HLTB15H3 Lecture Notes - Lecture 7: Normal Distribution, Systematic Sampling, Standard Deviation

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1 Mar 2011

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Cluster sampling - sampling population is i) ii) divided into groups clusters elements within each cluster are selected. cluster 1 all provinces or territories. Non-random (non-probabil i ty) sampl i ng designs. does not follow the theory of probability! number of elements either unknown or cannot be individually identified. consent to participate not based on an obvious or visible characteristic common among market research and media. Application - construction of a historical reality or description of a phenomenon. No need for information on sampling frame, sample elements, location characteristics of both random and non-random sampling designs. the greater the heterogeneity or diversity, the greater the number of respondents needed. 1: degree of accuracy level of variation. Objective: to estimate the age of students in this lecture hall: assume willing to tolerate an error of 0. 5 years, confidence level 95, assume standard deviation is within 2 years.

Related Questions

Explain the difference between a stratified sample and a cluster sample. (Select all that apply.)

(1) In a cluster sample, every sample of size n has an equal chance of being included.

(2) In a stratified sample, every sample of size n has an equal chance of being included.

(3) In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

(4) In a cluster sample, random samples from each strata are included.

(5) In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

(6) In a cluster sample, the only samples possible are those including every kth item from the random starting position.

(7) In a stratified sample, random samples from each strata are included.

(8) In a stratified sample, the only samples possible are those including every kth item from the random starting position.

redrabbit701

Explain the difference between a stratified sample and a cluster sample. (Select all that apply.)

1) In a stratified sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

2) In a stratified sample, random samples from each strata are included.

3) In a cluster sample, the only samples possible are those including every kth item from the random starting position.

4) In a cluster sample, the clusters to be included are selected at random and then all members of each selected cluster are included.

5) In a cluster sample, random samples from each strata are included.

6) In a stratified sample, every sample of size n has an equal chance of being included.

7) In a stratified sample, the only samples possible are those including every kth item from the random starting position.

8) In a cluster sample, every sample of size n has an equal chance of being included.

grayotter535

Which sampling method does not require a frame?

A. Cluster

B. Systematic

C. Stratified

D. Simple random

E. All of the above sampling methods require a frame

pucechinchilla834

HLTB15H3 Lecture Notes - Lecture 7: Normal Distribution, Systematic Sampling, Standard Deviation

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