4130 Chapter 12: Sampling

Card Set Information

Author:
hmijares
ID:
159334
Filename:
4130 Chapter 12: Sampling
Updated:
2012-06-22 14:58:30
Tags:
4130
Folders:

Description:
Sampling
Show Answers:

Home > Flashcards > Print Preview

The flashcards below were created by user hmijares on FreezingBlue Flashcards. What would you like to do?


  1. Chapter 12: Sampling
  2. What is sampling?
    concepts
    *Is a process of selecting representative units of a population for study in a research investigation.

    *A whole population cannot be studied thus a smaller sample must be obtained.

    *To ensure that scientific findings are as accurate or true as possible the researcher must put in controls to match the sample to the larger population.

    *A population is a well-defined set that has  certain specified characteristics from which data can be gathered or analyzed

    • (people, objects, animals, all the children admitted to hospital
    • in Alberta in 2008).
  3. Sampling & Elements
    Sampling: subset of the population that contains elements

    • Elements: basic unit about which information is collected
    • -people
    • -places
    • -objects 
  4. Identifying population descriptors
    inclusion
    exclusion
    examples
    When sample is planned...the researcher must...

    Specify inclusion (eligibility)

    Specify exclusion (delimination) 

    =leads to sample selection

    Examples of descriptors are: 

    gender, age, marital status, socioeconomic status, religion, ethnicity, education, health status, diagnosis, co-morbidities or BSN students. 

    Examples: population descriptors

    Think about the concept of inclusion or eligibility criteria applied to a research study where the subjects are clients. For example, participants in a study investigating the pain experience of children with leukemia during the first year after diagnosis had to meet the following inclusion (eligibility) criteria

    *Age: 4 to 17 years.

    *Diagnosis: acute lymphocytic leukemia within one month of diagnosis.

    *Health status: no other existing chronic illness associated with pain, fulminating disease, or known cognitive disability.

    *Status: ability to cope with the burden of research tasks as determined by the primary nurse.

    *Language: English- or Spanish-speaking.
  5. Homogeneity and Heterogeneity
    Homogeneity: same characteristics in a sample

    Heterogeneity: dissimilar characteristics in a sample. This decreases researcher's ability to increase generizability (↓theresearcher's ability to generalize research findings to the larger population [external validity]) The process is a control to ↓sample bias
  6. Target population & Accessible population
    Target population

    is the entire set of cases about which the researcher would like to make generalizations. An example: all the BSN students enrolled in a BSN program in Canada

    Accessible population:

    meets the researcher's target population  criteria by is available to the researcher. In our example it may be all BSN students enrolled in a BSN program in BC
  7. Representative Sample
    representative sample is one whose key characteristics closely approximate those of the population
  8. 2 types of sampling strategies:
    nonprobability
    probability
    Nonprobability:

    nonrandom. All sample characteristics DO NOT have a chance to be represented

    Probability:

    random selction of the sample units. More likely to be representative of the population. Rigorous sampling strategy
  9. Confused yet? Re-look the terms
    *Randomization or random assignment is the distribution of subjects to either an experimental or a control group on a random basis. This refers to the groups within the sample.

    *Random selection is when each element of the population has an equal and independent chance of being included in the sample. Random selection refers to the larger population not the sample.

    *These are not the same concepts. An example is that a study may use a convenience sampling method (non probability type) and randomize the subjects into a treatment or control group.

    *A randomized clinical trial (RCT) is a full experimental test of a new treatment, involving random assignment to treatment groups & is of a large & diverse sample (aim is for random selection sampling).

    *Note in a research report how the researchers sampled the larger population to determine if it was representative.
  10. 1. Nonprobability Sampling
    convenience: very easy
    quota: relatively easy
    Convenience: quant, very easy drawing representative sample, greater than any other sampling strategy for risk of bias, representativeness is questionable

    Choosing the most readily accessible persons or objects as study subjects. Accessibility = convenience for the researcher.

    High level of research bias thus ↓internal & external validity

    (Iint valid: if the IV caused the change in the DV [hx, maturation, testing, instrumentation, mortality, selection bias. Ext valid: which the findings can be generalized [subject selection, reactive effects or study conditions, measurement/testing effects]


    examples:

    *the first 24 patients admitted to ICU at hospital x with pneumonia.

    *the patient's on a oncology unit. 


    Quota (def: a nonprobability sampling strategy that identifies strata of population and proportionately represents strata in example): quant, relatively easy

    • *A strata of the population is known & thus a strata
    • is chosen for a sample. NOT randomly selected.

    The problem is that there could be over or under representation of characteristics. Based on researcher judgement thus bias. 
  11. 2. Probability: all quant
    a. simple random, adv/diadv: 
    b. stratified random
    c. multistage (cluster)
    d. systematic
    *This provides the highest level of sampling strategy.

    • *Simple random
    • *Stratified random
    • *Multistage (cluster)
    • *Systematic

    A. Simple Random: laborious, low risk of bias, representative of sample maximized

     

    Advantages and Disadvantages:

    *No research bias.

    *Generalizability to the larger population is maximized.

    *Differences between the sample & population are purely chance.

    *The larger the sample size the greater the representativeness or study external validity.

    *Disadvantage is that it is timely, costly & pose issues in obtaining accurate records/lists of every element of the population.

    *Reader be warned: Every type of sampling has some drawbacks. Look for how the researcher addresses them in their report. Ask why the sampling type was chosen & if it is randomized is it random selection or random assignment. 

    •  
    • B. Stratified Random: time-consuming, low risk of bias, enhanced representative of sample 

    *The population is divided into homogenous strata or subgroups.

    *The proportions of the sample represent the proportions noted of the concept being studied as it presents in the larger population.

    *The aim is to get a representative sample.

    *The concept is the same as nonprobability quota sampling but random selection of the larger population is used.

    *It is difficult to find a population list containing complete information, it is time consuming, enrolling a proportion of the population is challenging & costly. 

    C. Multistage (cluster): less time-consuming than simple/stratified sampling, subject to more sampling errors than simple or stratified sampling, less representative than simple or stratified sampling

    *This involves successive random sampling of clusters that progress from large to small until the sample criteria is met. 

    Example:

    *Clinical nurse gerontological specialists are desired as a sample.

    *1st sample: random sample of hospitals where CNS work (a list obtained from the CRNBC).

    *2nd sample: CNS in each hospital from the 1st sample (a list from the HR department at each hospital). Random selection of 2 CNS from each hospital who meet the eligibility criteria set by the researcher. 

    D. Systematic Sampling: more convenient & efficient than the other 3, bias in the form of nonrandomness can be inadvertently introduced, less representative if bias occurs as reult of coincidental nonrandomness

    *A strategy where a subject is chosen at fixed intervals such as every 10th person (or 5th, 7th, 10th)

  12. Sample Size: to be determined before the study
    power analysis aka power = measures representative sample (type of formula used for estimating optimum sample size)
    effect size
    *This is to be determined prior to the study.

    *The aim is to get the largest sample possible because it strengthens generalizability. 

    *A sample size in quantitative research can be determined by a statistical procedure known as a power analysis. Researchers refer to this as power. The goal is to get a representative sample. 

    • *To determine this the researcher must first determine
    • how much effect the treatment will have on the outcome of a study. For example a study to see the effect of pre-operative teaching on patient post operative anxiety.

    *The difference between anxiety pre ope & anxiety post ope is called the effect size.

    • If a small difference (effect size) is expected the
    • sample must be large.

    When a large difference (effect size) is expected the sample can be smaller

    _________________________Review______________

    *This is to be determined prior to the study.

    *The aim is to get the largest sample possible because it strengthens generalizability.

    *A sample size in quantitative research can be determined by a statistical procedure known as a power analysis. Researchers refer to this as power. The goal is to get a representative sample.

    • *To determine this the researcher must first determine
    • how much effect the treatment will have on the outcome of a study. For example a study to see the effect of pre-operative teaching on patient post operative anxiety.

    *The difference between anxiety pre ope & anxiety post ope is called the effect size.

    • *If a small difference (effect size) is expected the
    • sample must be large.

    • *When a large difference (effect size) is expected the
    • sample can be smaller.
  13. Factors influencing sample size or how large should my sample be?
    type of design used

    type of smapling procedure used 

    type of formula used for estimating optimum sample size (power analysis)

    degree of precision required

    heterogeneity of attributes under investigation

    relative frequency that the phenomenon of interest occurs in the population (ie. common vs rare health problem)

    projected cost of using a particular sampling strategy   
  14. Review
    overview of nonprobability & probability
    *4 steps to sampling procedure: prior to intervening with participants



  15. Critiquing Criteria

What would you like to do?

Home > Flashcards > Print Preview