Class 1: Research Quality
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Questions and Answers

What are the five requirements of good research when conducting desk research?

Objective & Independent, Controllable, Reliability, Validity, Generalizability

Which type of research question examines differences and similarities?

  • Comparative Research Question (correct)
  • Evaluative Research Question
  • Explanatory Research Question
  • Descriptive Research Question

    • Quantitative Research uses non-numerical data to explore complex phenomena.

    False

    Field research involves collecting new data directly from sources like ____, interviews, experiments, and observations.

    <p>participants</p> Signup and view all the answers

    What are observations?

    <p>Observations involve systematically watching and recording behaviors and events in their natural settings.</p> Signup and view all the answers

    What are the advantages of homogeneous focus groups?

    <p>Allow exploration of shared experiences and attitudes in-depth</p> Signup and view all the answers

    Structured observations involve predefined categories and systematic recording.

    <p>True</p> Signup and view all the answers

    The primary role of an observation guide is to provide a framework for __________ data collection.

    <p>systematic</p> Signup and view all the answers

    Match the following terms with their definitions:

    <p>Reliability = Consistency of observations over time and across different observers Validity = Accuracy and truthfulness in capturing what observations intend to measure Cluster sampling = Divides the population into clusters and selects some clusters for study Targeted sampling = Selection based on specific criteria relevant to the research</p> Signup and view all the answers

    What is the purpose of selective responses in research?

    <p>Selective responses can lead to bias by favoring certain groups over others, impacting the generalizability of the study.</p> Signup and view all the answers

    What are the 3 requirements for causal relationships mentioned in the content?

    <p>Statistical Association, Temporal Order, No Confounding Variables</p> Signup and view all the answers

    What is a Likert-scale used for?

    <p>Measure attitudes and beliefs</p> Signup and view all the answers

    How does a Likert-scale operationalize variables?

    <p>By assigning numerical values to respondents' levels of agreement with statements</p> Signup and view all the answers

    Open questions in surveys provide quantifiable data.

    <p>False</p> Signup and view all the answers

    Match the following designs with their descriptions:

    <p>Between-subjects design = Different participants in each group Within-subjects design = Same participants exposed to all conditions</p> Signup and view all the answers

    What is the purpose of A/B testing?

    <p>To compare two versions to determine performance</p> Signup and view all the answers

    Eye-tracking is a method for measuring where and for how long a person ______ at various parts of a visual environment.

    <p>looks</p> Signup and view all the answers

    What is a crucial step in preparing an in-depth interview?

    <p>Ensuring informed consent</p> Signup and view all the answers

    Focus groups are used to gather diverse perspectives on a specific topic.

    <p>True</p> Signup and view all the answers

    What is pretesting?

    <p>Pretesting involves testing the survey or research instrument on a small sample before the main study to identify and fix any issues.</p> Signup and view all the answers

    Why do we pretest?

    <p>We pretest to ensure clarity, relevance, and effectiveness of the research instrument, and to identify and correct any problems before the main study.</p> Signup and view all the answers

    What does bias mean?

    <p>Bias refers to systematic errors that affect the validity of research findings.</p> Signup and view all the answers

    What is social desirability bias?

    <p>Social desirability bias is a type of bias where respondents provide answers they believe are socially acceptable rather than their true thoughts or behaviors.</p> Signup and view all the answers

    Why do we do quantitative data analysis or statistics?

    <p>We do quantitative data analysis or statistics to summarize and describe data effectively, infer patterns, relationships, and trends within data, make decisions based on empirical evidence, and validate hypotheses and theories with statistical rigor.</p> Signup and view all the answers

    What is an analysis plan?

    <p>An analysis plan outlines the strategy and steps for analyzing data in a research study, including the methods and techniques to be used for data analysis.</p> Signup and view all the answers

    What are center sizes in statistics?

    <p>Center sizes are values that represent the center of a data set and are calculated to summarize the data with a single representative value.</p> Signup and view all the answers

    What is the purpose of a frequency table?

    <p>The purpose of a frequency table is to display the frequency of various outcomes in a sample, especially to organize and summarize categorical data.</p> Signup and view all the answers

    What are examples of good tables?

    <p>All of the above</p> Signup and view all the answers

    What is a scatterplot used for?

    <p>A scatterplot is used to display the relationship between two continuous variables, identifying trends, clusters, and outliers.</p> Signup and view all the answers

    When should you use a bar chart?

    <p>When you want to compare values across different categories.</p> Signup and view all the answers

    How can you interpret bar charts?

    <p>By comparing the lengths or heights of the bars; longer or taller bars represent higher values.</p> Signup and view all the answers

    Why use a horizontal bar chart?

    <p>They are used when category names are long or when there are many categories, making the labels easier to read.</p> Signup and view all the answers

    What does a 100% stacked bar chart show?

    <p>The relative percentage of multiple data series in stacked bars, with each bar totaling 100%.</p> Signup and view all the answers

    When should you use a 100% stacked bar chart?

    <p>When you want to compare the percentage distribution of categories across different groups while keeping the total constant at 100%.</p> Signup and view all the answers

    When should you not use pie charts?

    <p>When you have many categories or when the differences between categories are small.</p> Signup and view all the answers

    When should you not use donut charts?

    <p>When precise comparison between categories is needed.</p> Signup and view all the answers

    How can you remove unnecessary noise in graphs?

    <p>By simplifying the design, avoiding excessive gridlines, 3D effects, and complex color schemes.</p> Signup and view all the answers

    How can you focus attention in graphs?

    <p>By highlighting key data points, using contrasting colors for important information, and providing clear labels and annotations.</p> Signup and view all the answers

    What are characteristics of good graphs?

    <p>Good graphs effectively communicate the intended message, are easy to read, and accurately represent the data.</p> Signup and view all the answers

    What are characteristics of bad graphs?

    <p>Bad graphs are cluttered, misleading, and difficult to interpret.</p> Signup and view all the answers

    Why is data used in journalism?

    <p>To provide evidence-based reporting, uncover trends, and support investigative stories.</p> Signup and view all the answers

    Why should we be critical when reading data journalism?

    <p>To ensure the data is accurately presented and interpreted, checking sources, methodology, and potential biases.</p> Signup and view all the answers

    Study Notes

    Class 1: Introduction to Research

    • Quality in research includes:
      • Objectivity and independence: unbiased and impartial
      • Controllability: transparent research execution and documentation
      • Reliability: consistent results
      • Validity: accurate measurement of concepts
      • Generalizability: representative sample
    • Research process:
      1. Formulation of problem definition and research question
      2. Critical literature review
      3. Methodology
      4. Data collection
      5. Data analysis
      6. Report and presentation

    Class 2: Research Questions and Hypotheses

    • Research question:
      • A specific question that guides the research
      • Formulated in a concrete way, involving variables
    • Types of research questions:
      • Descriptive: describes features of a concept
      • Comparative: examines differences and similarities
      • Evaluative: looks at advantages and disadvantages
      • Explanatory: investigates causes and consequences
      • Testing: assesses the effect of one variable on another
    • Good research question:
      • Clear, complete, and concise
      • Comprised of a main question and possible sub-questions
      • Feasible and specific
      • Relevant and original
    • Hypothesis:
      • A preliminary statement indicating what the researcher expects to find
      • Logically deduced from a theory or developed from observed facts
      • Closely linked to the research question
    • Good hypothesis:
      • Precise, testable through research, and formulated before conducting the research
      • Includes variables, the studied group, and the expected outcome

    Class 2: Variables

    • Variables:
      • Characteristics or features that can vary among respondents or participants
      • Directly linked to the research question and hypotheses
    • How to identify variables:
      • Break down the research question into specific elements that can be measured or observed
    • Conceptualizing and operationalizing:
      • Linking abstract concepts to measurable variables
      • Conceptualization: developing clear, concise definitions
      • Operationalization: defining how concepts will be measured or observed
    • Levels of measurement:
      • Determine how variables are quantified and analyzed
      • Different levels of measurement: nominal, ordinal, interval, and ratio

    Class 3: Desk Research

    • Desk research:
      • Analyzing existing information from sources like books, news media, and scientific articles
      • Less expensive, but may include outdated, biased, or incomplete information
    • How to conduct desk research:
      • Identify key terms
      • Gather information
      • Store and categorize
      • Process information
      • Quick scan and critique
    • Evaluating desk research:
      • Check for objectivity, controllability, reliability, validity, and generalizability

    Class 3: Referencing

    • Referencing:
      • Citing sources of information and ideas used in the research
      • Acknowledging original authors and providing evidence for arguments
    • Why do we do referencing?
      • Give credit to original authors
      • Provide evidence and support for arguments
      • Allow readers to verify and follow up on sources
      • Avoid plagiarism

    Class 3: Field Research

    • Field research:
      • Collecting new data directly from sources like participants
      • Specific to the researcher's needs, providing current, detailed information
    • What is the difference between qualitative and quantitative research?
      • Qualitative research: focuses on understanding deep insights, attitudes, and behaviors
      • Quantitative research: involves numerical data to measure and analyze variables

    Class 4: Quantitative Research

    • Associations between variables:
      • Identifying independent and dependent variables
      • Independent variable: manipulated or categorized to observe its effect
      • Dependent variable: measured and expected to change in response to the independent variable
    • Correlation and causality:
      • Correlation: indicates a relationship between variables, but does not imply causality
      • Causality: implies a direct effect of one variable on another
    • Experimental research:
      • Involves manipulating the independent variable to observe its causal effect
      • Identifying and controlling for confounding variables

    Class 4: Survey

    • Survey:
      • A quantitative data collection method that involves spreading a questionnaire
      • Structured, typically consisting of closed questions
    • Likert-scale:
      • Used in surveys to measure attitudes, beliefs, and perceptions
      • Assigns numerical values to respondents' levels of agreement
    • Criteria for a good survey:
      • Simple and understandable language
      • Free of double negation and ambiguous questions
      • Objective, avoiding socially desirable answers
      • Structured with a clear introduction, organized layout, and logically bundled topics
      • Exhaustive and non-overlapping in answer options
    • Types of surveys:
      • Written
      • Telephone
      • Face-to-face
      • Online
      • Panel surveys
    • Codebook and datamatrix:
      • Help in organizing and analyzing survey data
      • Codebook links each survey question to a code and each response option to a specific value
      • Datamatrix represents the collected data in a structured format### Eye-Tracking
    • Used to examine what captures the most attention of users or how users scan specific information
    • Concepts linked to eye-tracking:
      • Fixations: periods when the eyes are relatively stationary, focused on a particular point
      • Heatmaps: visual representations of the most frequently viewed areas on a visual display

    In-Depth Interviews

    • Qualitative data collection method involving direct, one-on-one engagement with participants to explore their perspectives on a specific topic in detail
    • When to use in-depth interviews:
      • When detailed, deep insights are needed about a participant's thoughts, feelings, or behaviors
    • When not to use in-depth interviews:
      • When needing to generalize findings to a larger population or when quantitative data is required
    • Advantages of in-depth interviews:
      • Provides deep, rich qualitative data
      • Flexibility in exploring new topics that arise during the interview
    • Disadvantages of in-depth interviews:
      • Time-consuming and resource-intensive
      • Potential for interviewer bias
    • Crucial steps in preparing an in-depth interview:
      • Develop a clear research goal
      • Create an interview guide
      • Pre-test the guide
      • Ensure informed consent is obtained from participants
    • Developing an in-depth interview based on the specific research goal:
      • Link research questions to variables, then to indicators, and finally to interview questions

    Focus Groups

    • Qualitative research method involving guided discussions with a small group of participants to gather diverse perspectives on a specific topic
    • When to use focus groups:
      • To explore complex behaviors, attitudes, and motivations
      • To generate ideas and insights for further research
      • When interaction between participants can provide deeper understanding
    • When not to use focus groups:
      • When quantitative data is needed
      • When individual privacy and confidentiality are paramount
      • When the topic is too sensitive for group discussion
    • Advantages of focus groups:
      • Generate rich, detailed data
      • Allow interaction and discussion among participants, leading to deeper insights
      • Cost-effective for gathering data from multiple people simultaneously
    • Disadvantages of focus groups:
      • Potential for groupthink, where participants conform to the majority view
      • Difficult to generalize findings due to small, non-random samples
      • Requires skilled moderation to manage group dynamics

    Observations

    • Systematic watching and recording of behaviors and events in their natural settings
    • Research questions suitable for observations:
      • Questions about how people behave in specific contexts
      • Questions aiming to understand processes, interactions, and environmental influences
    • Contexts where observations are applied:
      • In natural settings like schools, workplaces, or public spaces
      • In controlled environments like laboratories
    • Structured vs unstructured observations:
      • Structured: predefined categories and systematic recording
      • Unstructured: open-ended and flexible, without predefined categories
    • Participating vs non-participating observations:
      • Participating: observer is actively involved in the context
      • Non-participating: observer remains detached and does not interact with subjects
    • Open vs covered observations:
      • Open: subjects are aware they are being observed
      • Covered: subjects are unaware of the observation
    • Natural vs artificial observations:
      • Natural: observations in real-world settings
      • Artificial: observations in a controlled, experimental setting
    • People vs automatic observations:
      • People: observations conducted by human observers
      • Automatic: observations made using technology, such as cameras or sensors
    • Direct vs indirect observations:
      • Direct: observing actual behavior as it occurs
      • Indirect: observing evidence of behavior after it has occurred

    Ethnography

    • Qualitative research method involving immersive observation and participation in the daily life of the study subjects
    • Advantages of ethnographical research:
      • Provides deep, holistic insights
      • Captures context and nuances
    • Disadvantages of ethnographical research:
      • Time-consuming
      • Potential for observer bias

    Qualitative Data Analysis

    • Research steps/process of qualitative research:
      1. Data collection
      2. Data analysis
      3. Interpretation of findings
    • Inductive vs deductive in qualitative data analysis:
      • Inductive: developing theories based on observed data
      • Deductive: testing existing theories through data
    • Three steps of processing qualitative data:
      1. Open coding: identifying and categorizing basic units of meaning in the data
      2. Axial coding: finding relationships between open codes
      3. Selective coding: identifying the core category and integrating other categories around it

    Generalizability

    • The extent to which the results of a study can be applied to or across different populations, settings, and times beyond the specific conditions of the original study
    • Importance of generalizability:
      • Ensures the broader applicability and relevance of research findings
      • Allows findings to be useful in various contexts and for different groups, enhancing the study's impact and value
    • When is generalizability important?
      • When research aims to inform policy or practice on a large scale
      • In studies seeking to establish universal principles or theories
    • When is generalizability less important?
      • In exploratory or case studies focused on understanding specific contexts
      • In qualitative research prioritizing depth over breadth

    Sampling

    • Possible ways of sampling:
      1. Simple random sampling
      2. Systematic sampling
      3. Stratified sampling
      4. Cluster sampling
      5. Quota sampling
      6. Targeted sampling
      7. Snowball sampling
      8. Convenience sampling
    • Role/importance of a sampling frame:
      • A sampling frame is a list of all members of the population from which the sample is drawn
      • It is crucial for ensuring that the sample accurately represents the population
    • Difference between aselect and select sampling:
      • Aselect (random) sampling: every member of the population has an equal chance of being included
      • Select (non-random) sampling: members are chosen based on specific criteria or convenience### Grouped Frequency Tables
    • Used for large data sets with continuous variables
    • Important aspects:
      • Determine appropriate class intervals
      • Ensure intervals are mutually exclusive
      • Cover all data points
    • Calculating frequencies:
      • Absolute frequency: count of occurrences of each value
      • Absolute cumulative frequency: cumulative count of occurrences up to a certain value
      • Relative frequency: proportion of occurrences of each value
      • Relative cumulative frequency: cumulative proportion of occurrences up to a certain value

    Cross Tables

    • A table showing the frequency distribution of variables simultaneously
    • Used to explore relationships between two categorical variables
    • Role of X and Y variables: X and Y represent the two variables being compared

    Center Sizes (Measures of Central Tendency)

    • Values that represent the center of a data set
    • Calculated to summarize a data set with a single representative value
    • Link to measurement level: determines which measure is appropriate (e.g., mean for interval/ratio, median for ordinal)
    • Link to normal distribution: mean, median, and mode are equal in a perfectly normal distribution

    Measures of Central Tendency

    • Modus (Mode): most frequently occurring value
      • How to find: identify the value with the highest frequency
      • When to use: for nominal data or when the most common category is of interest
    • Median: middle value when data is ordered
      • How to find: arrange data in ascending order and find the middle value
      • When to use: for ordinal data or when data is skewed
    • Mean: average of all values
      • How to find: sum all values and divide by the number of values
      • When to use: for interval/ratio data with a normal distribution

    Data in Daily Life

    • Leaving data traces in daily life: digital footprints created through interactions with digital devices and online services
    • Examples of a data-driven world: personalized advertising, recommendations on streaming services, smart home devices, and big data analytics in healthcare, finance, and urban planning
    • Open data: data freely available for anyone to use, reuse, and redistribute without restrictions

    Reporting Quantitative Results

    • In-text reporting: used when having a limited amount of numerical data to share
      • Effective for highlighting key numbers directly within the text
    • Tables: used when presenting a broad range of data or comparing data points
      • How to use: organize clearly with labeled columns and rows, provide a title and legend, and avoid clutter and excessive use of borders and colors
    • Good tables: present data clearly and concisely, with appropriate labels and a clear structure, and without unnecessary detail

    Data Visualization

    • Heatmaps: graphical representations of data where individual values are represented as colors
      • Used to visualize the distribution and intensity of data across a given space
      • Link to tables: both present data, but heatmaps use color to represent data intensity
      • What to consider: color scale, avoiding too many colors, and normalizing data if necessary
    • Interpreting heatmaps: understanding the color gradient and what it represents
    • Graphs: visual representations of data designed to show relationships, patterns, and trends
      • Different graphs for different goals:
        • Bar charts for categorical data comparisons
        • Line graphs for showing trends over time
        • Scatter plots for showing relationships between two variables
    • Scatterplots: display the relationship between two continuous variables
      • When to use: exploring potential relationships or correlations between two variables
      • Interpreting scatterplots: looking for patterns, trends, clusters, and outliers
    • Lineplots: display data points over a continuous range, typically time
      • When to use: showing trends or changes over time
      • Interpreting lineplots: examining the slope of the lines to understand the direction and rate of change
    • Bar charts: represent categorical data with rectangular bars
      • When to use: comparing values across different categories
      • Interpreting bar charts: comparing the lengths or heights of the bars
    • 100% stacked bar charts: show the relative percentage of multiple data series in stacked bars, with each bar totaling 100%
      • When to use: comparing the proportional contributions of different categories while keeping the total constant at 100%

    Data Journalism

    • Using data in journalism: providing evidence-based reporting, uncovering trends, and supporting investigative stories
    • Being critical when reading data journalism: checking data sources, methodology, and potential biases in data collection and presentation

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    Description

    This quiz covers the principles of quality research, including objectivity, independence, controllability, and reliability. Understand the importance of unbiased research and its applications.

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