Podcast
Questions and Answers
What is the purpose of data coding in quantitative data analysis?
What is the purpose of data coding in quantitative data analysis?
Which of the following is NOT a type of data editing in quantitative data analysis?
Which of the following is NOT a type of data editing in quantitative data analysis?
What is an outlier in quantitative data analysis?
What is an outlier in quantitative data analysis?
What is the purpose of examining frequencies in quantitative data analysis?
What is the purpose of examining frequencies in quantitative data analysis?
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Which of the following is NOT a measure of central tendency used in descriptive statistics?
Which of the following is NOT a measure of central tendency used in descriptive statistics?
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What is the purpose of hypothesis testing in quantitative data analysis?
What is the purpose of hypothesis testing in quantitative data analysis?
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What type of statistical test is used to compare the means of two independent groups?
What type of statistical test is used to compare the means of two independent groups?
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What is the primary purpose of regression analysis in quantitative data analysis?
What is the primary purpose of regression analysis in quantitative data analysis?
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What is a theme in data analysis?
What is a theme in data analysis?
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What is the first step in coding data?
What is the first step in coding data?
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What is the purpose of memoing in data analysis?
What is the purpose of memoing in data analysis?
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What is the recommended number of themes to aim for when reducing the list of codes?
What is the recommended number of themes to aim for when reducing the list of codes?
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What type of theme is often unexpected?
What type of theme is often unexpected?
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Which of the following is NOT a step in coding data?
Which of the following is NOT a step in coding data?
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What is the main purpose of summarizing data after coding?
What is the main purpose of summarizing data after coding?
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What does it mean to "make sense of the data as a whole"?
What does it mean to "make sense of the data as a whole"?
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What type of hypothesis claims that the relationship between perceived intelligence and likelihood to date is influenced by gender?
What type of hypothesis claims that the relationship between perceived intelligence and likelihood to date is influenced by gender?
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Which hypothesis suggests that physical attractiveness is a predictor of likelihood to date?
Which hypothesis suggests that physical attractiveness is a predictor of likelihood to date?
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What does the mediation hypothesis imply about communality of interests?
What does the mediation hypothesis imply about communality of interests?
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Which statement represents an interaction hypothesis?
Which statement represents an interaction hypothesis?
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Which of the following is NOT a form of model validity according to the provided information?
Which of the following is NOT a form of model validity according to the provided information?
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What statistical test is used to assess the significance of parameters in a model?
What statistical test is used to assess the significance of parameters in a model?
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Which predictor is NOT mentioned as independently predicting likelihood to date?
Which predictor is NOT mentioned as independently predicting likelihood to date?
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What does the R2 value indicate in model validation?
What does the R2 value indicate in model validation?
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What is one anticipated benefit of AI in the construction industry?
What is one anticipated benefit of AI in the construction industry?
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How might AI contribute to inclusivity in the workforce?
How might AI contribute to inclusivity in the workforce?
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What role will intelligent sensing systems play in future construction projects?
What role will intelligent sensing systems play in future construction projects?
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In future construction, what technology will assist designers alongside AI?
In future construction, what technology will assist designers alongside AI?
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What aspect of the workforce is enhanced by AI, according to the anticipated future impacts?
What aspect of the workforce is enhanced by AI, according to the anticipated future impacts?
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What is a concern that may arise with the implementation of AI technologies?
What is a concern that may arise with the implementation of AI technologies?
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What future benefit is expected from AI's role in the design process?
What future benefit is expected from AI's role in the design process?
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What function will AI serve in assisting individuals who are differently abled?
What function will AI serve in assisting individuals who are differently abled?
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What major theme was identified regarding the challenges students face in developing critical thinking skills?
What major theme was identified regarding the challenges students face in developing critical thinking skills?
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What minor theme was associated with the identified major theme of reading challenges?
What minor theme was associated with the identified major theme of reading challenges?
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Which of the following was suggested as a supplementary material to enhance critical thinking skills?
Which of the following was suggested as a supplementary material to enhance critical thinking skills?
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Which observation was commonly noted about students' behavior in group settings?
Which observation was commonly noted about students' behavior in group settings?
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What was a noted reason for students requesting extensive assistance from teachers?
What was a noted reason for students requesting extensive assistance from teachers?
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What minor theme was associated with the need for authentic learning experiences?
What minor theme was associated with the need for authentic learning experiences?
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Which of the following was cited as a behavioral issue affecting students’ critical thinking?
Which of the following was cited as a behavioral issue affecting students’ critical thinking?
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What is a common response from students regarding their engagement in group work?
What is a common response from students regarding their engagement in group work?
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What is one impact of limited vocabulary on students' reading abilities?
What is one impact of limited vocabulary on students' reading abilities?
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According to the interviews, which sector has shown interest in the use of AI for improving compliance and safety?
According to the interviews, which sector has shown interest in the use of AI for improving compliance and safety?
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What type of AI application has been notably utilized in Singapore's construction market according to Siti?
What type of AI application has been notably utilized in Singapore's construction market according to Siti?
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What has been a significant challenge associated with the potential of AI?
What has been a significant challenge associated with the potential of AI?
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What is one application of AI mentioned in Brazil's construction industry?
What is one application of AI mentioned in Brazil's construction industry?
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What has been observed in Singapore regarding AI tools?
What has been observed in Singapore regarding AI tools?
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Flashcards
Data Coding
Data Coding
The process of assigning numbers to participant responses for database entry.
Outlier
Outlier
An observation that is significantly different from other data points.
Inconsistent Responses
Inconsistent Responses
Responses that do not align with other gathered information.
Illegal Codes
Illegal Codes
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T-Test
T-Test
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ANOVA
ANOVA
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Simple Regression
Simple Regression
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Descriptive Statistics
Descriptive Statistics
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Theme/Category
Theme/Category
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Organizing Data
Organizing Data
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Exploring Data
Exploring Data
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Memoing
Memoing
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Coding Data
Coding Data
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Identifying Themes
Identifying Themes
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Types of Themes
Types of Themes
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Summarizing Data
Summarizing Data
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Moderator
Moderator
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Dependent Variable
Dependent Variable
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Independent Variable
Independent Variable
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Mediating Variable
Mediating Variable
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Main Effect Hypothesis
Main Effect Hypothesis
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Moderation Hypothesis
Moderation Hypothesis
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Model Validity
Model Validity
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Interaction Hypothesis
Interaction Hypothesis
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Critical Thinking Challenges
Critical Thinking Challenges
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Reading Challenges
Reading Challenges
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Authentic Learning Experience
Authentic Learning Experience
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Time Constraints
Time Constraints
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Need to Read Newspapers
Need to Read Newspapers
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Unwillingness to Work in Groups
Unwillingness to Work in Groups
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Limited Independent Thinking
Limited Independent Thinking
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Need for Journals
Need for Journals
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Limited Vocabulary
Limited Vocabulary
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Deciphering Meaning
Deciphering Meaning
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Critical Thinking
Critical Thinking
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Artificial Intelligence (AI)
Artificial Intelligence (AI)
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Generative AI
Generative AI
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Construction Industry
Construction Industry
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Automation Challenges
Automation Challenges
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AI in Document Analysis
AI in Document Analysis
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AI in construction
AI in construction
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Augmented Reality (AR)
Augmented Reality (AR)
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Intelligent sensing systems
Intelligent sensing systems
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Continuous learning in AI
Continuous learning in AI
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Automation effects
Automation effects
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Inclusion through technology
Inclusion through technology
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Empowerment via AI
Empowerment via AI
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Sustainable construction
Sustainable construction
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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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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.