Discuss the relative advantages & disadvantages of each sampling methods
Samplimg Methods: Descriptive Research
Sampling Methods Descriptive Research
Objectives:
At the completion of this lesson you will be able to:
1. Learn the reasons for sampling 2. Develop an understanding about different sampling methods 3. Distinguish between probability & non probability sampling 4. Discuss the relative advantages & disadvantages of each sampling methods 5. Identifysampling methods in descriptive research. 6. State the purpose of descriptive research. 7. Explain when descriptive research is useful. 8. Distinguish between probability & non probability sampling.
9. Provide examples of descriptive research in nursing
Descriptive Research
Descriptive research is about describing how reality is, can involve collecting quantitative data. Descriptive research seeks to provide an accurate description of observations of behavior and status and cannot make predictions or determine causality. Descriptive research is undertaken to provide answers to questions of who, what, where, when, and how – but not why. Can describe categories
Examples:
What is the average age at which children learn to walk? What % of adult men are unemployed… What is the divorce rate…
Descriptive studies are classified as non experimental or observational designs.
Purpose
Determines, describes and documents the way things are. Determine proportion of people who act a certain way
Classification of Descriptive studies
Classified by how data are collected
Self-report
Individuals respond to statements or questions about themselves
Observation
Data is collected by the researcher watching participants
Study variables In descriptive studies elements, features, or characteristics of persons, experiences, situations, or things studied are referred to as variables.
Statistics Versus Parameters
A parameter is a characteristic of a population. It is a numerical or graphic way to summarize data obtained from the population. A statistic, on the other hand, is a characteristic of a sample. It is a numerical or graphic way to summarize data obtained from a sample.
Types of Numerical Data
There are two fundamental types of numerical data a researcher can collect.
Quantitative data are obtained by determining placement on a scale that indicates amount or degree. Categorical data are data obtained by determining the frequency of occurrences in each of several categories.
Selecting Subjects: Target Population and Sampling
Sampling
The process of selecting units (e.g., people, organizations) from a population of interest for a study. A sample is “a smaller (but hopefully representative) collection of units from a population used to determine truths about that population” (Field, 2005)
Why sample?
Resources (time, money) and workload Gives results with known accuracy that can be calculated mathematically In creating a sample Researchers make choices: Who to collect data from? What to collect data about? How much data to collect?
Important Statistical Points
Population: a set which includes all measurements of interest to the researcher Sampling frame :subset of units that have a chance to become part of the sample Sample: A subset of the population Researchers study the sample to make generalizations back to the population
Types of Samples Non probability sampling:
population elements are selected on the basis of their availability (e.g., because they volunteered) or because of the researcher’s personal judgment that they are representative. Does not rely on random selection Weakens sample-to-population representativeness Used when other techniques will not result in an adequate or appropriate sample Used when researchers desire participants with special experiences or abilities
Nonprobability sampling techniques
Convenience sample:sample is selected from elements of a population that are easily accessible Example: A Nurse educator conducts a research study of the RN-BSN students enrolled in the University and Junior college programs at the institution where the teacher educator is a faculty member. The sample is one of convenience because the RN-BSN students are selected for the study based on their availability to participate.
Snowball sampling: This technique relies on referrals from initial subjects to generate additional subjects.(friend of friend….etc.)
Purposive sampling: The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched
Quota Sampling: Non probability sampling technique in which researchers divide the population into groups and then arbitrarily choose participants from each group
Probability sampling :
All elements (e.g., persons, households) in the population have some opportunity of being included in the sample, and the mathematical probability that any one of them will be selected can be calculated. When probability for selection is equal, selection is random Also known as random sampling Sampling error will always occur
Types of probability sampling
Simple Random Sample:(Simplest and quickest)Each member of the population has an equal and known chance of being selected.
Stratified Random Sample: 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.
Cluster Sample: The population is divided into subgroups (clusters) like families. A simple random sample is taken of the subgroups and then all members of the cluster selected are surveyed.
Sample Size Determined prior to selecting sample
The more heterogeneous a population is, the larger the sample needs to be. For probability sampling, the larger the sample size, the better. With nonprobability samples, not generalizable, still consider stability of results
Addressing generalizability
Extent to which conclusions developed from data collected from sample can be extended to its population Sample is representative to the degree that all units had same chance for being selected Representative sampling eliminates selection bias Representativeness can only be assured through random sampling
Measuring of Variables in Descriptive Studies
Surveys
This involves the collection of data that will provide an account or description of individuals, groups or situations. Instruments we use to obtain data in descriptive studies include
Surveys represent the most common type of self- report measures Allows to infer the current status of an issue
Advantages of conducting surveys
Less time is required Less expenses are incurred Larger samples can be used
Questionnaires
A data-collection instrument that gathers information about participants’ attitudes or beliefs concerning a particular topic based on the degree of intensity that they indicate in their responses.
Interviews
A data-collection method in which the researcher asks questions of individuals or groups and records the participants’ answers. The interviewer usually asks the questions orally in a face- to-face interaction or over the telephone, but electronic interviews administered through e- mail also are possible.
What is Good Data? A true measurement method captures the essence and attributes of what it is intended to measure.
Validity
the extent to which a measure actually measures what it is intended to measure
The truthfulness of a measures.
A test can be reliable and not be valid.
Internal validity:
the extent to which changes in the dependent variable can be attributed to the influence of the independent variable rather than to confounding variables.
Reliability
The consistency and stability of a measure or score.
Analysis of Descriptive Research Descriptive Statistics:
There are two important groups of descriptive statistics:
1. Frequency counts and frequency distributions
A frequency count is the computation of the frequency of a particular subject that fall in a given category. The placement of all the subjects in their categories forms a frequency distribution.
2. Summary statistics
Summary statistics describe the data with just one or two numbers to make comparison of groups easier and also to provide a basis for later analysis when inference would be made.
Percentages
The proportion of participants who obtain a particular score in a frequency distribution.
Example: In the following frequency distribution, 30% of the participants obtained a mathematics score of 55.
Measures of Dispersion
Range The difference between the highest and lowest score in a set of scores or frequency distribution.
Example: The range for the following set of five scores is 5: 9, 10, 10, 12, 14.
Variance is the most commonly used measure of dispersion. It is calculated by taking the average of the squared differences between each value and the mean.
Standard deviation, another commonly used statistic, is the square root of the variance.
Measures on Central Tendency
A score in a set of scores or a frequency distribution that is typical or representative of all the scores. Measures of central tendency are the mean, median and mode.
Mean: In general, the average score in a set of scores or frequency distribution, calculated as the sum of the scores divided by the number of scores.
Example: The mean of the following set of five scores is 11: 9, 10, 10, 12, 14
mean is easily influenced by outliers
Median: The middle score in a set of scores or frequency distribution such that 50% of the scores are at or below the median score.
Example: The median of the following set of five scores is 10: 9, 10, 10, 12, 14.
less influenced by outliers
Mode: The most frequent score in a set of scores or a frequency distribution.
Example: The mode for the following set of five scores is 10: 9, 10, 10, 12, 14.
can have more than one mode