This document discusses key concepts from the class including exam results, sampling methods, and determining sample size. It reviews terms like population, sample, stratified random sampling, and cluster random sampling. It also discusses non-random sampling methods like convenience sampling and determining an appropriate sample size using a sample size calculator.
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in each
Sampling is the process of selecting units (e.g., people, organizations) from a population of interest so that by studying the sample we may fairly generalize our results back to the population from which they were chosen. Let's begin by covering some of the key terms in sampling like "population" and "sampling frame." Then, because some types of sampling rely upon quantitative models, we'll talk about some of the statistical terms used in sampling. Finally, we'll discuss the major distinction between probability and Nonprobability sampling methods and work through the major types in each
By: Jalen Rebolledo and Manilou Allanic
Factors affecting sample selection.
sampling methods and its advantages and disadvantages
Steps on random sampling
Research and Statistics
Sampling- technique of getting a representative portion of a population.
The term population s the entire sum of objects, families, species or orders of plants or animals.
A must see for graduate students. This presentation describes how to conduct common quantitative statistical analyses, interpret the results, and present them in APA format. Dr. James Lani covers both quantitative and qualitative analyses, such as: descriptive statistics, chi-square, pearson correlation, t-test, ANOVA, regression, mediation, and moderation. He also discusses grounded theory and phenomenological analysis
SSP is now Intellectus Statistics Software. Intellectus Statistics™ software primarily serves the academic and research communities as a powerful statistical package that can be purchased via four distinct cloud based subscriptions. Learn more here: http://www.statisticssolutions.com/buy-intellectus/
Methods of collecting data
Survey, methods and type, response rate, variable language
Hands on: Graphical techniques II, SPSS
Questionnaire design
Tips on writing a research paper
Individual project: article critique
Basic Terminologies
Population
Sample and Sampling
Advantages & Disadvantages of Sampling
Probability Sampling
Types of Probability sampling
Non-Probability Sampling
Types of Non-probability sampling
By: Jalen Rebolledo and Manilou Allanic
Factors affecting sample selection.
sampling methods and its advantages and disadvantages
Steps on random sampling
Research and Statistics
Sampling- technique of getting a representative portion of a population.
The term population s the entire sum of objects, families, species or orders of plants or animals.
A must see for graduate students. This presentation describes how to conduct common quantitative statistical analyses, interpret the results, and present them in APA format. Dr. James Lani covers both quantitative and qualitative analyses, such as: descriptive statistics, chi-square, pearson correlation, t-test, ANOVA, regression, mediation, and moderation. He also discusses grounded theory and phenomenological analysis
SSP is now Intellectus Statistics Software. Intellectus Statistics™ software primarily serves the academic and research communities as a powerful statistical package that can be purchased via four distinct cloud based subscriptions. Learn more here: http://www.statisticssolutions.com/buy-intellectus/
Methods of collecting data
Survey, methods and type, response rate, variable language
Hands on: Graphical techniques II, SPSS
Questionnaire design
Tips on writing a research paper
Individual project: article critique
Basic Terminologies
Population
Sample and Sampling
Advantages & Disadvantages of Sampling
Probability Sampling
Types of Probability sampling
Non-Probability Sampling
Types of Non-probability sampling
Sampling is procedure or process of selecting some units from the population with some common characteristics and is primarily concerned with the collection of data of some selected units of the population.
2. Road Map
• Discuss Exam 1
• Quick review of 9/20 class
• Finish Chapter 5 material (sampling)
3. Exam 1
• Will share class statistics next class
– (Brian will enter grades in BbLearn right now)
– Mean on multiple choice = 83%
• Discuss short answer questions
• Any questions about multiple choice?
4. Quick Review
• Scales of measurement
• Reliability
• Validity
• Appropriate use/interpretation of reliability
and validity information
6. Sampling
• Very important part of research methodology
Let’s establish some vocabulary:
• Population- full set of elements that exist
• Sample- a set of elements taken from the
population
• Element- the basic unit of sampling
7. More Lingo
• Sampling method
• Representative sample
• Equal probability of selection method
– (EPSEM)
8. Statistics vs. Parameters
• Statistic
• Parameter
• Sampling Error: Difference between the sample
values and the “true” population value
• illustration
• Inferential Statistics: goal is to draw conclusions
(inferences) about population based on sample
statistics
9. Even More Lingo
• Census
• Response rate: % of people selected to be in
the sample who actually participate in the
study
• What if our response rate is really low?
10. Will the Lingo Never End?
• Biased Sample: A non-representative sample
Hopefully you have:
• Proximal similarity: generalization to people,
places, settings, and contexts similar to those
described in the study
12. Simple Random Sampling
• popular and most basic type of random
sampling
• Think slips of paper in a hat
• With replacement
• Without replacement – preferred
13. Stratified Random Sampling
• Divide the population into mutually exclusive
groups (strata)
• Then select a random sample from each
group
• Stratification variable
• Proportional vs. Disproportional
14. Cluster Random Sampling
• Cluster- collective type of unit that includes
multiple elements (people)
• Examples?
– Schools, classes, families
– clusters are randomly selected
15. Systematic Sampling
1. Determine the sampling interval (k)
2. Randomly select an element between 1 and k
3. Select every kth element.
• Sampling interval- The population size divided
by the desired sample size
– symbolized by the letter k
16. example
• Population N=100
• Desired sample n =10
• k = Population N/sample n = 100/10 = 10
• Step 1 select element between 1 and 10
– we randomly select 7
• Now select every 10th (k) element
• Sample= 7,17,27,37,47,57,67,77,87,97
17. Warning for Systematic Sampling
• Periodicity - problem if there is a cyclical
pattern in the population from which you’re
sampling
Example:
• If I have lists of classes organized by student
grade (highest to lowest) and the length of
each class list is = to k.
• Might always be selecting the A or F students.
18. Nonrandom Sampling
• Weaker method (less representative of
population)
• But sometimes necessary for practical reasons
• Four types
– Convenience sampling
– Quota sampling
– Purposive sampling
– Snowball sampling
19. Convenience Sampling
• Use of people who are readily available,
volunteer, or are easily recruited for inclusion
in a sample
20. Two most common research
participants
• White Rat
• College student
21. Quota Sampling
• Researcher sets quotas
– numbers of the kind of people wanted in the
sample
• Then locates (via convenience sample) the
numbers of people to meet the quotas
23. Snowball Sampling
• When research question requires individuals who are
hard to find
• Example: researching HIV+ white females
• Start with small group who meet criteria
• They spread the word
– “snowball effect”
24. Random Selection vs. Random
Assignment
• Random Selection: select participants for
study
– Purpose: create a representative sample
• Random assignment: place participants in
experimental conditions
– Purpose: create equivalent groups for use in an
experiment
25. How do we determine sample size?
• If population <100 then get them all
• In general, get as big a sample as possible
• Sample size calculator: G*Power
