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UNI > MCM-140 FINALS > Flashcards

MCM-140 FINALS Flashcards

1
Q

What are the Main features of Longitudinal Design Study?

A
  1. Covers a single sample over a long period of time (years/decades).
  2. DIACHRONIC ANALYSIS: Enables comparison of the changes of phenomena, data, individuals over time.
  3. Establishes a prerequisite for micro-level analysis.
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2
Q

Which procedure is efficient for examining long-term relationships between variables? Why?

A

Longitudinal surveys are efficient because they permit the collection of responses over time.

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3
Q

What is the nature of the longitudinal design study?

A

It is generally observational, collecting quantitative and/or qualitative data on outcomes without any external influence.

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4
Q

Which samples are often studied using Longitudinal design study to evaluate the relationships?

A
  1. Risk factors & Developments of disease
  2. Outcome of treatments over time.
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5
Q

Why is Statistical Testing appropriate for Longitudinal Study?

A

Since data is collected from individuals within a predefined group, statistical testing can be used to analyze changes over time, either for the entire group or for specific individuals.

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6
Q

What is cross-sectional analysis?

A

It examines multiple variables at a single point in time. A “snapshot” of data which allows researchers to identify associations and patterns but not changes over time.

Because it captures data at only one moment, it CANNOT determine cause-and-effect relationships.

This method is typically quicker & less expensive compared to longitudinal studies.

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7
Q

Types of Longitudinal Study Designs

A
  1. Repeated cross-sectional studies
  2. Retrospective studies
  3. Prospective studies
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8
Q

What is repeated cross-sectional studies?

A

A study where participants are different on every sampling occasion.

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9
Q

What is retrospective studies?

A

A study done after some participants have already experienced relevant events. Data about their past exposures is collected and analyzed afterward.

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10
Q

What is prospective studies?

A

A study where SAME participants are followed over time.

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11
Q

Types of Panels

A
  1. Cohort panels
  2. Representative panels
  3. Linked panels
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12
Q

Characteristics of Trend/Cohort Analysis

A

Technique used to monitor changes in people’s attitudes and behavior, associating it with maturation.

However, because the exact same people aren’t always surveyed each time (different samples from the same group are used), any changes seen might be due to differences between the groups being surveyed, not just age or maturation differences.

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13
Q

Characteristics of Panel studies

A

It involves data collection over time from SAME sample of respondents. Panel studies has to be based on original data collection.

Sample = Panel

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14
Q

Problem of Panel Studies

A

Attrition: Loss of panel members (participants) over time. It causes the panel to gradually decrease in size as study progresses.

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15
Q

What is a key limitation of trend and cohort studies compared to other longitudinal studies?

A

Different people are surveyed each time, making it impossible to track changes in attitudes or behavior for specific individuals.

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16
Q

Main strengths of Longitudinal Study

A
  1. Examines cause-and-effect relationships, allowing to draw conclusions.
  2. Tracks Growth
  3. Distinguishes real trends from random events.
  4. Micro-Level Data analysis
  5. Reduces Sampling Error: Consistent sample over time (Economical).
  6. Reliable Recommendations: Supports evidence-based interventions.
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17
Q

5 Advantages of Longitudinal Cohort Studies

A
  1. Identify & relate events to specific exposures, including their presence, timing & duration.
  2. Establish the sequence of events.
  3. Track individual changes over time
  4. Excludes recall bias by collecting data beforehand
  5. Corrects for the “cohort effect” by analyzing the impact of individual time components: cohort (birth dates), period (current time), & age (at the point of measurement).
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18
Q

5 Disadvantages of Longitudinal Cohort Studies

A
  1. Interrupted follow-up & Attrition with loss to follow-up participants.
  2. Difficulty in separating the reciprocal impact of exposure & outcome.
  3. Inaccurate conclusions if the statistical techniques don’t consider how measurements are related for the same person over time.
  4. High-cost & time-consuming.
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19
Q

Advantages of Longitudinal Over repeated Cross-sectional Data

A

◦ Better insight into causality
◦ Can track processes
◦ Links prior intentions to observed behavior
◦ Shows patterns & changes over time
◦ Controls for hidden differences (unobserved heterogeneity)

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20
Q

Disadvantages of Longitudinal over repeated cross-sectional data

A

◦ High-cost & difficult to execute
◦ Less representative than contemporary samples; poorer population estimates.

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21
Q

Advantages & Disadvantages of Panel Data

A

ADVANTAGES:
1. Regularly repeated interviews: relatively accurate fresh data — Broad and detailed data.

DISADVANTAGES:
1. Short span: It takes time before there are long panel runs
2. Incomplete histories
3. Panel conditioning
4. Attrition

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22
Q

Observational vs Experimental Research

A

Observational research is a systematic method of directly observing patterns, events, or behaviors in their natural environment without interacting with people involved. It tends to be less reliable in terms of consistency but more accurate.

Experimental research, where a controlled & semi-artificial environment is created to eliminate external factors, and at least one variable is manipulated as part of the experiment.

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23
Q

Conditions for using Observational Research

A

Information collected must be observable or inferred, the behavior should be frequent, repetitive, or predictable, and it must be of short duration.

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24
Q

Advantages of Observational Research

A
  1. Flexibility – Research can adjust their approach.
  2. Direct measure of behaviors rather than relying on reports/intentions
  3. Ideal for nonverbal communication-based situations
  4. Observational data can be executed quickly & accurate.
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25
Q

Disadvantages of Observational Research

A
  1. Limitation to behavioral variables – May not provide the full picture since it cannot measure attitudes, beliefs, or emotions.
  2. Prone to researcher bias & relies on subjective interpretation.
  3. Often involves small, non-representative samples, and data analysis tends to be more qualitative than quantitative.
  4. Time-consuming, costly & challenging if participants are not readily available.
  5. Data can be time-sensitive, making predictive analysis difficult.
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26
Q

Observational Techniques in Marketing Research

A
  1. Personal Observation
  2. Mechanical Observation
  3. Audits
  4. Trace Analysis
  5. Content Analysis
  6. Physiological Measurement

P-M-A-T-C-P

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27
Q

3 Types of Observational Research

A
  1. Covert observational research - Researchers do not identify themselves.
  2. Overt observational research - Researchers identify themselves as researchers & explain their purpose.
  3. Researcher Participation - Participates in research to further understand the study.
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28
Q

What is Meta-analysis study?

A

It combines the results from multiple separate studies to increase validity & reliability (over individual studies), improve estimates of the size of the effect, and/or resolve uncertainty when reports & research studies disagree.

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29
Q

What is the principle of meta-analysis study?

A

It produces a weighted average of the included study results.

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30
Q

Main Media Research Metrics

A
  1. Rating
  2. Share
  3. CPM
  4. Reach
  5. Frequency
  6. Gross Rating Points

R-S-C-R-F-G

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31
Q

What is Rating?

A

Estimate of number of people or households are watching/listening to a program, based on some universal estimate.

This estimate could be the entire national audience (like for a network or cable channel) or a sample of people in a local market.

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32
Q

What are the Rating Formulae?

A

Audience (Individuals or Households) / Universal Estimate = RATING (%)

Number of Viewers or Listeners / Total Viewers or Listener Population = RATING (%)

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33
Q

What is Share?

A

Share measures the percentage of viewing audience, based on actual number of viewers such as Households Using Television [HUT] or listeners (Persons Using Radio [PUR]) at a given time.

Shares show how a program/station is performing compared to other competitors.

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34
Q

What is the Share formula?

A

Audience (Individuals or Households) / HUT or PUR = Share (%)

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35
Q

RATING VS SHARE: Size and Scope

A

Ratings are smaller than shares because ratings consider the entire population with sets on/off. Shares consider only the actual households or individuals using a medium.

With that – total share adds up to 100% of available audience while ratings will not.

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36
Q

What is CPM?

A

It is a pricing model that describes the cost to reach 1,000 people. It helps compare competitors, media types, and time periods.

CPM captures exposures over clicks. It works well for big publishers because advertisers pay a fixed price based on impressions an ad receives.

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37
Q

How to measure CPM campaign’s efficiency?

A

Look at the click-through rate (CTR) which shows the ratio of people who click the ad compared to overall impressions.

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38
Q

What is the formula for CPM?

A

Total Campaign Spend ÷ Number of Impressions × 1,000 = CPM

39
Q

Distinctive Features of CPM

A
  • CPM tracks how often an ad is viewed.
  • CPM increases brand exposure
  • CPM can be cost-effective if brands want a large audience to view their ads.
  • CPM is customizable in how the ad is shown.
40
Q

What is Reach?

A

It is a metric that measures to the total number/percentage of people or households expose who see content at least once within a period.

Reach does not measure impressions; it only shows that someone was near the message, not their engagement.

41
Q

What are the Media with largest number of global users?

A

Facebook, WhatsApp, YouTube, Instagram & WeChat

42
Q

What is the formula for Reach?

A

Reach = Gross rating points (GRPs) ÷ Average frequency

43
Q

What are the Types of REACH?

A
  1. Organic Reach
  2. Paid Reach
  3. Viral Reach
44
Q

Organic Reach

A

Number of users that have seen the post on their feed.

45
Q

Paid Reach

A

Number of times that distinct users have come across sponsored/paid ads, or content.

46
Q

Viral Reach

A

Number of users viewed on posts that have been commented or shared by other users on social media

47
Q

What is Frequency?

A

It refers to the number of times a person/ household is exposed to an ad.

OR: Number of times an ad is repeated through a specific
medium over a specific time

48
Q

What does a low & high frequency indicate?

A

LOW - Wider audience is reached.

HIGH - Narrower audience is reached, but increases how often they see the campaign.

49
Q

What are the formulae for Frequency?

A

Frequency = Impressions ÷ Unique Users

Frequency = Gross rating points (GRPs) ÷ Reach Percentage

50
Q

What are the Main Rationales for using Frequency?

A
  • To increase consumer loyalty
  • To establish a more effective brand
  • To increase market share
51
Q

What is GRPs?

A

Sum of all rating points generated in an advertiser’s schedule.

It measures advertising impact.

It is used in radio & television.

52
Q

What is the formula for GRPs?

A

GRPs = Reach (%) × Average frequency

53
Q

What is Coding in Research Data Design Methodology?

A

It is a process of assigning codes, words, phrases of parts of data to identify topics, then organizing the data for easier analysis.

54
Q

6 Characteristics of a Coding Pattern

A
  1. Similarity
  2. Difference
  3. Frequency
  4. Sequence
  5. Correspondence
  6. Causation
55
Q

5 Coding Factors

A
  1. Developing a preliminary coding scheme
  2. Testing & refining the coding scheme
  3. Testing inter-coder reliability
  4. Coding
  5. Reviewing data by code
56
Q

Developing a Preliminary Coding Scheme

A

This involves making a list of codes (words or phrases) to categorize data by topic. Researchers focus on the questions they want to answer & the topics related to those questions. Meetings help identify key issues & decide which codes are needed. A coding dictionary is also created at this stage.

57
Q

Testing & Refining Coding scheme

A

This involves a small group of researchers coding a subset of qualitative data using the preliminary scheme. Afterward, they meet to discuss their coding process.

This helps identify new issues and refine existing codes to ensure they are distinct from each other.

58
Q

Testing inter-coder reliability

A

It ensures that multiple researchers are coding the data in the same way. After refining the coding scheme, researchers code another small sample of data. They then check how consistent their coding is. Using software, they calculate the kappa coefficient, a statistic that shows the coding was done the same way by chance to identify inconsistent codes. Researchers meet to refine the definitions of those codes & repeat the process until confident.

59
Q

First set of Distinctions regarding the data-driven development of codes

A

TWO Versions:

  1. First-cycle coding happens early in research and helps reduce data for analysis, while second-cycle coding comes later and focuses on analyzing the data.
  2. Comparing Rough, Analytic & Focused Coding: Rough coding is used for data reduction, where simple labels are added to identify key points. Analytic coding is more detailed and helps develop themes and findings. Focused coding involves revisiting the data to refine your approach around specific ideas of interest.
60
Q

Second set of distinctions regarding the data-driven development of codes

A

This distinction covers whether the codes are developed based on the data or created in advance.

  • When codes are predetermined, based on a theory or hypothesis, it is called deductive/closed coding.
  • In contrast, open/inductive coding involves developing codes from what is observed in the data itself. Open coding is more common in qualitative research.

In both approaches, researchers may also use theoretical memos to explore the connections between ideas, theory, and data.

61
Q

Third set of distinctions regarding the data-driven development of codes

A

This set focuses on what is coded.

Manifest coding focuses on surface-level, easy-to-see details in texts.
Latent coding involves interpreting deeper meanings or symbols within the texts.

62
Q

First Cycle Coding

A

It breaks down qualitative data into discrete parts so it can be further examined and compared.

It covers Grammatical, Elemental, Affective, Literary & Language Methods, Exploratory Methods, and Procedural Methods.

63
Q

Second Cycle Coding

A

It involves reorganizing & condensing initial analytic details into a study structure that allows researchers to refine codes & categories, developing themes and align data with research and theory.

64
Q

What are the 6 Approaches to Second Cycle coding?

A

Pattern coding, Focused coding, Axial coding, Theoretical coding, Elaborative coding & Longitudinal coding

65
Q

Organizing & Categorizing Codes

A

Researcher organizes codes to create larger categories. It can be done through highlighting, post-it notes, visual mapping, and more.

66
Q

Code Mapping & Code Landscaping

A

Code Mapping is categorizing & organizing the codes.

Code Landscaping is visually presenting these codes.

67
Q

Codebook Qualitative Research

A

A systematic method for classifying & interpreting qualitative data. It involves a set of guidelines
outlining the process of organizing & classifying qualitative data themes, patterns, and concepts.

68
Q

Importance of Codebook Qualitative Research

A

It helps researchers stay focused on their initial coding ideas & ensure consistency in research study analysis.

69
Q

Testing the Reliability of the Code

A
  1. Conduct pilot coding
  2. Evaluate intercoder reliability through Cohen’s kappa or percentage agreement
  3. Address differences
  4. Refine codebook as necessary based on feedback & insights from pilot coding.
70
Q

4 Basic Steps to Meta-Analysis Research Methodology

A
  1. Identification.
  2. Selection.
  3. Abstraction.
  4. Analysis.

I-S-A-A

71
Q

What is Meta-analysis?

A

Meta-analysis is a research method that combines results from multiple similar studies into one overall analysis. It aims to increase accuracy, reliability, and clarity by pooling data to identify common trends or resolve disagreements between studies. Essentially, it’s like “researching previous research.”

72
Q

What is the significance of Meta-analysis?

A

Meta-analysis is a powerful tool in research because it helps summarize a large body of evidence on a research field. It’s fundamental for evidence-based decision-making and is often part of systematic reviews, providing a comprehensive understanding of a research area.

73
Q

What are the advantages of Meta-analysis?

A
  1. Generalizes results to a larger population.
  2. Improves accuracy by combining more data.
  3. Increases statistical power to detect effects.
  4. Quantifies inconsistencies between studies & explains their sources.
  5. Allows hypothesis testing on summary estimates.
  6. Identifies trends or patterns across studies.
  7. Detects publication bias.
  8. Resolves conflicting findings & generates new research questions.
74
Q

What is Grounded theory?

A

It is a qualitative research method that develops a theory directly from the data collected. Introduced by Barney Glaser and Anselm Strauss in the 1960s, it focuses on understanding human behavior & social interactions by building theories grounded in real-world data, rather than starting with a hypothesis. Researchers analyze data through coding to identify patterns & concepts, which are then used to develop a theory.

75
Q

Three Major Paradigmatic Schools of Grounded Theory

A
  1. Glaserian Grounded Theory
  2. Straussian Grounded Theory
  3. Constructivist Grounded Theory
76
Q

What is Triangulation?

A

It is a research method that uses multiple approaches—such as different data sources, methods, theories, or researchers—to study a single research question.

The goal is to increase the accuracy, validity & credibility of research findings by cross-verifying data. It helps reduce bias & offers a more complete understanding of the phenomenon being studied.

77
Q

What is the significance of Triangulation?

A

It enhances the trustworthiness of research. By combining different methods or perspectives, researchers can overcome the limitations of using just one approach. It provides a richer, in-depth & more balanced view of the topic and helps identify patterns or inconsistencies in the data.

78
Q

Types of Methodological Triangulation

A
  1. Across Method use quantitative & qualitative techniques to collect data.
  2. Within Method use multiple data collection techniques of same type (either quantitative or qualitative).
79
Q

5 Types of Triangulation

A
  1. Data Triangulation
  2. Methodological Triangulation
  3. Theoretical Triangulation
  4. Investigator Triangulation
  5. Time Triangulation
80
Q

Data Triangulation

A

Use of multiple sources of DATA (surveys, interviews, observations, etc.) to examine a research question/phenomenon.

81
Q

Methodological Triangulation

A

Use of multiple research QUALITATIVE and/or QUANTITATIVE methods to examine a research question or phenomenon.

– Most common type of Triangulation.

82
Q

Theoretical Triangulation

A

Use of multiple theoretical frameworks/models to analyze & interpret research findings.

83
Q

Investigator Triangulation

A

Use of multiple researchers to examine a research question/phenomenon.

84
Q

Time Triangulation

A

Studying same research question/phenomenon at different time points.

85
Q

When to use Triangulation?

A

When the research is…
1. Complex
2. Exploratory
3. Sensitive
4. Interdisciplinary
5. Longitudinal

86
Q

Advantages of Triangulation

A
  1. Increases Validity: Strengthens credibility of findings.
  2. Reduces Bias
  3. Enhances Data Quality: Provides more complete and detailed data.
  4. Comprehensive Understanding
  5. Confirms Findings: Cross-checks data for consistency, making conclusions more reliable.
87
Q

Disadvantages of Triangulation

A
  1. Resource-intensive
  2. Increased Complexity
  3. Difficulty in Data Comparison
  4. Data inconsistencies
88
Q

Common Triangulation Data Collection Methods

A

Interviews, Observations, Surveys, Document Analysis & Focus Groups

89
Q

Common Data Analysis methods used in Triangulation

A
  1. Comparative Analysis
  2. Convergent Validation
  3. Divergent Validation
  4. Complementary Analysis
  5. Triangulated Verification
  6. Meta-triangulation
  7. Member Checking
  8. Peer review
  9. Triangulated Coding
  10. Inter-rater Reliability
90
Q

Examples of Triangulation

A

Mixed-methods Research, Market research and Educational Research

91
Q

How to Conduct Triangulation?

A
  1. Define the Research Question
  2. Select Data Sources (e.g.: surveys, interviews, observations, or existing datasets)
  3. Choose Data Collection Methods
  4. Collect Data: Gather data using the chosen methods & document the process
  5. Analyze Data: Use suitable analysis methods
  6. Compare Findings
  7. Synthesize Findings: Combine data insights to draw conclusions.
  8. Evaluate & Report
92
Q

Advantages of Grounded Theory

A
  1. Generates new theories directly from data.
  2. Provides a systematic approach for analyzing complex phenomena.
  3. Flexible & adaptable to real-world settings.
  4. Encourages deep understanding of participants’ experiences.
93
Q

Disadvantages of Grounded Theory

A
  1. Time-consuming & resource-intensive.
  2. Theories can lack generalizability.
  3. Relies heavily on the researcher’s interpretation, risking bias.
  4. Results are less focused on measurable outcomes compared to hypothesis-driven research.

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