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# Data Collection PowerPoint PPT Presentation

Data Collection. Sampling. Target Population. The group of people to whom the researcher wishes to generalize the results of the study. Accessible Population. -The smaller portion of the target population to whom the researcher actually has access. Sample.

Data Collection

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## Data Collection

Sampling

### Target Population

The group of people to whom the researcher wishes to generalize the results of the study

### Accessible Population

• -The smaller portion of the target population to whom the researcher actually has access

### Sample

• -The group of people who supply data for the study (Study group)

### Sampling

• the process of selecting a portion of the target population (sample) in such a way that the individuals chosen represent, as nearly as possible, the characteristics of the target population.

### Sampling Unit

• -A single member of the target population.

### Sampling Bias

-An overrepresentation or underrepresentation of some characteristic in the sample relative to the target population

Unconscious

Conscious

• The extent to which bias is a concern is a function of the homogeneity or heterogeneity of the target population.

• When a variation (relevant to the research question) occurs in a population, then it must occur in the sample

### Strata

• -Subpopulations of the target population

### Sampling error

• -the fluctuation of a statistic from one sample to another drawn from the same population. (Can be estimated with probability sampling) Note: the larger the sample, the less sampling error.

### Probability Sampling

• -Sampling procedures use some form of randomization to select samples from the population.

### Non Probability Sampling

• Sampling procedures

using other than random procedures.

### NON PROBABILITY SAMPLING

• CONVENIENCE SAMPLING

• PURPOSIVE SAMPLING

• QUOTA SAMPLING

### Convenience Sampling(Accidental Sampling)

• Involves the use of the most convenient and readily available subjects for the sample.

• CMan on the street interviews

• CTeacher uses students

• CVolunteers

### Convenience/accidental sampling

• Problem: Sample bias because of “self selection”--available subjects may be highly atypical of the population with regard to critical variables.

### SNOWBALL SAMPLING”

• Variation of above, used when subjects are hard to find. One subject recommends another. Even more prone to bias.

• Convenience sampling is the most widely used yet weakest form of sampling. There is no way to evaluate all of the biases that may be operating.

### QUOTA SAMPLING

• Researcher uses some knowledge of the population to build some representativeness into the sampling plan

• divides population into different strata and samples from each of them

• USUALLY BETTER THAN JUST CONVENIENCE

• THE BASIS OF THE CHARACTERISTICS CHOSEN SHOULD REFLECT IMPORTANT DIFFERENCES IN THE DEPENDENT VARIABLE

• Cage

• Cgender

• Cethnicity

• Csocioeconomic status

• Ceducation

• Cmedical diagnosis

• Coccupation

### Quota Sampling

• Problem: you cant always determine which characteristics in the sample are going to be reflected in the dependent variable

### PURPOSIVE SAMPLING“Judgmental Sampling”

• PROCEEDS ON THE BELIEF THAT THE RESEARCHER KNOWS ENOUGH ABOUT THE POPULATION AND ITS ELEMENT TO HANDPICK THE SAMPLE

• Cselects “typical” persons

• Cselects widest variety

### Purposive or Judgemental Sampling

• Assumption:

• judgemental errors will tend to balance out.

• Risk of conscious bias greatly multiplied

• Should be avoided if the population is heterogeneous.

### PROBABILITY SAMPLING

• SIMPLE RANDOM

• STRATIFIED RANDOM

• CLUSTER

The probability of any member of the target population being included in the sample can be calculated.

• SYSTEMATIC SAMPLING(Can be either probability or non probability)

### SIMPLE RANDOM SAMPLING

Cidentify population

Cestablish sampling frame

Cnumber elements in sampling frame consecutively

Crandomly select from list

• Random sampling does not guarantee representativeness, it does guarantee that difference between the sample and the population are purely a function of chance.

### STRATIFIED RANDOM SAMPLE

• The population is divided into two or more strata by relevant characteristics and subjects are randomly chosen from these strata

• Slightly better than simple random, especially if the sample is not very large.

### CLUSTER SAMPLING

• Multistage sampling process

• Used when target population is very large

• Results in more sampling error

• Statistical analysis more complicated

### SYSTEMATIC SAMPLING

• Selection of every Kth case from a list of possible subjects.

• ( K represents any number)

### SAMPLE SIZE

• N Determined by:

• COHEN’S POWER ANALYSIS

Determine “effect size of treatment”

Use in power analysis formula

Achieves the least measurement error

### N DETERMINED BY CONVENTION

The bigger the better

Ccost and convenience

C10% minimum for descriptive studies

C15 subjects/group for experiments

C5 for each cell in factorial