Data Analysis Seminar 5
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Questions and Answers

What is the purpose of data coding in quantitative data analysis?

  • To organize and categorize qualitative data for analysis.
  • To identify outliers and inconsistencies in the data.
  • To transform data into a format suitable for regression analysis.
  • To assign numerical values to participants' responses. (correct)
  • Which of the following is NOT a type of data editing in quantitative data analysis?

  • Checking for inconsistent responses.
  • Calculating descriptive statistics. (correct)
  • Verifying the completeness of data collection.
  • Identifying outliers.
  • What is an outlier in quantitative data analysis?

  • A response that is consistent with other responses, but not meaningful.
  • A response that is coded incorrectly.
  • A response that is missing or incomplete.
  • A response that is significantly different from other responses in the data set. (correct)
  • What is the purpose of examining frequencies in quantitative data analysis?

    <p>To identify patterns and trends in the data. (B)</p> Signup and view all the answers

    Which of the following is NOT a measure of central tendency used in descriptive statistics?

    <p>Standard deviation (D)</p> Signup and view all the answers

    What is the purpose of hypothesis testing in quantitative data analysis?

    <p>To determine if there is a significant difference between groups. (C)</p> Signup and view all the answers

    What type of statistical test is used to compare the means of two independent groups?

    <p>Two-sample t-test (D)</p> Signup and view all the answers

    What is the primary purpose of regression analysis in quantitative data analysis?

    <p>To examine the relationship between two or more variables. (C)</p> Signup and view all the answers

    What is a theme in data analysis?

    <p>A pattern that emerges in the data (A)</p> Signup and view all the answers

    What is the first step in coding data?

    <p>Reading through the data and getting a general sense of it (B)</p> Signup and view all the answers

    What is the purpose of memoing in data analysis?

    <p>Getting an initial sense of the data (B)</p> Signup and view all the answers

    What is the recommended number of themes to aim for when reducing the list of codes?

    <p>Five to seven (B)</p> Signup and view all the answers

    What type of theme is often unexpected?

    <p>Unexpected themes (D)</p> Signup and view all the answers

    Which of the following is NOT a step in coding data?

    <p>Identifying key events that keep repeating themselves (B)</p> Signup and view all the answers

    What is the main purpose of summarizing data after coding?

    <p>Synthesizing the key insights and learning from the data (B)</p> Signup and view all the answers

    What does it mean to "make sense of the data as a whole"?

    <p>Interpreting the meaning of the data (A)</p> Signup and view all the answers

    What type of hypothesis claims that the relationship between perceived intelligence and likelihood to date is influenced by gender?

    <p>Moderation hypothesis (A)</p> Signup and view all the answers

    Which hypothesis suggests that physical attractiveness is a predictor of likelihood to date?

    <p>Main effect hypothesis (B)</p> Signup and view all the answers

    What does the mediation hypothesis imply about communality of interests?

    <p>It indirectly impacts likelihood to date through perceived fit. (C)</p> Signup and view all the answers

    Which statement represents an interaction hypothesis?

    <p>There is an interaction effect between perceived intelligence and physical attractiveness on likelihood to date. (C)</p> Signup and view all the answers

    Which of the following is NOT a form of model validity according to the provided information?

    <p>Temporal validity (B)</p> Signup and view all the answers

    What statistical test is used to assess the significance of parameters in a model?

    <p>t-test (C)</p> Signup and view all the answers

    Which predictor is NOT mentioned as independently predicting likelihood to date?

    <p>Communality of interests (C)</p> Signup and view all the answers

    What does the R2 value indicate in model validation?

    <p>Statistical model fit (A)</p> Signup and view all the answers

    What is one anticipated benefit of AI in the construction industry?

    <p>Faster and higher quality construction projects (B)</p> Signup and view all the answers

    How might AI contribute to inclusivity in the workforce?

    <p>By providing opportunities for those currently facing barriers (C)</p> Signup and view all the answers

    What role will intelligent sensing systems play in future construction projects?

    <p>They will monitor the structural integrity of buildings (A)</p> Signup and view all the answers

    In future construction, what technology will assist designers alongside AI?

    <p>Augmented reality glasses for enhanced visuals (D)</p> Signup and view all the answers

    What aspect of the workforce is enhanced by AI, according to the anticipated future impacts?

    <p>Opportunities for a more diverse workforce (C)</p> Signup and view all the answers

    What is a concern that may arise with the implementation of AI technologies?

    <p>Regulation insufficiencies leading to misuse (A)</p> Signup and view all the answers

    What future benefit is expected from AI's role in the design process?

    <p>Inclusiveness and interactivity in design (B)</p> Signup and view all the answers

    What function will AI serve in assisting individuals who are differently abled?

    <p>It will facilitate new contributions in the workplace (C)</p> Signup and view all the answers

    What major theme was identified regarding the challenges students face in developing critical thinking skills?

    <p>Reading challenges (D)</p> Signup and view all the answers

    What minor theme was associated with the identified major theme of reading challenges?

    <p>Time constraints (A)</p> Signup and view all the answers

    Which of the following was suggested as a supplementary material to enhance critical thinking skills?

    <p>Reading of newspapers (D)</p> Signup and view all the answers

    Which observation was commonly noted about students' behavior in group settings?

    <p>Lack of group cohesion (A)</p> Signup and view all the answers

    What was a noted reason for students requesting extensive assistance from teachers?

    <p>Unwillingness to self-learn (D)</p> Signup and view all the answers

    What minor theme was associated with the need for authentic learning experiences?

    <p>Greater immersion in reading (C)</p> Signup and view all the answers

    Which of the following was cited as a behavioral issue affecting students’ critical thinking?

    <p>Fatigue (A)</p> Signup and view all the answers

    What is a common response from students regarding their engagement in group work?

    <p>Feeling of laziness (C)</p> Signup and view all the answers

    What is one impact of limited vocabulary on students' reading abilities?

    <p>Students struggle to understand written work. (B)</p> Signup and view all the answers

    According to the interviews, which sector has shown interest in the use of AI for improving compliance and safety?

    <p>Construction (C)</p> Signup and view all the answers

    What type of AI application has been notably utilized in Singapore's construction market according to Siti?

    <p>Automated 3D model checking (A)</p> Signup and view all the answers

    What has been a significant challenge associated with the potential of AI?

    <p>Ethical concerns and accuracy issues (C)</p> Signup and view all the answers

    What is one application of AI mentioned in Brazil's construction industry?

    <p>Budgeting and contract management (B)</p> Signup and view all the answers

    What has been observed in Singapore regarding AI tools?

    <p>AI tools have been widely used for five years. (A)</p> Signup and view all the answers

    Flashcards

    Data Coding

    The process of assigning numbers to participant responses for database entry.

    Outlier

    An observation that is significantly different from other data points.

    Inconsistent Responses

    Responses that do not align with other gathered information.

    Illegal Codes

    Values that do not match the established coding instructions.

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    T-Test

    A statistical method to compare the mean of a sample to a population standard.

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    ANOVA

    A statistical technique used to analyze mean differences among three or more groups.

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    Simple Regression

    Analysis to determine how one independent variable affects one dependent variable.

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    Descriptive Statistics

    Statistical techniques that summarize and describe data features, like central tendencies.

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    Theme/Category

    A group of similar ideas or issues expressed in data.

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    Organizing Data

    The process of categorizing and narrowing down data types.

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    Exploring Data

    Initial step of reading and ensuring completeness of data.

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    Memoing

    Writing notes about field observations to grasp initial data insights.

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    Coding Data

    Assigning labels to text segments to describe meanings.

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    Identifying Themes

    Recognizing patterns and repeating key phrases within the data.

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    Types of Themes

    Different categories of themes include ordinary, unexpected, hard-to-classify, major, and minor.

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    Summarizing Data

    Writing a summary of insights gained post-coding.

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    Moderator

    A variable that affects the strength or direction of a relationship between independent and dependent variables.

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    Dependent Variable

    The outcome variable that researchers are trying to explain or predict.

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    Independent Variable

    The variable that is manipulated to observe its effect on the dependent variable.

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    Mediating Variable

    A variable that explains the relationship between the independent and dependent variables.

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    Main Effect Hypothesis

    A hypothesis stating a primary prediction about a relationship between variables.

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    Moderation Hypothesis

    A hypothesis that specifies how a moderator influences the relationship between two variables.

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    Model Validity

    The degree to which a model accurately reflects the data being analyzed.

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    Interaction Hypothesis

    A hypothesis that proposes that the effect of one variable depends on the level of another variable.

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    Critical Thinking Challenges

    Students face various obstacles in developing critical thinking skills, such as reading difficulties.

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    Reading Challenges

    Specific difficulties that hinder students' ability to engage with texts and develop critical thinking.

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    Authentic Learning Experience

    Real-world learning that helps students connect better to reading materials and critical thinking.

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    Time Constraints

    Limited time available for students to read and think critically, affecting their engagement.

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    Need to Read Newspapers

    Encouragement to read current events to enhance awareness and critical analysis skills.

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    Unwillingness to Work in Groups

    Students often avoid collaborative learning, which can limit engagement and critical thinking development.

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    Limited Independent Thinking

    A lack of ability to think autonomously, often due to over-reliance on teachers.

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    Need for Journals

    Supplementary reading materials like journals can improve students’ critical thinking skills.

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    Limited Vocabulary

    A restricted range of words that students can use and understand.

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    Deciphering Meaning

    The ability to interpret and understand written words or phrases.

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    Critical Thinking

    The ability to analyze and evaluate an issue to form a judgment.

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    Artificial Intelligence (AI)

    Technology that simulates human intelligence to perform tasks.

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    Generative AI

    A type of AI that can create new content or designs based on input data.

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    Construction Industry

    Sector focused on building infrastructure, including architecture and engineering.

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    Automation Challenges

    Issues related to replacing human jobs with machines and technology.

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    AI in Document Analysis

    Using AI technology to improve the management and interpretation of documents.

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    AI in construction

    Artificial intelligence assisting in all stages of construction.

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    Augmented Reality (AR)

    Technology that overlays digital information on the real world.

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    Intelligent sensing systems

    Technologies that monitor structures for safety and efficiency.

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    Continuous learning in AI

    AI's ability to learn and adapt from experience over time.

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    Automation effects

    The impact of automating tasks on jobs and efficiency.

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    Inclusion through technology

    Using technology like AI to empower marginalized individuals.

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    Empowerment via AI

    How AI offers new opportunities for individuals with disabilities.

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    Sustainable construction

    Building practices that minimize waste and environmental impact.

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    Study Notes

    Data Analysis Seminar 5

    • Seminar presented by Dr. Waqas Ahmed
    • Focuses on quantitative and qualitative data analysis
    • Location: Nizhny Novgorod

    Getting Data Ready for Analysis

    • Data coding involves assigning numbers to participant responses for database entry
    • Data entry is the process of inputting coded responses into a database, using software like SPSS
    • Raw data can be entered via other software

    Editing Data

    • Outlier responses are significantly different from other responses
    • Inconsistent responses don't align with other information
    • Illegal codes are values not in the coding instructions

    Transforming Data

    • The presentation shows a screenshot of SPSS software
    • Data are organized in rows and columns.
    • Data manipulation and analysis (such as computing or using formulas) are shown within the software. Various variables and data points from the database are visible.

    Getting a Feel for the Data

    • Explains scale types for data analysis
    • Presents various methods for visually summarizing data, including bar charts, pie charts, histograms, scatterplots, and others. Tables (Table 14.1) visually categorize scale types by data examples, analysis methods and displaying the summary

    Frequencies

    • Analysis of data within SPSS software
    • Presentation of frequency tables
    • Data visualization through graphical representations
    • Computation of descriptive statistics (mean, median, mode, etc.)
    • Shows functions and tools available for data manipulation with examples

    Descriptive Statistics - Central Tendencies and Dispersions

    • Presents SPSS software; data arranged in tables; shows data manipulation tools for central tendency and summary statistics

    Reliability Analysis

    • Shows SPSS software, with a focus on reliability analysis tools
    • Includes tools for various statistical tests, model exploration and analysis.
    • Examples of variables and statistical functions

    Quantitative Data Analysis: Hypothesis Testing

    • Focuses on hypothesis testing in quantitative data analysis
    • Discusses the one-sample t-test, a technique to evaluate whether the mean of a population matches a standard
      • Shows one-tailed and two-tailed tests
      • Explains the rejection and acceptance regions for different hypothesis tests
    • Discusses "Analysis of Variance" (ANOVA) as a technique to analyze differences in means across more than two groups of an interval- or ratio-scaled dependent variable

    Regression Analysis

    • Simple regression analysis analyzes the relationship where one independent metric variable affects a dependent metric variable

    Statistical Significance Tests

    • Illustrates different statistical tests (Fisher's Exact Test, Students T-Test, Regression Analysis, ANOVA)
    • Graphs accompany each test explaining the visual representation of the tests and the data being analyzed (X and Y-axis)
    • The example data are related to evaluating characteristics of a tumor and drug dose.

    Conceptual Model

    • Presentation of a model illustrating relationships between variables with arrows to depict interaction, directions and influence.
    • Includes ideas of moderating effects (like gender, race, etc), mediating variables, independent and dependent variables

    Hypothesis based on conceptual model

    • Outlines hypothesized relationships and effects (Main Effect, Moderation, Mediation, Multiple predictor and Interaction)

    Model Validation

    • Explains the components of a model validation, like face validity, statistical validity (model fit, parameter/model significance, effect strength, discussion of multicollinearity from correlation matrix) and predictive validity (out-of-sample issues).

    Step 1

    • Model Summary - shows adjusted R squared values, and estimates for step one
    • ANOVA shows the model and residual data and figures
    • Coefficients data with B, Std.Error, Beta, t and P values

    Step 2

    • Model Summary - illustrates adjusted R squared values, and estimate for step two
    • ANOVA table - data about the model and residual data.
    • Coefficients data with B, Std.Error, Beta, t, and p-values

    Step 3

    • Model Summary - shows adjusted R squared values, and estimates for step three
    • ANOVA shows the model and residual data and figures
    • Coefficients data with B, Std. Error, Beta, t and p-values
    • Highlights that some independant variables have an insignificant effect on the dependent variable while the other have a significant effect

    Software for Quantitative Data Analysis

    • Lists software options (SPSS, Minitab, Stata) for conducting quantitative analysis
    • Provides links for each software program.

    Qualitative Data Analysis

    • Defines qualitative data as data that can't be easily measured numerically; that focus on attitudes, opinions, behaviours and experiences
    • Describes the way qualitative data is collected
    • Describes processes and procedures to explain, interpret and understand people and situations

    Qualitative Data Collection

    • Identifies methods for collecting qualitative data
    • Includes: Observations, Interviews, Documents, Focus Groups, Audio/Video Recordings and Questionnaires

    Coding

    • Coding is a data reduction method to group similar data into manageable units.
    • Helps visualize relationships and patterns in qualitative data.

    Categories/Themes

    • Explains the process of categorizing and creating themes from qualitative data, based on similar participant responses
    • Shows that themes can be named based on data or by researcher observations
    • Discusses different types of themes (ordinary, unexpected, hard-to-classify, major, and minor)

    Organizing Data

    • Discusses methods for organizing qualitative data sets
    • Includes: Reviewing all data, grouping key data into smaller groups, material organization by type (observation, interview, field notes)

    Exploring Data

    • Explaining data to get a overall sense of the data set.
    • Reading through the data
    • Ensuring data is complete and understandable before proceeding to analysis
    • Summarizes each data point to give an initial sense of the data
    • Taking notes and creating memos.

    Steps in Coding the Data

    • Steps involved
    • Defining and assigning codes
    • Making lists of code words and grouping them for relationships
    • Returning to the data
    • Summarizing codes into major themes

    Identifying Themes

    • Explains how to identify patterns and repeated events/phrases in data
    • Focuses on themes and keywords within the data

    Themes

    • Explains different types of themes (ordinary, unexpected, hard-to-classify, major, and minor)

    Summarizing Your Data

    • Explains how to summarize qualitative data and its themes
    • Importance of summing up learnings. Synthesis across sources

    Major and Minor Themes from Teacher's Interview

    • Shows how interviews with teachers can be analyzed.
    • Shows how questions and responses from teachers can be grouped into major and minor themes
    • Example questions and themes/subthemes

    Collating Data into a Table of Coded Responses

    • Shows how collected data and responses can be organized for analysis;
    • Illustrates connections based on the major and minor themes and interviews collected

    Explanation of Themes

    • Discusses how to explain data and themes using quotes from participants

    Example of Narrative Format

    • Illustrates how to create a narrative explanation based on participants' quotes

    Software for Qualitative Analysis

    • Provides software options for qualitative data analysis (ATLAS.ti, NVIVO)
    • Gives links for the softwear mentioned

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    Related Documents

    Data Analysis Seminar 5 PDF

    Description

    This seminar led by Dr. Waqas Ahmed covers essential topics in quantitative and qualitative data analysis. Participants will learn about data coding, editing, and transformation using SPSS software, along with techniques for identifying outliers and managing inconsistent responses. Join us to enhance your data analysis skills.

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