Sampling Design ppt2

Slide 1: 

SAMPLING DESIGN By: Carolyn Henry Terry-Ann HoSang Nichelle Serju Debbie Douglas Nicolette Wright

WHAT IS SAMPLING : 

Imagine an experiment to test the effects of a new education technique on schoolchildren. Wouldn’t it would be impossible to select the entire school age population of a country, divide them into groups and perform research? WHAT IS SAMPLING

WHAT IS SAMPLING : 

Sampling basically means: selecting people/objects from a "population" in order to test the population for something. WHAT IS SAMPLING

WHAT IS SAMPLING : 

For example, we might want to find out how people are going to vote at the next election. Obviously we can't ask everyone in the country, so we ask a sample. WHAT IS SAMPLING

Slide 5: 

SAMPLE POPULATION WHAT IS A SAMPLE

WHAT IS SAMPLING : 

When considering a particular population it is usually advisable to; Choose a sample in such a way that everyone is represented. This allows careful thought about sample size and composition. Questionnaires are often devised to identify the required information. These need to be idiot proof, so questions need to cover all alternatives and give little scope for variation. WHAT IS SAMPLING

RATIONALE FOR SAMPLING : 

Use inferential statistics to draw conclusions about populations from samples. This enables us to determine a population's characteristics by directly observing only a portion (or sample) of the population. RATIONALE FOR SAMPLING

RATIONALE FOR SAMPLING : 

We obtain a sample rather than a complete enumeration (a census ) of the population because; Though census have the advantage of completeness it may not be practical and is not always economical RATIONALE FOR SAMPLING

REASONS FOR CHOOSING SAMPLING : 

Budget and Time constraints- less expensive and less time to study a sample than a population. REASONS FOR CHOOSING SAMPLING

REASONS FOR CHOOSING SAMPLING : 

Resource constraints – most business researchers are forced to deal with resource constraints including the all important factor of cost and time. Well selected samples can be less costly. REASONS FOR CHOOSING SAMPLING

REASONS FOR CHOOSING SAMPLING : 

Complete population inaccessible- Researchers may at no one time be able to reach every unit in the population. REASONS FOR CHOOSING SAMPLING

REASONS FOR CHOOSING SAMPLING : 

Accurate and reliable results- since sampling is done by skilled and qualified researchers the results are expected to be accurate Whenever destruction of test units is involved. Sometimes the very act of observing the desired characteristic of a unit of the population destroys it for the intended use REASONS FOR CHOOSING SAMPLING

Slide 13: 

There are 7 steps in the sampling process Select the population- The explicit designation of all elements concerned. A proper definition includes elements, sampling units, extent and time. (extent is simply the range of conditions to which the population under study is restricted. Select sampling units- Select what sampling units are appropriate in the population. It may be one element or multiple elements. THE SAMPLING PROCESS

Slide 14: 

3. Select a Sampling Frame- This means physically representing the population. This is a critical step because if the sampling frame chosen does not adequately represent the population selected in step 1, then the results of the study will be questionable. Select a Sample Design- The method by which the sample is chosen. There are probability & non-probability type designs. THE SAMPLING PROCESS

Slide 15: 

5. Select the sample size- The selection of the number of people or objects to study in the population. The sample size depends on a number of factors : THE SAMPLING PROCESS

Slide 16: 

Homogeneity of sampling units : the more alike the sampling units are the smaller the sample need to estimate the parameters. Confidence: the degree to which researchers want to be sure that they are estimating true population parameters. Precision: how close should the estimate be to the true population parameter. Statistical power: making the right decision to reject certain hypothesis and recognize that relationship when it exists. THE SAMPLING PROCESS

Slide 17: 

6. Select a Sampling Plan- this specifies the procedures and methods to obtain the desired sample. If selected correctly it guides the researcher in the selection of the study sample so that errors may be minimized. 7. Select the Sample- the specific units of analysis are enumerated and designated for the next step in the research process. THE SAMPLING PROCESS

Slide 18: 

TYPES OF SAMPLING

Slide 19: 

Used when the selection of the sample is purely based on chance. Usually more costly as they require a complete enumeration of the population and then we must locate the units for analysis. It is however more accurate, they take more time because they are more exact. PROBABILITY SAMPLING

Slide 20: 

SIMPLE RANDOM SAMPLING – Easiest form of probability sampling. Researcher ensure that all the members of the population are included in the list and then randomly select the desired number of subjects. It can be as mechanical as picking strips of paper with names written on it from a hat while the researcher is blindfolded or technically using a computer software to do the random selection. TYPES OF PROBABILITY SAMPLING

Slide 21: 

STRATIFIED/PROPORTIONAL RANDOM SAMPLING Subjects are initially grouped into different classifications such as age, socioeconomic status or gender. The researcher then randomly selects the final list of subjects from the different strata. Researchers usually use stratified random sampling if they want to study a particular subgroup within the population. It is also preferred over the simple random sampling because it warrants more precise statistical outcomes. TYPES OF PROBABILITY SAMPLING

Slide 22: 

SYSTEMATIC RANDOM SAMPLING Similar to an arithmetic progression wherein the difference between any two consecutive numbers is the same.. TYPES OF PROBABALITY SAMPLING

Slide 23: 

CLUSTER SAMPLING/ MULTISTAGE CLUSTER SAMPLING Used when the population is so big or the geographical area of the research is so large. simple random sampling is almost impossible because of the size of the population. In cluster sampling, the research first identifies boundaries, in case of our example; it can be countries within Asia. The researcher randomly selects a number of identified areas. It is important that all areas (countries) within the population be given equal chances of being selected. The researcher can either include all the individuals within the selected areas or he can randomly select subjects from the identified areas. TYPES OF PROBABALITY SAMPLING

Slide 24: 

TYPES OF NON PROBABALITY SAMPLING ACCIDENTAL / CONVENIENCE SAMPLING The most common of all sampling techniques because it is the easiest, cheapest and least time consuming. Samples are selected because they are accessible to the researcher. Subjects are chosen simply because they are easy to recruit. not representative of target population because sample are selected if they can be accessed easily and conveniently.

Slide 25: 

TYPES OF NON PROBABALITY SAMPLING QUOTA SAMPLING Non-probability sampling technique wherein the researcher ensures equal or proportionate representation of subjects depending on which trait is considered as basis of the quota. Advantage over accidental/convenience sampling is that many sectors of the population are represented. But its representativeness is doubtful because there is no proportional representation and there are no guidelines in the selection of the respondents.

Slide 26: 

JUDGEMENT SAMPLING Sample is taken based on certain judgements about the overall population. Critical issue: Objectivity “how much can judgement be relied upon to arrive at a typical sample?” Advantage: reduced cost and time involved in acquiring the sample Disadvantage of quota sampling - Interviewers choose who they like (within above criteria) and may therefore select those who are easiest to interview. Impossible to estimate accuracy (because not random sample) TYPES OF NON PROBABALITY SAMPLING

Slide 27: 

SNOWBALL SAMPLING- is usually done when there is a very small population size. In this type of sampling, the researcher asks the initial subject to identify another potential subject who also meets the criteria of the research. The downside of using a snowball sample is that it is hardly representative of the population. TYPES OF NON PROBABALITY SAMPLING

Slide 28: 

SAMPLING TERMINOLOGIES

SAMPLING ELEMENT/UNIT OF ANALYSIS : 

The unit about which the information is collected. Each member of the population is a unit. It forms the basis from which conclusion are drawn and research problems are solved. SAMPLING ELEMENT/UNIT OF ANALYSIS

Slide 30: 

The complete set of unit analysis that are under investigation. The collection of individuals, families groups, organizations and events that the researcher wants to find out about. The target population is the entire group a researcher is interested in; the group about which the researcher wishes to draw conclusions. SAMPLING POPULATION

SAMPLING TERMINOLOGIES : 

Example Suppose we take a group of men aged 35-40 who have suffered an initial heart attack. The purpose of this study could be to compare the effectiveness of two drug regimes for delaying or preventing further attacks. The target population here would be all men meeting the same general conditions as those actually included in the study. SAMPLING TERMINOLOGIES

Slide 32: 

The element or some set of elements considered for selection in some stage of sampling and may include individuals, households, city blocks, census tracts, departments, companies or any other logical unit that is related to the study. SAMPLING UNITS

Slide 33: 

A physical representation of objects, individual groups etc., that is important to the development of the study sample. The actual list of the study population Note: If you were doing a phone survey and selecting names from the telephone book, the book would be your sampling frame. SAMPLING FRAME

Slide 34: 

Missing elements: Some members of the population are not included in the frame. Foreign elements: The non-members of the population are included in the frame. Duplicate entries: A member of the population is surveyed more than once. Groups or clusters: The frame lists clusters instead of individuals. SAMPLING FRAME PROBLEMS

Slide 35: 

Parameter: A value computed by a whole population e.g. the average purchase price of all homes sold in Ocho Rios in 2010. a parameter is a "true" value Statistic: a value computed from a sample e.g. take a sample of just 100 home purchases and compute the average price for that sample a statistic is a guess or estimate of the true value A statistic is used to estimate the value of the parameter SAMPLING PARAMETER & STATISTICS

Slide 36: 

A measure of the difference between a statistic and the parameter it is estimating . Procedural error- a bias in the sampling procedure itself. Imprecision -associated with using statistics to estimate parameters. SAMPLING ERRORS