A Brief Guide to Questionnaire Development, Robert B. Frary
1. Defining the research question: Before developing a questionnaire, it's important to
clearly define the research question that the questionnaire will address. This will help
ensure that the questionnaire is relevant and focused.
2. Identifying the target population: The target population for the questionnaire should
be clearly defined, as this will influence the design and content of the questionnaire.
3. Choosing the appropriate question format: The guide discusses the pros and cons of
different question formats, such as open-ended questions, closed-ended questions,
and rating scales.
4. Drafting and testing the questionnaire: The guide provides tips on how to draft clear
and concise questions, as well as how to pre-test the questionnaire to ensure that it
is easy to understand and answer.
5. Administering the questionnaire: The guide discusses the different methods for
administering questionnaires, such as online surveys, telephone interviews, and
face-to-face interviews.
6. Analyzing the data: Finally, the guide briefly touches on the importance of data
analysis and the different methods that can be used to analyze questionnaire data.
, Sampling Methods for Web and E-Mail Surveys" by Ronald D. Fricker
1. Population and sampling frame: The guide emphasizes the importance of defining
the target population and sampling frame for the survey, and provides tips on how to
select a representative sample. This involves clearly defining the characteristics of
the population, such as age, gender, location, education level, etc. The sampling
frame is the list of individuals from which the sample will be drawn, and it should be
representative of the target population.
2. Probability sampling methods, such as
a. simple random sampling
Advantages:
● Every member of the population has an equal chance of being selected for the
sample.
● It's straightforward to implement and requires little knowledge of the population.
● It produces results that are easy to analyze and generalize to the larger population.
Limitations:
● It can be inefficient and costly if the population is large and dispersed.
● It may not be suitable if the population is not homogeneous and there is a need to
oversample certain subgroups.
b. Stratified sampling
Advantages:
● It ensures that each stratum or subgroup of the population is represented in the
sample proportionally to its size in the population.
● It can improve the precision of the estimates compared to simple random sampling.
● It can provide more accurate estimates for subgroups of interest.
Limitations:
● It requires knowledge of the population to stratify it properly.
● It can be more complex and time-consuming to implement than simple random
sampling.
● It may be less effective if the strata are not clearly defined or if there is a large
variability within the strata
c. Cluster sampling
Advantages:
● It can be more efficient and cost-effective if the population is large and dispersed, as
it involves selecting clusters of individuals rather than individual members.
● It's easier to implement than stratified sampling.
● It can provide useful information about geographic and organizational clusters.
Limitations:
● It can introduce additional sources of variability compared to simple random sampling
or stratified sampling.
● It may require larger sample sizes to achieve the same level of precision as simple
random sampling or stratified sampling.
● It may be less effective if the clusters are not homogeneous or if there is a large
variability within the clusters.
3. Non-probability sampling methods, such as convenience sampling and snowball
sampling, may be used if probability sampling methods are not feasible or practical,
but they may introduce bias. Selection bias:
1. Defining the research question: Before developing a questionnaire, it's important to
clearly define the research question that the questionnaire will address. This will help
ensure that the questionnaire is relevant and focused.
2. Identifying the target population: The target population for the questionnaire should
be clearly defined, as this will influence the design and content of the questionnaire.
3. Choosing the appropriate question format: The guide discusses the pros and cons of
different question formats, such as open-ended questions, closed-ended questions,
and rating scales.
4. Drafting and testing the questionnaire: The guide provides tips on how to draft clear
and concise questions, as well as how to pre-test the questionnaire to ensure that it
is easy to understand and answer.
5. Administering the questionnaire: The guide discusses the different methods for
administering questionnaires, such as online surveys, telephone interviews, and
face-to-face interviews.
6. Analyzing the data: Finally, the guide briefly touches on the importance of data
analysis and the different methods that can be used to analyze questionnaire data.
, Sampling Methods for Web and E-Mail Surveys" by Ronald D. Fricker
1. Population and sampling frame: The guide emphasizes the importance of defining
the target population and sampling frame for the survey, and provides tips on how to
select a representative sample. This involves clearly defining the characteristics of
the population, such as age, gender, location, education level, etc. The sampling
frame is the list of individuals from which the sample will be drawn, and it should be
representative of the target population.
2. Probability sampling methods, such as
a. simple random sampling
Advantages:
● Every member of the population has an equal chance of being selected for the
sample.
● It's straightforward to implement and requires little knowledge of the population.
● It produces results that are easy to analyze and generalize to the larger population.
Limitations:
● It can be inefficient and costly if the population is large and dispersed.
● It may not be suitable if the population is not homogeneous and there is a need to
oversample certain subgroups.
b. Stratified sampling
Advantages:
● It ensures that each stratum or subgroup of the population is represented in the
sample proportionally to its size in the population.
● It can improve the precision of the estimates compared to simple random sampling.
● It can provide more accurate estimates for subgroups of interest.
Limitations:
● It requires knowledge of the population to stratify it properly.
● It can be more complex and time-consuming to implement than simple random
sampling.
● It may be less effective if the strata are not clearly defined or if there is a large
variability within the strata
c. Cluster sampling
Advantages:
● It can be more efficient and cost-effective if the population is large and dispersed, as
it involves selecting clusters of individuals rather than individual members.
● It's easier to implement than stratified sampling.
● It can provide useful information about geographic and organizational clusters.
Limitations:
● It can introduce additional sources of variability compared to simple random sampling
or stratified sampling.
● It may require larger sample sizes to achieve the same level of precision as simple
random sampling or stratified sampling.
● It may be less effective if the clusters are not homogeneous or if there is a large
variability within the clusters.
3. Non-probability sampling methods, such as convenience sampling and snowball
sampling, may be used if probability sampling methods are not feasible or practical,
but they may introduce bias. Selection bias:
