Psych Ch. 6 (part 2) Quiz#2

Formula for calculating sample size

' Z²σ² n=-------------- ' D²

Confidence Levels

- The 95% confidence limits mean that if we drew many samples, and find the mean for each, then we can expect 95% of the sample means to lie within the stated limits. -95% confidence is considered acceptable in social research, medical research often requires 99% confidence Ex: A 95% level of confidence would mean that if 100 confidence intervals were constructed, we would expect 95 of the intervals to contain the true population mean.

Difference Between Strata and Clusters

-All strata are represented in the sample; but only some clusters are in the sample thus not fully representing the population -In stratified sampling, a random sample is drawn from each of the strata, whereas in cluster sampling only the selected clusters are studied.

Sampling and Generalization

-An important goal of many research studies is to apply the results -This will largely depend on your sample and how it relates to the larger population.

Populations and Samples

-Because it is usually not possible to study an entire population, you must be content to study a sample of that population

Confidence interval estimate of a parameter

-Consists of an interval of numbers along with a probability that the interval contains the unknown parameter.

Types of sampling: Non probability samples

-Convenience samples (ease of access) -Purposive Sampling (judgemental)

Why sampling?

-Get information about -large populations -Less costs -Less field time -More accuracy i.e. (Can Do A Better Job of Data Collection) -Most times it is impossible to study the whole population

How sampling and generalization relates to Power?

-If your sample accurately represents your population, then you have higher power.

Generalization

-Is the ability to apply findings from a sample to a larger population -Studies whose findings can be applied across a variety of research settings and subject populations possess a high degree of external validity. -The ability to generalize findings to a larger population contributes to the external validity of a study.

Standard error details

-Measure of the statistical accuracy of a large population based on the sample -The larger the sample the lower the standard error. -When the sample has normal distribution, we can use the z score approach to obtain confidence limits for the sample mean.

Sampling method

-Probability samples -Non-probability samples

Convenience samples (ease of access):

-Sample is selected from elements of a population that are easily accessible

Convenience Sample

-Selection of whichever individuals are easiest to reach -It is done at the "convenience" of the researcher

Types of sampling: Probability samples

-Simple Random Sample -Stratified Random Sample -Cluster sampling -Systematic -Convenience

Use random sampling to draw inferences about a population.

-Statistics can be used to gain information about a population by examining a sample of the population -Generalizations about a population from a sample are valid only if the sample is representative of that population. -Random sampling tends to produce representative samples and support valid inferences.

Volunteers

-Tend to be more highly educated -Tend to come from a higher social class -Are of higher intelligence in general -Have a higher need for approval -Are more social

Standard Error

-Using the standard deviation of the population and the sample size a statistical calculation can measure the degree of error likely to occur between the results of a sample and the results from the population

Experiment with volunteers

-Whether a person volunteers for a study and how that person performs may depend on a combination of personal and study characteristics. -This is why you should examine dimensions of the "big-five" personality characteristics Ex: agreeableness, openness to new experience, conscientiousness, extroversion/introversion, and neuroticism)

-Purposive Sampling (judgemental)

-You chose who you think should be in the study (may be exposed to experimenter bias)

The sample mean, x̅, is the best point estimate of the population mean, μ.

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Systematic Sample

Every (k) member ( for example: every 10th person) is selected from a list of all population members.

Simple Random Sample

Every member from the population has an equal chance of being selected for the experiment

Level of confidence

In a confidence interval is a probability that represents the percentage of intervals that will contain if a large number of repeated samples are obtained.

Population

Includes all possible samples or data points

Volunteer bias

Is a real problem that must be addressed in all designed

Sample

Is a subset of individuals selected from a larger population.

Nonrandom Sampling and Animal Subjects

Laboratory animals are usually ordered for a given study from a single supplier and typically consist of animals of the same species, strain, sex, and age (indeed, many of them may be littermates).

Probability Samples

Offer each respondent an equal probability or chance at being included in the sample. - Objective -Empirical -Scientific -Quantitative -Representative

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.

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.

Target Population:

The population to be studied/ to which the investigator wants to generalize his results

Statistics and Samples

When presenting your research you need to be able to demonstrate, how representative of the whole population the sample data you have collected is. There are two statistical test used to do this: -Standard error -Confidence levels

Confidence level %: 90% 95% 99%

Z value: 1.65 1.96 2.58