Data Collection and Analysis

Research Methods in Architecture

Spring 2023

THE FUTURE STARTS HERE

Dr. Yasser Mahgoub

Sampling

Aim of sampling is to equate unknown characteristics that may influence variation and to preserve the representativeness of the sample. It is also a time-convenient and a cost-effective and hence forms the basis of any research design.

Sampling definition

Sampling is a technique of selecting individual members or a subset of the population to make statistical inferences from them and estimate characteristics of the whole population.

DifferencesAmongOccupants

While readily visible distinctions are apparent (e.g. gender, age, etc.), many subtle psychological, cultural, and social factors exist which may be extremely important for the designer to consider but which cannot be readily identified. People have memories of past events, the ability to learn, a cultural and biological heritage, and many other attributes which serve to distinguish individuals from one another.

PersonalandCulturalDifferences

Age –

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Gender –

Health –

Education –

Economic Status

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Social Status

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Nature of Employment

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Ethnic Heritage

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Previous Experience

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Expectations

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Motivations

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Attitudes

–…

Sampling Methods

1. Non-probability Sampling

2. Probability Sampling

Populations and Samples

Sample

Target Population Sample

Population

-All teachers in high schools in one city

-College students in all community colleges

-Adult educators in all schools of education

Sample

-All high school biology teachers

-Students in one community college

-Adult educators in 5 schools

1. Non-probability Sampling

• The researcher chooses members for research at random.

This sampling method is not a fixed or predefined selection process.

1. Non-probability Sampling

• Subjective judgments are used to determine the population that are contained in the sample.

• This makes it difficult for all elements of a population to have equal opportunities to be included in a sample

Convenience sampling

This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street. Cases are selected based on their availability for the study.

Convenience sampling

Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness

Convenience sampling

This non-probability sampling method is used when there are time and cost limitations in collecting feedback. In situations where there are resource limitations such as the initial stages of research, convenience sampling is used.

Snowball sampling

Apply when the subjects are difficult to trace. Researchers can track a few categories to interview and derive results. They can also implement this sampling method in situations where the topic is highly sensitive and not openly discussed.

Judgmental sampling

Select cases based on some purpose: most similar\dissimilar, typical or critical cases

Systematic Sampling

Select cases based on some predefined criteria (Interval sampling)

Every 4th

Quota sampling

• The selection of members in this sampling technique happens based on a pre-set standard.

• A sample is formed based on specific attributes, the created sample will have the same qualities found in the total population. It is a rapid method of collecting samples.

Uses of non-probability sampling

• Create a hypothesis or an assumption:

– When limited or no prior information is available.

– Immediate return of data and builds a base for further research.

• Exploratory research:

– When conducting qualitative research, pilot studies, or exploratory research.

• Budget and time constraints:

– Budget and time constraints,

– Preliminary data must be collected.

–

It is easier to pick respondents at random and have them take the survey or questionnaire.

Advantages of Non-probability sampling

• Fast, low effort and cost methods

•

Useful in exploratory research

2. Probability Sampling

• A sampling technique where a researcher sets a selection criteria and chooses members of a population randomly.

• All the members have an equal opportunity to be a part of the sample with this selection parameter.

• Common feature is that each unit in the population has a known, nonzero probability of being included in the sample.

Simple Random Sample

Each member of the study population has an equal probability of being selected.

Simple Random Sample

Entirely random method of selecting the sample through an automated process, the lottery system and using number generating software/ random number table.

Stratified Random Sample

The researcher divides a more extensive population into smaller groups that usually don’t overlap but represent the entire population. Each member of a population is assigned to a group or stratum, then random sample is drawn from each stratum separately to ensure levels represented.

Stratified Random Sample

A standard method is to arrange or classify by sex, age, ethnicity, and similar ways. Splitting subjects into mutually exclusive groups and then using simple random sampling to choose members from groups.

Stratified Random Sample

Members of these groups should be distinct so that every member of all groups get equal opportunity to be selected using simple probability. This sampling method is also called “random quota sampling.”

Proportional Random Sample

Each member of a population is assigned to a sub-group, then representative sample is drawn from each group proportional to population.

Proportional Stratification Sampling Approach

Sample = 300

Systematic sampling

• When you choose every “nth” individual to be a part of the sample. For example, you can select every 3th person to be in the sample. There’s an equal opportunity for every member of a population to be selected using this sampling technique.

Advantages of Probability Sampling

• It’s Cost-effective and Time-effective: A larger sample can also be chosen based on numbers assigned to the samples and then choosing random numbers from the more significant sample.

• It’s simple and straightforward: Easy way of sampling, does not involve a complicated process.

• It is non-technical: Doesn’t require any technical knowledge. It doesn’t require intricate expertise and is not at all lengthy.

When to use probability sampling?

1. When you want to reduce the sampling bias: The selection of the sample largely determines the quality of a researcher’s findings and inference. Probability sampling leads to higher quality findings because it provides an unbiased representation of the population.

2. When the population is usually diverse: Create samples that fully represent the population. Helps pick samples from various socio-economic strata, background, etc. to represent the broader population.

3. To create an accurate sample: Create accurate samples of population. Use proven statistical methods to draw a precise sample size to obtained well-defined data.

What are the steps involved in probability sampling?

1. Choose your population of interest carefully: Carefully think and choose from the population, people you believe whose opinions should be collected and then include them in the sample.

2. Determine a suitable sample frame: Your frame should consist of a sample from your population of interest and no one from outside to collect accurate data.

3. Select your sample and start your survey: It can sometimes be challenging to find the right sample and determine a suitable sample frame. Getting a sample to respond to a probability survey accurately might be difficult but not impossible.

Youprobablycan’tsendsurveystoeveryone,butyou canalwaysgiveeveryoneachancetoparticipate,this iswhatprobabilitysampleisallabout.

Differences between probability and non-probability sampling

Probability sampling

Non-probability sampling

Randomly selected. Subjective judgment.

Everyone in the population has an equal chance of getting selected. Not everyone has an equal chance to participate.

No sampling bias.

Useful in an environment having a diverse population.

Sampling bias is not a concern for the researcher.

Useful in an environment that shares similar traits.

Used when the researcher wants to create accurate samples.

Finding the correct sample is not simple.

This method does not help in representing the population accurately.

Finding an sample is very simple.

How do you decide on the type of sampling to use?

• It is essential to choose a sampling method accurately to meet the goals of your study.

• Factors:

– Research goals.

– Combination of cost, precision, or accuracy.

– Effective sampling techniques that might potentially achieve the research goals.

– Test each of these methods and examine whether they help in achieving your goal.

Sampling Methods

1. Non-probability Sampling

2. Probability Sampling

Accuracy and Reliability of Data

• Data quality: validity, reliability and utility of measurement

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Reduction of error in measurement

Design Fit

• Statistical Conclusion Validity

• Utility (Efficiency/Generality)

Scales of Measurement

1. Nominal or Categorical

2. Ordinal

3. Interval

4. Ratio

Select Scales of Measurement

Nominal (Categorical): categories that describe traits or characteristics participants can check

Ordinal: participants rank order a characteristic, trait or attribute

Select Scales of Measurement

• Interval: provides “continuous” response possibilities to questions with assumed equal distance

• Ratio: a scale with a true zero and equal distances among units

1. Nominal or Categorical

• Classification according to presence or absence of qualities

• No information provided on order or magnitude of differences

• Because nominal scales have no quantitative properties, data consist of frequencies only –

E.g., sex, race, religion, political party

• Classification according to degree of quality present

• Distinguish between ordered relationships between classes or characteristics, but no information about the magnitude of difference

– E.g., tall > normal > short first > second > third

3. Interval

• Addition of a meaningful unit of measure: equal size interval

• Consistent and useful unit of measure allows the use of basic arithmetic functions (addition, subtraction, multiplication, division)

– E.g., Fahrenheit scale, shoe size

4. Ratio

• Addition of an absolute zero point to interval scale

• Zero implies total absence of the characteristic

• Ability to utilize ratio statements (2:1, 1:5)

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E.g., Height and weight

Bar Graphs

• Qualitative Data (Nominal\Ordinal)

• Width of the bars is constant

• Bars separated by constant distance

• Normally height of bar corresponds to frequency of category

• Concerns:

• Orientation (horiz vs. vertical)

• Grid lines

• Axes & Tickmarks

• Fill

• Order

Figure 1.

Prevalence of Eye Color

Frequency

Elements needed:

•Identification (Figure #)

•Title

•Labels\Headings

Remember:Figureshouldreadlikeaself-containedparagraph.

Blue

Brown

Green

Black

Eye Color

Quantitative Data (Interval\Ratio)

• Histogram (similar to Bar Graph)

• Okay to put breaks in axis where set of values omitted

• Bar widths represent real limits

• Therefore, touch

• Keep bar widths constant

Scores of First Exam

Elements needed:

•Identification (Figure #)

•Title

•Labels\Headings

Test Scores

Quantitative Data (Interval\Ratio)

• Frequency Polygon

• Values represented as points above interval

Scores of First Exam

Test Scores

Elements needed:

•Identification (Figure #)

•Title

•Labels\Headings

Remember:Figureshouldreadlikeaself-containedparagraph.