research methods assignment

Type and Definition

Description of Steps

Advantages

Disadvantages

I. Simple random sampling—when each individual in a defined population has an equal and independent chance of being selected into the sample.

1. Assign to each member of population a unique number.

2. Select via use of random numbers (random number table, dice, computer, etc.) the sample members in a sufficient number

1. Maximum external validity, assuming reasonably small refusal rate.

2. Requires minimum knowledge of the population characteristics in advance

3. Free of possible classification errors

4. Very simple to implement

5. Easy to analyze data & compute error

1. Researcher must complete population list (often difficult)

2. Doesn’t use knowledge of population researcher may have

3. For same sample size, produces larger sampling error compared to stratified random sampling.

II. Systematic random sampling—when each individual in a defined population has an equal (but not independent) chance of being selected into the sample

1.Compute sampling interval r=N/n, where

N=number in population

n=number needed in sample

round up to an integer

2. Randomly select a start #

3. Select every rth individual

1.Maximum external validity, assuming no ordering in the list or file of names

2. Very simple/quicker than Simple random sampling because there is no need for a numbered list

3. Easy to analyze data & compute error

1. If sampling interval is related to a periodic order, increased variability may be introduced

2. Estimates of errors likely to be high where there is an order

3. May produce errors if N is miscalculated initially.

III. Multistage random sampling—when each individual in randomly sampled units have an equal chance of being selected into the sample.

1. Use random sampling (I or II) to select some sampling units (companies, schools, classes, etc.)

2. Use random sampling (I or II) to select individuals from each sampling unit.

1.Sampling lists, identification, numbering are required only for members in sampling units; especially advantageous with large or difficult-to-enumerate populations

2. If sampling units are geographically defined, this reduces data collection costs

3. High external validity

1. Sampling error larger than I or II for same sample size

2. Sampling error increases as number of units sampled in first stage decreases.

IV. Stratified random sampling—

a. Proportionate:

when each individual in purposively defined strata has an equal and independent chance of being selected into the sample

1. Divide population list into strata on the basis of their relevant characteristic(s)

2. Randomly select from each stratum a number of sample members proportionate to the size of each stratum

1. Assures representativeness of sample with respect to stratification variable

2. Decreases chance of failure to have a sufficient number of a subgroup(s) needed for desired analysis

3. Less extraneous variability than I-III.

4. Medium high external validity

1. Requires accurate information on proportion of population in each stratum; otherwise, increased error

2. May be costly, time-consuming to achieve stratified population list

3. Possibility of faulty classification that creates higher random variance

b. Disproportionate:

when each individual in purposively defined strata has an unequal but random chance of being selected into the sample

1. Same as IV.a

2. Randomly select from each stratum a number of sample members disproportionate to the sized of each stratum (i.e., one or more strata “overrepresented”)

1. More efficient than IV.a for comparing across strata (fewer total number required)

2. Assures having a sufficient number of a low incidence subgroup of population

3. Medium external validity

1. Same as IV.a on strata

2. Less efficient than IV.a for point estimates for entire population

3. Must use sampling weights prior to statistical analysis; make the data analysis more complex

Type and Definition

Description of Steps

Advantages

Disadvantages

V. Cluster (or area probability) sampling—

a. Simple

when each individual in randomly selected clusters have an equal and independent chance of being selected into the sample

1. Randomly select clusters or geographical area (e.g., states, counties, census tracts) by some form of random sampling (I or II)

2. Include all members of each cluster in sample (i.e., enumeration)

1. Has lowest interviewer data collection costs of all probability sampling methods

2. Requires listing of only individuals within the sampling clusters (or areas) which reduces time and money costs

3. Characteristics of clusters can also be used in research/data analysis (or cluster can be used as the unit of analysis)

1. Larger errors for comparable n than other probability samples

2. Requires unique assignment of each individual to exactly one cluster; inability to do so results in duplication and/or omission of individuals

3. Medium external validity

b. Stratified:

when each individual in randomly selected clusters and purposively defined strata have an equal and independent chance of being selected into the sample

1. Divide clusters into strata by stratum characteristics

2. Randomly select clusters from within each stratum

3. Include all members of each cluster in sample (i.e., enumeration)

1. Reduced variability compared to V.a; more efficient for comparison by strata

2. Comes closer than V.a to assuring researcher the ability to make relevant comparison of clusters across the different strata

1. Disadvantages of stratified added to those of the simple cluster (compounds distance from simple random sampling)

2. Cluster properties may change after characteristics are measured

3. Medium to low external validity

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VI. Quota sampling—

When only a predetermined proportion of a population with only characteristic(s) specified have a chance of being selected as a subject

1. Classify population members by some relevant variable(s)

2. Determine the proportion of sample desired with relevant characteristic(s)

3. Fix a quota of subjects with desired characteristic(s) for each observer/data collector

1. Reduces costs of obtaining sample members, and, perhaps, data collection

2. May introduces some stratification effect (but researcher won’t know for sure until after data collection /analysis)

3. If third step is done randomly, this may make it more like stratified sample (but, still can’t really be sure it is)

1. Variability and bias of estimates can’t be measured or adjusted for

2. Possible bias of researchers’ misclassification of subjects

3. Introduces biases of nonrandom selection by observers /data collectors that differ by observer

4. Low external validity

VII. Judgment or purposive sampling—

When only purposively selected individuals have a chance of being selected as a subject

1. Select subgroup(s) of the defined population than on the basis of best information is judged to be representative of the target population

2. Enumerate, select, or recruit individuals from subgroup(s)

1. Reduces costs of obtaining sample members, and, perhaps data collection since this is typically done where the subgroups are geographically proximate

2. Quick

1. Variability and bias of estimates can’t be measured or adjusted for

2. Requires strong assumptions about the population and its subgroup(s)

3. Violates all assumptions of all statistical techniques

4. Very low external validity

VIII. Convenience/snowball /volunteer sampling—

When individuals become subjects by convenience, referral, or by volunteering

Nor real method. Subjects are selected or recruited for researchers’ convenience and minimization of cost and/or time

1. Reduces costs of obtaining sample members, and, perhaps, data collection

2. Very quick

1. Violates all assumptions of all statistical techniques

2. No external validity; very high probability that sample is NOT representative of any population