The document discusses data collection methods for research studies. It covers key topics such as sampling procedures, sample size determination, sampling methods, types of data, and methods for collecting data. Specifically, it describes the 7 steps for sampling procedures which include defining the population, determining the sampling method, sample size determination, creating a sampling frame, selecting the sample, obtaining consent, and collecting data. It also discusses common sampling methods like random sampling, stratified sampling, and cluster sampling. Finally, it outlines various data collection techniques including surveys, observations, experiments, interviews, and secondary data analysis.

1. UNIT II
DATA
COLLECTION
Dr. Arnav Chowdhury

2. AGENDA
Sampling procedure; Sample size; Determination and
selection of sample member; Types of data and various
methods of collecting data; Preparation of
questionnaire and schedule; Precautions in preparation
of questionnaire and collection of data. Measurement &
Scaling – Attitude Measurement, Sampling Methods –
Probabilistic &Non Probabilistic Sampling, Sample
Design & Procedures- Sample size Estimation, etc.
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3. Sampling
Sampling refers to the process of selecting a subset
of individuals or items from a larger population to
gather information or make inferences about the
entire population. It is a common technique used in
various fields, including statistics, market research,
and scientific studies.
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4. Sampling Procedure
1. Define the Population: Clearly define the target population for the study. The population
is the group of individuals or items that the researcher wants to make inferences about.
2. Determine the Sampling Method: Choose the appropriate sampling method based on
the research objectives and the characteristics of the population. Common methods
include random sampling, stratified sampling, cluster sampling, and convenience
sampling.
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5. Sampling Procedure
3. Sample Size Determination: Determine the required sample size based on statistical
considerations such as the desired level of precision, confidence level, and variability within
the population. Statistical formulas or sample size calculators can help determine an
appropriate sample size.
4. Sampling Frame: Create a sampling frame, which is a list or representation of all the
individuals or items in the population. The sampling frame should accurately and completely
represent the population.
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6. Sampling Procedure
5. Select the Sample: Use the chosen sampling method to select the sample from the
sampling frame. Ensure that the selection process is random or follows the predetermined
criteria. Random number generators or other randomization techniques may be used to
achieve randomness.
6. Obtain Consent and Participation: If human subjects are involved, obtain informed
consent from the selected individuals before including them in the sample. Provide them with
the necessary information about the study, their rights, and any potential risks or benefits.
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7. Sampling Procedure
7. Data Collection: Collect data from the selected sample using appropriate research
methods such as surveys, interviews, observations, or experiments. Ensure that the data
collection process is standardized and consistent across all sample participants.
8. Analyze the Data: Perform statistical analysis on the collected data to draw conclusions
and make inferences about the population. Use appropriate statistical techniques and
methods based on the research objectives and the nature of the data.
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8. Sampling Procedure
9. Interpretation: Interpret the results of the analysis and draw conclusions about the
population based on the findings from the sample. Recognize any limitations or potential
biases in the sampling procedure and discuss their impact on the generalizability of the
results.
10. Report and Disseminate: Summarize the findings from the sample in a research report
or presentation. Clearly describe the sampling procedure used, including any limitations or
potential biases. Provide recommendations for future research or implications of the findings.
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9. Sample Size
Sample size refers to the number of individuals or items included in a sample selected from a
larger population. Determining an appropriate sample size is crucial for obtaining reliable and
accurate results in research studies. The sample size should be sufficient to provide
meaningful statistical power while balancing practical considerations such as time, resources,
and feasibility.
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10. Sample Size
The determination of sample size depends on several factors, including:
1. Desired Level of Precision: The level of precision or margin of error indicates how close
the sample estimate is expected to be to the true population parameter. A smaller margin
of error requires a larger sample size.
2. Confidence Level: The confidence level represents the level of certainty desired in the
results. Commonly used confidence levels are 95% and 99%. Higher confidence levels
require larger sample sizes.
3. Population Variability: The variability or dispersion of the population data affects the
sample size. A larger variability requires a larger sample size to obtain a reliable
estimate.
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11. Sample Size
3. Effect Size (for hypothesis testing): In hypothesis testing studies, the effect size refers to
the magnitude of the difference or relationship between variables being investigated. A larger
effect size typically requires a smaller sample size to detect the effect with adequate power.
4. Statistical Power: Power is the probability of detecting a true effect or relationship when it
exists. Researchers usually aim for a high level of statistical power, such as 80% or 90%,
which requires a larger sample size.
5. Research Design and Analysis: The specific research design and analysis techniques
being used may influence the required sample size. Different statistical tests or models may
have different requirements for sample size calculations.
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12. Sampling Method
1. Random Sampling: In random sampling, each
individual or item in the population has an equal chance
of being selected. To determine sample members,
researchers typically use a randomization technique,
such as a random number generator or a table of
random numbers. This ensures that the selection
process is unbiased and eliminates any potential for
researcher bias.
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13. Sampling Method
2. Stratified Sampling: In stratified sampling, the
population is divided into subgroups or strata based on
certain characteristics. Sample members are then
selected from each stratum in proportion to their
representation in the population. The determination of
sample members involves identifying the appropriate
number of individuals to be selected from each stratum
and using random sampling within each stratum.
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14. Sampling Method
3. Cluster Sampling: Cluster sampling involves dividing
the population into clusters or groups, such as
geographic regions or schools, and selecting a random
sample of clusters. All individuals within the selected
clusters become sample members. The determination of
sample members in cluster sampling involves randomly
selecting the clusters and including all individuals within
those clusters.
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15. Sampling Method
4. Convenience Sampling: Convenience sampling
involves selecting individuals who are easily accessible
or convenient to the researcher. Sample members are
determined based on convenience and availability rather
than a random process. While this method is quick and
easy, it may introduce biases and may not be
representative of the population.
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16. Sampling Method
5. Purposive Sampling: Purposive sampling involves
selecting sample members based on specific criteria
determined by the research objectives. Researchers
purposefully choose individuals who possess the desired
characteristics or knowledge relevant to the study. The
determination and selection of sample members in
purposive sampling are based on the researcher's
judgment and expertise.
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17. Sampling Method
6. Snowball Sampling: Snowball sampling is commonly
used when studying hard-to-reach or hidden
populations. The initial sample members are determined
by the researcher, often based on specific criteria. These
participants then help identify and refer additional
individuals who meet the criteria, creating a "snowball"
effect. The determination and selection of sample
members in snowball sampling rely on referrals and
recommendations from existing sample members.
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18. Types of Data
1. Categorical Data: Categorical data, also known as qualitative or nominal data, represents
characteristics or attributes that can be divided into categories or groups. The categories
are typically non-numerical and represent different qualities or characteristics. Examples of
categorical data include gender (male/female), marital status (single/married/divorced), and
eye color (blue/brown/green).
2. Ordinal Data: Ordinal data is similar to categorical data but has an inherent order or
ranking among the categories. The categories can be ranked or ordered based on some
characteristic or attribute, but the differences between the categories may not be uniformly
measurable. Examples of ordinal data include survey responses such as rating scales
(e.g., Likert scales) where participants choose options like "strongly agree," "agree,"
"neutral," "disagree," "strongly disagree."
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19. Types of Data
3. Numerical Data: Numerical data, also known as quantitative data, represents quantities or
numerical values that can be measured or counted. Numerical data can be further classified
into two types:
a. Discrete Data: Discrete data represents values that are distinct and separate, usually whole
numbers or counts. Discrete data cannot take on values between the defined points. Examples
include the number of children in a family (1, 2, 3, etc.) or the number of cars in a parking lot (0,
1, 2, etc.).
b. Continuous Data: Continuous data represents values that can take on any numeric value
within a range. Continuous data is typically obtained through measurement and can have
infinite possible values. Examples of continuous data include height, weight, temperature, and
time.
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20. Types of Data
4. Interval Data: Interval data is a type of numerical data where the intervals between values
are equal and meaningful. It has a numeric scale with equally spaced intervals, but it lacks a
true zero point. Examples of interval data include temperature measured in Celsius or
Fahrenheit, where zero represents an arbitrary starting point.
5. Ratio Data: Ratio data is similar to interval data but has a true zero point, meaning that a
value of zero indicates the absence of the quantity being measured. In ratio data, ratios and
proportions have meaningful interpretations. Examples of ratio data include weight, length, and
income.
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21. Data Collection Methods
1. Surveys: Surveys involve gathering data by asking questions to a sample of individuals or
groups. Surveys can be conducted through questionnaires (administered in person, via mail, or
online) or through interviews (in-person, phone, or online). Surveys are widely used for collecting
self-reported data on opinions, attitudes, behaviors, and demographic information.
2. Observations: Observational methods involve systematically watching and recording behaviors,
events, or phenomena. Researchers can directly observe and document behaviors in natural
settings (naturalistic observation) or create controlled environments for observation (controlled
observation). Observational data collection can be conducted through structured or unstructured
observations, and it is useful for studying social interactions, behaviors, and natural phenomena.
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22. Data Collection Methods
3. Experiments: Experimental methods involve manipulating variables and observing their effects
on outcomes. In experiments, researchers assign participants to different conditions or treatments
and measure the response. This method allows researchers to establish cause-and-effect
relationships. Experiments can be conducted in laboratory settings or in natural settings, depending
on the research context.
4. Interviews: Interviews involve conducting one-on-one or group discussions with individuals to
gather detailed information. Interviews can be structured (using a predetermined set of questions),
semi-structured (allowing for some flexibility in questioning), or unstructured (allowing open-ended
exploration of topics). Interviews are useful for collecting in-depth qualitative data and capturing
personal perspectives and experiences.
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23. Data Collection Methods
5. Focus Groups: Focus groups involve gathering a small group of individuals (typically 6-12)
to engage in a guided discussion facilitated by a researcher. Focus groups are useful for
exploring opinions, attitudes, and perceptions on a specific topic. The interaction among
participants often generates rich qualitative data and insights.
6. Document Analysis: Document analysis involves examining existing documents, records,
reports, or other written or electronic materials to extract relevant information. This method is
often used in historical research, content analysis, policy analysis, or analyzing organizational
documents.
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24. Data Collection Methods
7. Case Studies: Case studies involve in-depth examination and analysis of a specific
individual, group, organization, or event. Researchers collect data through various methods,
such as interviews, observations, document analysis, and existing records. Case studies
provide detailed qualitative or quantitative data about a specific context or phenomenon.
8. Secondary Data Analysis: Secondary data analysis involves using existing data collected
for another purpose. Researchers analyze and interpret the data to answer their research
questions. Secondary data can be obtained from sources like government agencies, research
organizations, surveys, or publicly available datasets.
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25. Preparation of Questionnaire
1. Define the Objectives: Clearly define the objectives and research questions that the
questionnaire aims to address. This will guide the development of relevant and focused questions.
2. Determine the Question Types: Identify the types of questions that will best suit your research
objectives. Common question types include closed-ended (multiple-choice, rating scales), open-
ended (text-based responses), and a combination of both.
3. Structure the Questionnaire: Organize the questionnaire in a logical and coherent manner.
Start with an introduction that provides context and instructions for respondents. Group related
questions together and consider using subheadings or sections to enhance readability.
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26. Preparation of Questionnaire
4. Start with Demographic Questions: Begin the questionnaire with demographic questions (e.g.,
age, gender, education level) to collect basic respondent information. These questions are typically
closed-ended and help categorize and analyze the data later.
5. Use Clear and Concise Language: Ensure that the questions are clear, concise, and easily
understood by respondents. Use simple language and avoid jargon or technical terms that may
confuse participants. Avoid ambiguous or leading questions that may bias responses.
6. Be Objective and Neutral: Maintain objectivity in the wording of questions to avoid influencing
respondents' answers. Use neutral language and avoid introducing any bias or opinion in the
questions.
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27. Preparation of Questionnaire
7. Sequence the Questions Thoughtfully: Arrange the questions in a logical order. Start with
general and less sensitive questions, then progress to more specific and potentially sensitive
questions. This helps create a natural flow and reduces respondent fatigue.
8. Consider Response Options: For closed-ended questions, provide response options that cover
all possible answer choices. Make sure the options are mutually exclusive and collectively
exhaustive, meaning respondents can choose one option that best represents their answer.
9. Test and Revise: Pilot test the questionnaire with a small group of participants to identify any
potential issues or areas for improvement. Revise and refine the questionnaire based on feedback
received to enhance clarity, understandability, and relevance.
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28. Preparation of Questionnaire
10. Include an Ending Statement: Conclude the questionnaire with a closing statement, thanking
the respondents for their participation and emphasizing the importance of their responses.
11. Ethical Considerations: Consider ethical considerations, such as ensuring confidentiality and
obtaining informed consent if required. Include a statement about the purpose of the study, how the
data will be used, and any measures taken to protect participants' privacy.
12. Formatting and Design: Pay attention to the formatting and design of the questionnaire. Use a
clean and organized layout with consistent formatting for questions and response options. Ensure
the questionnaire is visually appealing and easy to navigate.
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29. Here's an example of a sample questionnaire that focuses on customer satisfaction with a hypothetical restaurant:
Title: Customer Satisfaction Survey
Introduction:
Thank you for dining at XYZ Restaurant! We greatly value your feedback to help us improve our services. Please take a few minutes to complete this survey. All
responses will remain confidential.
Section 1: Demographic Information
Gender:
a) Male
b) Female
c) Prefer not to say
Age:
a) 18-25
b) 26-35
c) 36-45
d) 46-55
e) 56 and above
How often do you visit XYZ Restaurant?
a) First-time visitor
b) Occasionally (once every few months)
c) Regularly (once a month or more)
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30. Section 2: Dining Experience
4. How would you rate the overall ambiance of the
restaurant?
a) Excellent
b) Good
c) Average
d) Poor
Please rate the friendliness and professionalism of our
staff:
a) Excellent
b) Good
c) Average
d) Poor
Did you find the menu options diverse and appealing?
a) Yes
b) No
c) Not sure
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How satisfied were you with the quality of the food?
a) Very satisfied
b) Satisfied
c) Neutral
d) Dissatisfied
e) Very dissatisfied
Did your food arrive promptly and at the right temperature?
a) Yes, both promptly and at the right temperature
b) Promptly, but not at the right temperature
c) At the right temperature, but not promptly
d) Neither promptly nor at the right temperature

31. Section 3: Customer Service
9. How likely are you to recommend XYZ Restaurant to
others?
a) Very likely
b) Likely
c) Neutral
d) Unlikely
e) Very unlikely
How responsive were our staff to your requests or
concerns?
a) Very responsive
b) Responsive
c) Neutral
d) Not very responsive
e) Not responsive at all
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Did our staff make you feel valued and appreciated as a
customer?
a) Yes, definitely
b) Yes, to some extent
c) Neutral
d) No, not really
e) No, not at all
Section 4: Additional Feedback
12. Please share any specific positive experiences or
outstanding staff members you encountered during your
visit.
Please provide any suggestions or improvements you
would like to see at XYZ Restaurant.
Thank you for taking the time to complete this survey. Your
feedback is invaluable to us.

32. Precautions in preparation of
questionnaire and collection of data
• Clearly Define Research Objectives: Clearly define the research objectives and ensure
that the questionnaire aligns with these objectives. This will help ensure that the questions
are relevant and focused on obtaining the necessary information.
• Pilot Testing: Before administering the questionnaire to the target population, conduct a pilot
test with a small group of individuals who are similar to the intended respondents. Pilot
testing helps identify any ambiguities, confusing questions, or potential issues with the
questionnaire's design, layout, or instructions. Revise and refine the questionnaire based on
the feedback received.
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33. Precautions in preparation of
questionnaire and collection of data
• Use Clear and Unambiguous Language: Ensure that the language used in the
questionnaire is clear, unambiguous, and easily understandable by the respondents. Avoid
jargon or technical terms that may confuse participants. Ambiguous or vague questions can
lead to inconsistent or inaccurate responses.
• Avoid Leading or Biased Questions: Be cautious of including leading or biased questions
that may influence respondents' answers. Use neutral language and avoid assumptions or
preconceived notions. The wording of the questions should be objective and not steer
respondents towards a particular response.
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34. Precautions in preparation of
questionnaire and collection of data
• Consider Question Order and Flow: Organize the questions in a logical and coherent
order. Start with general and non-sensitive questions to ease respondents into the survey.
Group related questions together, use appropriate transitions, and consider the flow of the
questionnaire to maintain respondent engagement and minimize fatigue.
• Minimize Response Burden: Keep the questionnaire concise and minimize the number of
questions to reduce respondent burden. Long and time-consuming surveys may lead to
lower response rates and increased respondent fatigue, affecting data quality. Prioritize
essential questions and avoid unnecessary repetition.
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35. Precautions in preparation of
questionnaire and collection of data
• Maintain Data Confidentiality: Ensure that respondents' privacy and confidentiality are
protected. Clearly state how the data will be used and assure respondents that their
responses will remain anonymous and confidential. Follow applicable data protection and
privacy regulations.
• Obtain Informed Consent: If required, obtain informed consent from participants before
collecting their data. Clearly explain the purpose of the study, the voluntary nature of
participation, and any potential risks or benefits. Provide contact information for questions or
concerns.
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36. Precautions in preparation of
questionnaire and collection of data
• Consider Sampling Bias: Be aware of potential sampling bias that may occur due to the
selection of participants. Use appropriate sampling techniques to ensure representative samples
and minimize the risk of bias. If using convenience sampling, acknowledge its limitations and
consider how they may affect the generalizability of the results.
• Data Validation and Cleaning: After data collection, conduct thorough data validation and
cleaning processes. Review the responses for completeness, accuracy, and consistency. Address
any missing or inconsistent data to ensure the quality and reliability of the collected data.
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37. Precautions in preparation of
questionnaire and collection of data
• Consider Sampling Bias: Be aware of potential sampling bias that may occur due to the
selection of participants. Use appropriate sampling techniques to ensure representative samples
and minimize the risk of bias. If using convenience sampling, acknowledge its limitations and
consider how they may affect the generalizability of the results.
• Data Validation and Cleaning: After data collection, conduct thorough data validation and
cleaning processes. Review the responses for completeness, accuracy, and consistency. Address
any missing or inconsistent data to ensure the quality and reliability of the collected data.
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39. Measurement & Scaling
• Nominal Scale: This is the simplest form of measurement where
variables are categorized into distinct groups or categories. Nominal
scales only allow for classification and identification of different
groups, but they do not imply any quantitative relationship between
the categories.
• Nominal measurement is the simplest level of measurement. It
involves assigning numbers to objects or events simply to identify
them. For example, you could assign the numbers 1, 2, and 3 to
three different types of fruit (apples, oranges, and bananas).Marital
status: Single, Married, Divorced, Widowed.
• Type of car: Sedan, SUV, Hatchback, Pickup.
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40. Measurement & Scaling
• Ordinal Scale: Ordinal measurement allows you to rank objects or events
in order. For example, you could rank the three types of fruit in order of
preference (apples, oranges, bananas).
• Ordinal scales involve variables that can be ordered or ranked but do not
have consistent intervals between the categories. The difference between
the categories may not be equal or measurable. For example, in a survey,
respondents may be asked to rate their satisfaction with a product on a
scale of "very unsatisfied," "unsatisfied," "neutral," "satisfied," and "very
satisfied." While there is an order to the categories, the difference between
"unsatisfied" and "neutral" may not be the same as the difference between
"neutral" and "satisfied.“
• Educational attainment: High School Diploma, Bachelor's Degree,
Master's Degree, Ph.D.
• Likert scale responses: Strongly Disagree, Disagree, Neutral, Agree,
Strongly Agree.
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41. Measurement & Scaling
• Interval Scale: Interval measurement allows you to rank objects or events
in order and to measure the difference between them. For example, you
could measure the temperature of three different cups of coffee (100
degrees Fahrenheit, 120 degrees Fahrenheit, and 140 degrees
Fahrenheit).
• Interval scales possess ordered categories with equal intervals between
them. However, interval scales do not have a true zero point. Common
examples include temperature measured in Celsius or Fahrenheit, where
zero does not represent the absence of temperature. In interval scales, it is
possible to calculate the differences between values, but ratios are not
meaningful.
• Temperature in Celsius: 10°C, 20°C, 30°C, 40°C.
• IQ scores: 90, 100, 110, 120.
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42. Measurement & Scaling
• Ratio Scale: It allows you to rank objects or events in order, to
measure the difference between them, and to compare their relative
sizes. For example, you could measure the weight of three different
bags of groceries (1 pound, 2 pounds, and 3 pounds).
• Ratio scales have ordered categories with equal intervals and a true
zero point that represents the absence of the variable being
measured. In ratio scales, both differences and ratios between
values are meaningful. Examples of ratio scales include age, weight,
height, and time.
• Height in centimeters: 150 cm, 160 cm, 170 cm, 180 cm.
• Weight in kilograms: 50 kg, 60 kg, 70 kg, 80 kg.
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43. Attitude Measurement
• Attitude measurement refers to the process of assessing and
quantifying individuals' attitudes, opinions, or beliefs towards a
particular object, topic, or situation. Attitudes are subjective
evaluations that can range from positive to negative or be
somewhere in between. Proper measurement of attitudes is
crucial for understanding individuals' preferences, opinions, and
behaviors.
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44. Attitude Measurement
• Likert Scale: The Likert scale is a widely used method for measuring attitudes. It
consists of a series of statements or items to which respondents indicate their
level of agreement or disagreement on a scale, typically ranging from "Strongly
Disagree" to "Strongly Agree." The responses are assigned numerical values,
allowing for quantitative analysis. For example:
Q. "I believe climate change is a significant global issue."
• Strongly Disagree
• Disagree
• Neutral
• Agree
• Strongly Agree
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45. Attitude Measurement
• Semantic Differential Scale: This scale measures the connotative
meaning associated with an attitude object. Respondents rate the
object on a series of bipolar adjectives anchored at opposite ends of
a continuum. For example:
Q. Attitude towards a new smartphone:
• Cheap -------- Expensive
• Unreliable -------- Reliable
• Outdated -------- Modern
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46. Attitude Measurement
• Thurstone Scale: This scale requires a panel of judges to
evaluate a large number of statements regarding the attitude
object. The judges rate each statement on a numerical scale,
and statements with similar average ratings are grouped
together to create the scale. Respondents then indicate their
agreement or disagreement with each statement. The scale
scores are calculated based on the judges' ratings. This method
is more complex and less commonly used than Likert and
semantic differential scales.
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47. Attitude Measurement
For example, let's say you want to measure people's attitudes towards the
death penalty. You could create a Thurstone scale with a series of
statements like the following:
• The death penalty is a just punishment for certain crimes. (1)
• The death penalty is a cruel and unusual punishment. (11)
• The death penalty deters crime. (7)
• The death penalty is not an effective deterrent to crime. (3)
Once you have created your list of statements, you need to have a group of
people rate each statement on a scale of 1 to 11. The average of the ratings
for each statement will then be used to create a scale score for the person.
For example, if the average rating for the statement "The death penalty is a
just punishment for certain crimes" is 6, then this statement would be
considered to be moderately favorable towards the death penalty.
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48. Attitude Measurement
Visual Analog Scale (VAS): The VAS involves a horizontal line with
anchors representing opposing attitudes. Respondents mark on the
line to indicate their position along the continuum. This scale allows
for the measurement of intensity or degree of an attitude rather than
a simple agreement or disagreement.
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49. Probabilistic Sampling:
• Probabilistic sampling involves selecting a sample from a population in a way that gives
every individual in the population a known and non-zero chance of being included in the
sample. It is characterized by random selection and allows for the estimation of sampling
error and generalization of the findings to the population as a whole. Common
probabilistic sampling methods include:
• a. Simple Random Sampling: Every member of the population has an equal chance of
being selected. For example, using a random number generator to select participants
from a list of all population members.
• b. Stratified Random Sampling: The population is divided into distinct strata based on
certain characteristics (e.g., age, gender, location), and random samples are drawn from
each stratum in proportion to their representation in the population.
• c. Cluster Sampling: The population is divided into clusters or groups, and a random
selection of clusters is chosen. All members within the selected clusters are included in
the sample.
• d. Systematic Sampling: Researchers select every nth individual from the population
after randomly selecting a starting point. For instance, every 10th person on a list is
selected after randomly picking a number between 1 and 10.
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50. Non-Probabilistic Sampling:
• Non-probabilistic Sampling:
• Non-probabilistic sampling, also known as non-random or
convenience sampling, involves selecting individuals based on
convenience or availability, rather than random selection. Non-
probabilistic sampling methods are often used when it is difficult
or impractical to obtain a truly random sample. While non-
probabilistic sampling may be less rigorous and subject to
selection biases, it can still provide valuable insights in certain
situations.
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51. Non-Probabilistic Sampling:
• a. Convenience Sampling: Researchers select participants who are
readily available or easily accessible. This method is convenient but
may introduce biases as it relies on a non-random selection process.
• b. Purposive Sampling: Researchers select participants who meet
specific criteria or have characteristics relevant to the research study.
This method is useful when seeking specific expertise or unique
perspectives, but it may not be representative of the broader
population.
• c. Snowball Sampling: Participants initially selected refer or
nominate others who meet the criteria of the study. This method is
commonly used when studying hard-to-reach or hidden populations.
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52. Sample size estimation
• Research Objectives: Clearly define the research objectives and
the specific hypotheses or research questions that need to be
answered. The sample size should be determined based on the
effect size or difference that you expect to detect in your analysis.
• Statistical Power: Statistical power refers to the probability of
correctly rejecting a false null hypothesis (i.e., detecting a true effect)
if it exists. Higher statistical power is desirable as it reduces the
chances of Type II error (false negatives). Determine the desired
level of statistical power (commonly set at 80% or 90%) to detect the
effect size of interest.
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53. Sample size estimation
• Significance Level: Specify the desired level of significance (alpha),
which represents the probability of making a Type I error (false
positive) by rejecting a true null hypothesis. Commonly used values
are 0.05 or 0.01, indicating a 5% or 1% chance of making a Type I
error, respectively.
• Effect Size: The effect size represents the magnitude of the
relationship or difference you expect to observe in your study. It is
typically based on previous research findings, pilot studies, or
theoretical considerations. Larger effect sizes require smaller sample
sizes to achieve adequate power.
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54. Sample size estimation
• Variability and Precision: Consider the variability or dispersion of
the data in your target population. Greater variability requires larger
sample sizes to achieve accurate estimates. Additionally, determine
the desired level of precision or margin of error around your
estimated parameter.
• Statistical Analysis: The type of statistical analysis you plan to
conduct also influences sample size estimation. Different analyses,
such as t-tests, chi-square tests, or regression models, have specific
requirements for sample size calculation. Consult statistical
references or consult with a statistician to determine the appropriate
calculations for your specific analysis.
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55. Sample size estimation
• Population Characteristics: Consider the size and
homogeneity of the target population. Larger populations may
require larger sample sizes to adequately represent the
diversity of the population. Homogeneity within the population
may allow for smaller sample sizes, while heterogeneity may
require larger samples.
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56. • Here are a few examples of objective-type questions commonly
used in data collection, along with their possible answers:
1.Multiple-Choice Questions:
• Question: Which of the following is the most common
programming language? A) Python B) Java C) C++ D)
JavaScript
2.True/False Questions:
• Question: The Earth revolves around the Sun. True or False?
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57. 3.Likert Scale Questions:
• Question: On a scale of 1 to 5, how satisfied are you with the
customer service provided by our company? 1 - Very
Dissatisfied 2 - Dissatisfied 3 - Neutral 4 - Satisfied 5 - Very
Satisfied
4.Matching Questions:
• Question: Match the country with its capital: A) France B)
Germany C) Japan
1.Paris
2.Tokyo
3.Berlin
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58. 5.Fill in the Blank Questions:
• Question: The capital of Australia is ___________.
6. Ranking Questions:
• Question: Please rank the following features in order of
importance, with 1 being the most important and 4 being the
least important:
A) Price B) Quality C) Convenience D) Brand Reputation
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