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Questions and Answers
What type of data refers to names and classifications?
What type of data refers to names and classifications?
Quantitative data can only be discrete and not continuous.
Quantitative data can only be discrete and not continuous.
False
What is a variable in the context of statistics?
What is a variable in the context of statistics?
A characteristic or property of the units being studied that can fluctuate.
Data that is collected from a subset of units in a population is known as __________ data.
Data that is collected from a subset of units in a population is known as __________ data.
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Match the following types of data with their definitions:
Match the following types of data with their definitions:
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Which of the following is an example of quantitative data?
Which of the following is an example of quantitative data?
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Secondary data involves collecting information from every unit in the population.
Secondary data involves collecting information from every unit in the population.
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What determines the quality of a statistical study?
What determines the quality of a statistical study?
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What type of data consists of labels or names used to identify an attribute?
What type of data consists of labels or names used to identify an attribute?
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Quantitative data can be represented in non-numeric terms.
Quantitative data can be represented in non-numeric terms.
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Give one example of qualitative data.
Give one example of qualitative data.
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Quantitative variables can be classified as __________ and continuous.
Quantitative variables can be classified as __________ and continuous.
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Match the following terms with their descriptions:
Match the following terms with their descriptions:
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Qualitative data can be ranked and ordered.
Qualitative data can be ranked and ordered.
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What is a characteristic of quantitative data?
What is a characteristic of quantitative data?
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An example of a continuous variable is __________.
An example of a continuous variable is __________.
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Which of the following does not represent qualitative data?
Which of the following does not represent qualitative data?
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Which scale of measurement includes an identifiable absolute zero point?
Which scale of measurement includes an identifiable absolute zero point?
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Ordinal data allows us to say how much more one value is compared to another.
Ordinal data allows us to say how much more one value is compared to another.
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Give an example of a nominal variable.
Give an example of a nominal variable.
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The temperature measured in degrees Celsius is an example of an _____ scale.
The temperature measured in degrees Celsius is an example of an _____ scale.
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Match the following scales of measurement with their appropriate descriptions:
Match the following scales of measurement with their appropriate descriptions:
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Which of the following is a characteristic of qualitative variables?
Which of the following is a characteristic of qualitative variables?
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Bivariate analysis involves three or more variables in a data set.
Bivariate analysis involves three or more variables in a data set.
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What is a common example of ordinal data?
What is a common example of ordinal data?
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The analysis that involves only one variable is called _____ analysis.
The analysis that involves only one variable is called _____ analysis.
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Which of the following statements about interval scales is true?
Which of the following statements about interval scales is true?
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Study Notes
Introduction to Statistics for Built Environment
- Course code: AEDF 0623
- Topic: Variables & Measurement
Contents
- Definition of variable and data
- Statistical data source
- Types of data/variables
- Levels/scales of measurement of variables
- Univariate, bivariate, and multivariate analysis
- Independent and dependent variables
Definition of Variable
- Any measurable characteristic, attribute, amount, or number
- A characteristic or property of the units being studied
- Values may fluctuate over time and between data units in a population
- Examples: Age, gender, revenue, costs, birth place, capital expenditure, grades, vehicle type, drainage system type
Definition of Data
- A collection of values or observations from reliable sources
- Arranged according to observational units (people, samples, populations) and variables
- Quality of data collected determines the study's quality
Statistical Data Source
-
Direct Method (Primary Data):
- Collecting data from every unit in a population (census) or a subset (sample)
- Examples: Surveys, interviews, focus group discussions (FGDs), observational studies, experiments
-
Indirect Method (Secondary Data):
- Data collected as part of daily processes and record-keeping by organizations
- Examples: Historical data, public records, books, journals, reports
Types of Data & Variables
-
Qualitative (Categorical/Attribute):
- Data representing categories/classifications only
- Examples: Types of vegetation, education level, satisfaction level
-
Quantitative (Numerical):
- Data representing counts or measurements
- Discrete: Counted items (e.g., number of rooms, defects per hour, number of days)
- Continuous: Measured characteristics (e.g., temperature, time, IQ test, CGPA, weight, height, distance)
Qualitative Data
- Labels/names to identify attributes/characteristics of elements
- Represented in separate categories
- Examples: Types of vegetation, exam results (pass/fail), residents' race
Quantitative Data
- Always numeric, indicating "how many" or "how much"
- Measurements and counts can be ranked and ordered
- Examples: Distance, height, weight, speed
Qualitative & Quantitative Data Example
- Data from the presentation showing various statistics based on qualitative and quantitative variables, such as:
- Labor force statistics, comparing female labor force participation rates by certificate level or age group
- Job satisfaction, burnout, intention to leave data, plotted as bar graphs, with percentage based data
- Business benefits of green building data, shown as bar charts and containing descriptive data points
Discrete & Continuous Variables
- Discrete: Variables with countable whole numbers (e.g., number of lightbulbs, cars)
- Continuous: Variables that can take on any value within a range (e.g., height, weight, temperature, distance)
Level/Scales of Measurement of Variables
- Nominal: Categories/labels only (e.g., color, brand, type)
- Ordinal: Ordered categories with meaningful differences—ranking (e.g., education level, satisfaction level)
- Interval: Ordered categories with equal intervals between values, no true zero point (e.g., temperature, IQ test)
-
Ratio: Ordered categories with equal intervals and a true zero point (e.g., weight, height, distance, time)
- Examples from the presentation demonstrating application of each scale include graphs and descriptions of data used.
Univariate, Bivariate & Multivariate Analysis
-
Univariate: Analysis of a single variable
- Example: Analyzing the average height of trees of one type
- Bivariate: Analysis of two variables to determine relationships (correlation) Example: Analyzing how surrounding temperature affects air conditioning usage
-
Multivariate: Analysis of more than two variables to understand relationships
- Example: How weather, season, soil, etc, affect growth of plants
Independent & Dependent Variables
- Independent Variable: Manipulated or controlled by the experimenter
- Dependent Variable: Measured and depends on the independent variable
- Examples from the presentation show application examples of these concepts through use cases of real-life data analysis
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Description
This quiz covers the foundational concepts of variables and measurement in the context of statistics for the built environment. It includes definitions of variables and data, types of data, levels of measurement, and different analysis forms. Perfect for students enrolled in the AEDF 0623 course.
