that can be quantified

91
Q

Disadvantages of content analysis research

A

Finding a representative sample can be difficult
Obtaining reliability in coding can be difficult
Defining terms operationally can be difficult

92
Q

Content analysis research process:

A

Select a topic
Identify scoring units
Create a sampling plan/sample
Create operational definitions
Assess inter-coder reliability
Code entire sample

93
Q

Big Data

A

no single or universally agreed upon definition

94
Q

“big data” is defined by

A

Volume: big data is large
Velocity: big data occurs at an unprecedented speed
Variety: big data comes in multiple formats/takes on multiple forms

95
Q

Big Data in Strat Comm can tell us things like

A

How are people talking about us online?
What types of messages are most likely to incur brand-level engagement?
What sorts of needs do our consumers have?
How can we personalize advertising experiences for users?

96
Q

Analytics/performance metrics

A

Organizations can gather larger quantities of data from websites and social media platforms for the purposes of metric generation

97
Q

Trace Data

A

refers to the traces/footprints that we leave when we use the Internet

98
Q

Common Metrics in Big Data usage

A

Engagement: were you engrossed in the product
Click-through rate
Conversions: when we can follow you from clicking in the ad all the way to purchasing a product
Followers/fans
Leads
Reach
Loyalty

99
Q

Social Listening

A

Organizations can use big data to monitor how people are talking about them online

100
Q

Personalization

A

Organizations can use big data to create personalized media experiences; survey users behavior and make inferences about that type of person

101
Q

When to use a hypothesis

A

When we are testing the relationship between two or more variables; and
When we have an educated/informed guess as to what is likely to occur
Used in explanatory research - when we are trying to explain/describe something

102
Q

Research question

A

A question around which research activities are organized
–> The knowledge we have isn’t enough for us to create a prediction of what could occur

102
Q

Research question

A

A question around which research activities are organized
–> The knowledge we have isn’t enough for us to create a prediction of what could occur

103
Q

When to use research questions

A

When we’re exploring a new area and/or aren’t clear about the relationship(s) between the variables in our study

104
Q

survey

A

refers to the method of data collection

105
Q

questionnaire

A

the instrument containing the questions

106
Q

Variables of primary interest

A

Variables you are explicitly interested in learning more about

107
Q

Control and descriptive variables

A

v​​ariables whose collection gives the researcher the ability to describe what the sample looks like in terms of demographics and to address spuriousness

108
Q

Spuriousness

A

a relationship between variables that seems real, but is in fact explained by the presence of another variable

109
Q

confounding variables

A

a third variable that influences both the independent and dependent variables

110
Q

How to write a good questionnaire

A

Develop a thorough list of variables you want to collect
Think about the best way(s) to measure each variable
Think of all possible answers to each question
Avoid biased, misleading, socially desirable, or double-barreled questions and overly technical terms
Pay attention to contingency questions
Think about the presentation order of your questions
Use clarity and brevity
Think long and hard about scaling/measurement level
Pre-test!

111
Q

Ways of deploying questionnaires:

A

In-person
Telephonically
Manual
Computer-assisted
Online
Each of the above methods (potentially) incur different types of bias

112
Q

Tabular format

A

A method of post collection
Data is organized using columns and rows -> each column represents a variable while each row represents a unique respondent or media artifact

113
Q

Things we can do with missing data

A

Listwise Deletion
Parawise Deletion
Imputation

114
Q

Listwise Deletion

A

ONE OF THE THINGS WE CAN DO WITH MISSING DATA
Delete all cases with one or more bit of data missing listwise; delete everyone who is missing one or more response

115
Q

Parawise Deletion

A

ONE OF THE THINGS WE CAN DO WITH MISSING DATA
Exclude cases with missing data on a variable-by-variable basis

116
Q

Imputation

A

ONE OF THE THINGS WE CAN DO WITH MISSING DATA
Replace missing data with an educated guess of how the respondent is likely to have answered

117
Q

Statistical Analysis

A

Once the data has been inspected, the next step is to conduct statistical analyses

118
Q

the 2 general types of statistics

A

descriptive and inferential

119
Q

Descriptive statistics

A

information that characterizes or summarizes the whole set of data

120
Q

Important descriptive statistics

A

Frequency distributions
measures of central tendency (mean, median, mode)
measures of dispersion (range, standard deviation)

121
Q

Frequency distributions

A

descriptive statistics
describe how frequencies are distributed over values
EX: frequency table (what we did in lab)
Frequencies shown using a bar chart are called histograms

122
Q

Measures of central tendency

A

descriptive statistics
Statistical indices that quantify the typical or central value in a distribution
EX: Mean, median and mode

123
Q

measures of dispertion

A

descriptive statistics
the measure of the spread of scores in a data set
EX: range (lowest and highest observed scores) and standard deviation

124
Q

standard deviation

A

a measure of variability indicating the degree to which all observed values deviate from the mean

125
Q

Covariation

A

tool used to determine the relationship between the movements of two random variables –> most basic one is the correlation coefficient

126
Q

Correlation Coefficients

A

the specific measure that quantifies the strength of the linear relationship between two variables in a correlation analysis

127
Q

Inferential statistics

A

allow us to generalize from the data collected to the general populations they were taken from–> trying to reach conclusions that extend beyond the immediate data alone

APRD 2004 (3 decks)

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