The document discusses sampling and analysis methods from an environmental science perspective. It defines a sample as a subset of a population that is used to make inferences about the entire population. There are two main types of sampling: probability sampling which uses random selection, and non-probability sampling which does not use random selection. Some specific sampling methods discussed include random sampling, stratified sampling, systematic sampling, cluster sampling, convenience sampling, purposive sampling, quota sampling, and snowball sampling. Ethical considerations for data collection like informed consent and confidentiality are also covered.
Making Sense of It All: Analyzing Qualitative DataGeorge Hayhoe
Qualitative methodologies are becoming increasingly important in our discipline. Because they are based on techniques that technical communicators commonly use, everyone in the profession finds these methods familiar and understandable.
This workshop will draw on that familiarity and comprehension to show practitioners how to analyze and interpret the data collected from interviews, focus groups, open-ended questionnaires, and communication artifacts. The workshop is based on simple, proven methods that produce meaningful results that can be used to inform decisions about product design and delivery.
First, the moderators will review examples of qualitative methods and data. Then, the moderators will explain how to organize data for analysis. Finally, the moderators will describe Content Analysis, a technique for analyzing and interpreting the data.
With this background, participants will work in teams to analyze and interpret data using Content Analysis. Then, the teams will report the results of their analysis and interpretation.
Making Sense of It All: Analyzing Qualitative DataGeorge Hayhoe
Qualitative methodologies are becoming increasingly important in our discipline. Because they are based on techniques that technical communicators commonly use, everyone in the profession finds these methods familiar and understandable.
This workshop will draw on that familiarity and comprehension to show practitioners how to analyze and interpret the data collected from interviews, focus groups, open-ended questionnaires, and communication artifacts. The workshop is based on simple, proven methods that produce meaningful results that can be used to inform decisions about product design and delivery.
First, the moderators will review examples of qualitative methods and data. Then, the moderators will explain how to organize data for analysis. Finally, the moderators will describe Content Analysis, a technique for analyzing and interpreting the data.
With this background, participants will work in teams to analyze and interpret data using Content Analysis. Then, the teams will report the results of their analysis and interpretation.
This course has been designed to equip the student with the basic sampling methods in health sciences. The course aims to impart basic knowledge on sample size, sample selection, etc.
Statistical analysis, presentation on Data Analysis in Research.Leena Gauraha
presentation on Data Analysis in Research, Meaning of Data analysis, Objectives & Steps of Data analysis, Types of Data analysis, Benefits to Business from Data analysis, Data Interpretation Methods in Data analysis.
This course has been designed to equip the student with the basic sampling methods in health sciences. The course aims to impart basic knowledge on sample size, sample selection, etc.
Statistical analysis, presentation on Data Analysis in Research.Leena Gauraha
presentation on Data Analysis in Research, Meaning of Data analysis, Objectives & Steps of Data analysis, Types of Data analysis, Benefits to Business from Data analysis, Data Interpretation Methods in Data analysis.
The growth leads to the depletion of natural resources of the planet. One of them is wood. We use unnecessary paper! Too much mess! Beware of CO2 imbalance... The immediate solution to stop destroying forests: dematerialization of exchanges with legal convincing value. Zero paper! The electronic originals are sealed and encrypted in a nominative and communicating electronic safe. The identification of counterparts is made via Magicaxess, a new high tech of identification WITHOUT having to download a digital certificate!
The Department of Environment has approved this faulty EIA submitted by the Power Development Board. The project would be implemented by the governments of Bangladesh and India.
This was a presentation that was carried out in our research method class by our group. It will be useful for PHD and master students quantitative and qualitative method. It consist sample definition, purpose of sampling, stages in the selection of a sample, types of sampling in quantitative researches, types of sampling in qualitative researches, and ethical Considerations in Data Collection.
Sampling means selecting the group that researcher will actually collect data from in research. It attempts to collect samples that are representative of the population.
Understanding The Sampling Design (Part-II)DrShalooSaini
This Power Point Presentation has been made while referring to the research books written by eminent, renowned and expert authors as mentioned in the references section. The purpose of this Presentation is to help the research students in developing an insight about the Sampling Design(Part-II).
Meaning & Definition of Population & Sampling, Types of Sampling - Probability & Non-Probability Sampling Techniques, Characteristics of Probability Sampling Techniques, Types of Probability Sampling Techniques, Characteristics of Non-Probability Sampling Techniques, Types of Non-Probability Sampling Techniques, Errors in Sampling, Size of sample, Application of Sampling Technique in Research
Explains the different methods of Sampling with diagram. In statistics, quality assurance, and survey methodology, sampling is the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Statisticians attempt for the samples to represent the population in question.
1. Department of
Department of Environmental Science
State University of Bangladesh
Masters in Environmental Science Program
ES 522: Environmental Analysis and Its
Interpretation
Lecture 4:
Sampling and Analysis of Results: An
Environmental Perspectives
2. Lecture Outline:
What is Sample? How to define Sample from population?
What is Sampling?
Sampling in Research..
Ethical Considerations in Data Collection
3. What is Sample?
A sample is the group of people who take part in the investigation.
The people who take part are referred to as “participants”.
Population
Sample
A sample is “a smaller (but hopefully representative) collection of
units from a population used to determine truths about that
population” (Field, 2005)
4. How to define Sample Size from population?
The Statistical equation for defining the sample size:
n0= t2pq/d2
Where,
n0= the desired sample size (when population is greater than 10,000)
t= the standard normal deviate set at 1.96, which corresponds to the 95
percent confidence level (at P<0.05)
p= the proportion in the target population estimated to the prevalence
rate of violence and discrimination incidence (set at 60%)
q= 1.0 - p.
d= Degree of accuracy desired, set at .05
n= (1.96)2 x (.60)(.40)/(.05)2= 368
For adjustment of sample population, we can apply:
n
nc = -------------
1+n/N
Where, N =20,000
Then, the sample size = 361 =360
5. Generally, Sampling is the process of selecting participants from the
population.
What is Sampling?
The process of selecting a number of individuals for a study in such
a way that the individuals represent the larger group from which
they were selected
STUDY POPULATION
TARGET POPULATION
SAMPLE
SAMPLING…….
7. The purpose of sampling…
To gather data about the population in order to make an
inference that can be generalized to the population
8. Stages in the Selection of Sample…
Define the Target Population
Select a Sampling Frame
Determine Probability/Non Probability Sample
Plan Procedure for Selecting Sampling Units
Select Actual Sampling Units
Conduct Fieldwork
Determine Sample Size
9. Sampling in Research..
I. Probability Sampling
Sampling
II. Non-Probability Sampling
1. Random Sampling
2. Stratified Sampling
3. Systematic Sampling
4. Cluster Sampling
2. Purposive sampling
3. Quota sampling
1. Convenience sampling
1. Sampling in Quantitative Research
4. Snowball sampling
10. 1. Random sampling
Selecting subjects so that all members of a population have an equal and
independent chance of being selected
Advantages
1. Easy to conduct
2. High probability of achieving a representative sample
3. Meets assumptions of many statistical procedures
Disadvantages
1. Identification of all members of the population can be
difficult
2. Contacting all members of the sample can be difficult
Continuation…
11. 2. Stratified Sampling
The population is divided into two or more groups called
strata, according to some criterion, such as geographic
location, grade level, age, or income, and subsamples are
randomly selected from each strata.
Advantages
More accurate sample
Can be used for both proportional and non-proportional samples
Representation of subgroups in the sample
Disadvantages
Identification of all members of the population can be difficult
Identifying members of all subgroups can be difficult
Continuation…
13. 3. Systematic sampling
Selecting every Kth subject from a list of the members of the
population
Advantage
Very easily done
Disadvantages
subgroups
Some members of the population don’t have an equal chance of being
included
Continuation…
Example, to select a sample of 25 sample in your college dorm, makes a list of
all the student numbers in the classroom. For example there are 100 student,
divide the total number (N) of student (100) by the number of sample (n) you
want in the sample (25). The answer is 4. This means that you are going to
select every 4th student from the list of student in the classroom.
14. Continuation…
3. Cluster sampling
The process of randomly selecting intact groups, not individuals, within
the defined population sharing similar characteristics
Clusters are locations within which an intact group of members of the
population can be found
Examples
Neighborhoods
School districts
Schools
Classrooms
Advantages
Very useful when populations are large and spread over a large
geographic region
Convenient and expedient
Do not need the names of everyone in the population
Disadvantages
Representation is likely to become an issue
15. Example: Cluster Sampling
Note: Cluster samples are frequently utilized when no list of the sample
population are available.
17. 1. Convenience sampling
2. Purposive sampling
…called “judgment” sampling
3. Quota sampling
If the study has potential limited validity of results.
If the study needs equivalent numbers to enable equivalent
analysis and conclusions
Non-probability samples
4. Snowball sampling
A sampling procedure in which initial respondents are selected by probability
methods, and then additional respondents are obtained from information provided
by the initial respondent.
18. Sampling in Qualitative Research
Researchers in qualitative research select their participants
according to their :
1) characteristics
2) knowledge
19.
20. Ethical Considerations in Data Collection
It is the researcher’s ethical responsibility to safeguard the story
teller by maintaining the understood purpose of the research…
The relationship should be based on trust between the researcher
and participants.
Inform participants of the purpose of the study.
Being respectful of the research site, reciprocity, using ethical interview
practices, maintaining privacy, and cooperating with participants.
Patton (2002) offered a checklist of general ethical issues to consider,
such as:
reciprocity
assessment of risk
confidentiality,
informed consent
and data access and ownership.