Sunday, 12 February 2017

BUSINESS RESEARCH METHOD UNIT III

BUSINESS RESEARCH METHOD UNIT III


RESEARCH DESIGN



A research design is a “Blue Print” for collection, measurement and analysis of data. It outlines how the research will be carried out. It is like glue which sticks together the entire process of research. It provides answers to various questions like - What techniques will be used to gather data. What kind of sampling will be used? How time and cost constraints be dealt with? Etc.
Essentials of Research Design
1. The design should be an activity and time based plan
2. It is always based on research question
3. It guides the selection of sources and types of information
4. It indicates a framework for specifying the relationship among the study’s variables
5. Outlines procedures for every research activity
6. It must be appropriate, efficient and economical
7. It should be flexible
8. It must be adequate
Types of Research Design
You cannot put the same shoe on every foot” - Syrus
Although every problem and research objective may seem unique, there are usually enough similarities among problems and objectives to allow decisions to be made in advance about the best plan to resolve the problem. There are some basic research designs that can be successfully matched to given problems and research objectives.
Three traditional categories of research design:
• Exploratory
• Descriptive
• Causal
The choice of the most appropriate design depends largely on the objectives of the research and how much is known about the problem and these objectives. The overall research design for a project may include one or more of these three designs as part(s) of it.
Further, if more than one design is to be used, typically we progress from Exploratory toward Causal.
Basic Research Objectives and Research Design
Research Objective Appropriate Design
To gain background information, to define terms, to clarify exploratory problems and develop hypotheses, to establish research priorities, to develop questions to be answered
To describe and measure phenomena at a point Descriptive
In time
To determine causality, test hypotheses, to make “if-then” Causal
Statements, to answer questions
Research Design: Exploratory Research
Exploratory research is most commonly unstructured, “informal” research that is undertaken to gain background information about the general nature of the research problem. Exploratory research is usually conducted when the researcher does not know much about the problem and needs additional information or desires new or more recent information. Exploratory research helps diagnose the dimensions of the problem so that successive research will be on target. It helps to set priorities for research. Exploratory research is used in a number of situations:
• To gain background information
• To define terms
• To clarify problems and hypotheses
• To establish research priorities
A variety of methods are available to conduct exploratory research:
• Secondary Data Analysis
• Experience Surveys
• Case Analysis
• Focus Groups
• Projective Techniques
Categories of Exploratory Research
• Experience Surveys: - Issues and ideas may be discussed with persons who have had
personal experience in the field.
• Secondary data analysis: - Another quick and economical source of background information is existing literature containing data that has been compiled for some purpose other than the purpose in hand
• Case Study method: -obtains information from one or a few situations that are similar to the problem situation. Primary advantage is that an entire organisation or entity can be investigated in depth and with meticulous attention to detail.
• Pilot Studies are used in different types of designs. - Within the context of exploratory research it covers some part of the research on a small scale. Major categories of pilot study include focus group interviews, projective techniques, and depth interviews.
Categories of Pilot Studies
• Focus Group interviews: - Unstructured, free flowing, group dynamic sessions that allow individuals the opportunity to initiate the topics of discussion. There is synergistic and spontaneous interaction among the respondents. Found to be highly advantageous.
• Projective techniques; - An indirect means of questioning the respondents. Uses word association tests, sentence completion test, third person test, role playing technique and
Thematic Apperception Test.
• Depth interviews: - unstructured, extensive interviews that encourage an individual to talk freely and in depth about a topic
Historical Research
History, the meaningful record of human achievement, helps us to understand the present and to some extent, to predict the future.
• Used to “prevent reinventing the wheel” every few years.
• It is the application of scientific method to the description and analysis of past events
Descriptive Research
Descriptive research is undertaken to provide answers to questions of who, what, where, when, and how – but not why.
Two basic classifications:
• Cross-sectional studies
• Longitudinal studies
Research Design
Descriptive Research -Cross-sectional Studies
• Cross-sectional studies measure units from a sample of the population at only one point in time.
• Sample surveys are cross-sectional studies whose samples are drawn in such a way as to be representative of a specific population.
• On-line survey research is being used to collect data for cross-sectional surveys at a faster rate of speed.
Descriptive Research -Longitudinal Studies
• Longitudinal studies repeatedly draw sample units of a population over time.
• One method is to draw different units from the same sampling frame.
• A second method is to use a “panel” where the same people are asked to respond periodically.
• On-line survey research firms recruit panel members to respond to online queries.
Research Design: Causal Research
• Causality may be thought of as understanding a phenomenon in terms of conditional statements of the form “If x, then y.”
• Causal relationships are typically determined by the use of experiments, but other methods are also used.
Experiments
An experiment is defined as manipulating (changing values/situations) one or more independent variables to see how the dependent variable(s) is/are affected, while also controlling the effects of additional extraneous variables.
– Independent variables: - that over which the researcher has control and wishes to manipulate i.e. package size, ad copy, price.
– Dependent variables: - that over which the researcher has little to no direct control, but has a strong interest in testing i.e. sales, profit, market share.
– Extraneous variables: - those that may affect a dependent variable but are not independent variables.
Experimental Design
An experimental design is a procedure for devising an experimental setting such that a change in the dependent variable may be solely attributed to a change in an independent variable.
Symbols of an experimental design:
O = measurement of a dependent variable
X = manipulation, or change, of an independent variable
R = random assignment of subjects to experimental and control groups
E = experimental effect
After-Only Design: X O1
One-Group, Before-After Design: O1 X O2
Before-After with Control Group:
• Experimental group: O1 X O2
• Control group: O3 O4
• Where E = (O2 – O1) – (O4 – O3)
How Valid Are Experiments?
An experiment is valid if:
• The observed change in the dependent variable is, in fact, due to the independent variable (internal validity)
• If the results of the experiment apply to the “real world” outside the experimental setting (external validity)
Choosing the right instrument for data collection
• The instrument you choose for data collection affects your entire study.
• Validity is your primary concern!
• Reliability is a secondary concern
What is the Validity of a Study?
Internal Validity – The degree to which changes in the dependent variable are affected by the manipulated independent variable. Maintaining high internal validity means controlling for all other independent variables other than the one(s) being studied
External Validity – The degree to which the results of a study can be generalized to the “real world”. Factors that negatively affect external validity also negatively affect the generalizability of the results
Instrument Validity
Does an instrument measure what it is supposed to measure? Four types of instrument validity are
as follows:
– Construct
– Criterion related
– Content
– Inter-rater / Intra-rater
Construct Validity
It is the most important type of validity. Construct validity is the degree to which the instrument actually measures whether or not an underlying construct is being measured.
For example, does a math test actually measure math achievement? Does a personality test actually measure personality?
Criterion Related Validity
Criterion Related Validity is of two types:-
• Concurrent validity – Degree to which scores on one test are correlated with scores on another test administered at the same time. Only one group is used.
• Predictive validity – Degree to which scores on one test predicts scores on a test administered in the future. Only one group is used.
Reliability
Reliability is the consistency with which an instrument measures the construct or content area it is intended to measure. Reliability is established using such techniques as
• Split-half,
• Rationale equivalence and inter-rater
Reliability is reported as a coefficient ranging from 0.00 (low) to +1.00 (high). Anything above .70 is considered sufficient for most cases
Measures of Reliability
• Stability (test / re-test)
• Equivalence (alternate forms)
• Equivalence and Stability Combined
• Internal consistency
• Scorer / Rater
Internal Consistency
Questions on tests should be equally difficult throughout entire instrument
 Split-half – Used with dichotomous tests
 Kuder-Richardson 20 / 21 – Improvement on split-half
 Cronbach’s Alpha – Only used with instruments with more than two scores (e.g., Likert
Scales)
Sampling Design
Sampling is concerned with the selection of a subset of individuals from within a statistical population to estimate characteristics of the whole population. Two advantages of sampling are that the cost is lower and data collection is faster than measuring the entire population. A Sample design is a definite plan for obtaining a sample from a given population
Definition
According to Gerald Hursh “a Sample Design is the theoretical basis and the practical means by which we infer the characteristics of some population by generalizing from the characteristics of relatively few of the units comprising the population
Steps in Sampling Design
1. Define the population or universe
2. State the sampling frame
3. Identify the sampling unit
4. State sampling method
5. Determine the sample size
6. Spell out the sampling plan
7. Select the sample
Population Definition
Successful statistical practice is based on focused problem definition. In sampling, this includes defining the population from which our sample is drawn. A population can be defined as including all people or items with the characteristic one wish to understand. Because there is very rarely enough time or money to gather information from everyone or everything in a population, the goal becomes finding a representative sample (or subset) of that population.
Sometimes that which defines a population is obvious. For example, a manufacturer needs to decide whether a batch of material from production is of high enough quality to be released to the customer, or should be sentenced for scrap or rework due to poor quality. In this case, the batch is the population.
Although the population of interest often consists of physical objects, sometimes we need to sample over time, space, or some combination of these dimensions. For instance, an investigation of supermarket staffing could examine checkout line length at various times, or a study on endangered penguins might aim to understand their usage of various hunting grounds over time.
For the time dimension, the focus may be on periods or discrete occasions.
In other cases, our 'population' may be even less tangible. For example, Joseph
Jagger studied the behaviour of roulette wheels at a casino in Monte Carlo, and used this to identify a biased wheel. In this case, the 'population' Jagger wanted to investigate was the overall behaviour of the wheel (i.e. the probability distribution of its results over infinitely many trials), while his 'sample' was formed from observed results from that wheel. Similar considerations arise when taking repeated measurements of some physical characteristic such as the electrical conductivity of copper.
This situation often arises when we seek knowledge about the cause system of which the observed population is an outcome. In such cases, sampling theory may treat the observed population as a sample from a larger 'super population'. For example, a researcher might study the success rate of a new 'quit smoking' program on a test group of 100 patients, in order to predict the effects of the program if it were made available nationwide. Here the super population is "everybody in the country, given access to this treatment" - a group which does not yet exists, since the program isn't yet available to all.
Note also that the population from which the sample is drawn may not be the same as the population about which we actually want information. Often there is large but not complete overlap between these two groups due to frame issues etc. (see below). Sometimes they may be entirely separate - for instance, we might study rats in order to get a better understanding of human health, or we might study records from people born in 2008 in order to make predictions about people born in 2009.
Time spent in making the sampled population and population of concern precise is often well spent, because it raises many issues, ambiguities and questions that would otherwise have been overlooked at this stage.
Sampling Frame
In the most straightforward case, such as the sentencing of a batch of material from production (acceptance sampling by lots), it is possible to identify and measure every single item in the population and to include any one of them in our sample. However, in the more general case this is not possible. Where voting is not compulsory, there is no way to identify which people will actually vote at a forthcoming election (in advance of the election). These imprecise populations are not amenable to sampling in any of the ways below and to which we could apply statistical theory.
As a remedy, we seek a sampling frame which has the property that we can identify every single element and include any in our sample. The most straightforward type of frame is a list of elements of the population (preferably the entire population) with appropriate contact information.
For example, in an opinion poll, possible sampling frames include an electoral register and a telephone directory.
Problem Related With Sampling Frame
1. Non coverage and incompleteness.
2. Appearance of cluster of element.
3. Inclusion of foreign element in the list.
Probability and Non-Probability Sampling
A probability sampling is one in which every unit in the population has a chance (greater than zero) of being selected in the sample, and this probability can be accurately determined. The combination of these traits makes it possible to produce unbiased estimates of population totals, by weighting sampled units according to their probability of selection.
Example: We want to estimate the total income of adults living in a given street. We visit each household in that street, identify all adults living there, and randomly select one adult from each household. (For example, we can allocate each person a random number, generated from a uniform distribution between 0 and 1, and select the person with the highest number in each household). We then interview the selected person and find their income. People living on their own are certain to be selected, so we simply add their income to our estimate of the total. But a person living in a household of two adults has only a one-in-two chance of selection. To reflect this, when we come to such a household, we would count the selected person's income twice towards the total. (The person who is selected from that household can be loosely viewed as also representing the person who isn't selected.)
In the above example, not everybody has the same probability of selection; what makes it a probability sample is the fact that each person's probability is known. When every element in the population does have the same probability of selection, this is known as an 'equal probability of selection' (EPS) design. Such designs are also referred to as 'self-weighting' because all sampled units are given the same weight. Probability sampling includes: Simple Random Sampling, Systematic Sampling, and Stratified Sampling, Probability Proportional to Size Sampling, and Cluster or Multistage Sampling. These various ways of probability sampling have two things in common:
1. Every element has a known nonzero probability of being sampled and
2. Involves random selection at some point.
Non Probability Sampling; - Non Probability Sampling is any sampling method where some elements of the population have no chance of selection (these are sometimes referred to as 'out of coverage'/'under covered'), or where the probability of selection can't be accurately determined. It involves the selection of elements based on assumptions regarding the population of interest, which forms the criteria for selection. Hence, because the selection of elements is nonrandom, non-probability sampling does not allow the estimation of sampling errors. These conditions give rise to exclusion bias, placing limits on how much information a sample can provide about the population. Information about the relationship between sample and population is limited, making it
difficult to extrapolate from the sample to the population.
Example: We visit every household in a given street, and interview the first person to answer the door. In any household with more than one occupant, this is a nonprobability sample, because some people are more likely to answer the door (e.g. an unemployed person who spends most of their time at home is more likely to answer than an employed housemate who might be at work when the interviewer calls) and it's not practical to calculate these probabilities.
Non probability sampling methods include accidental sampling, quota sampling and purposive sampling. In addition, no- response effects may turn any probability design into a non probability design if the characteristics of non response are not well understood, since non response effectively modifies each element's probability of being sampled.
Sampling Methods
Within any of the types of frame identified above, a variety of sampling methods can be employed, individually or in combination. Factors commonly influencing the choice between these designs include:
 Nature and quality of the frame
 Availability of auxiliary information about units on the frame
 Accuracy requirements, and the need to measure accuracy
 Whether detailed analysis of the sample is expected
 Cost/operational concerns
Simple Random Sampling
In a simple random sample (SRS) of a given size, all such subsets of the frame are given an equal probability. Each element of the frame thus has an equal probability of selection: the frame is not subdivided or partitioned. Furthermore, any given pair of elements has the same chance of selection as any other such pair (and similarly for triples, and so on). This minimises bias and simplifies analysis of results. In particular, the variance between individual results within the sample is a good indicator of variance in the overall population, which makes it relatively easy to estimate the accuracy of results.
However, SRS can be vulnerable to sampling error because the randomness of the selection may result in a sample that doesn't reflect the makeup of the population. For instance, a simple random sample of ten people from a given country will on average produce five men and five women, but any given trial is likely to over represent one sex and under represent the other.
Systematic and stratified techniques, discussed below, attempt to overcome this problem by using information about the population to choose a more representative sample.
SRS may also be cumbersome and tedious when sampling from an unusually large target population. In some cases, investigators are interested in research questions specific to subgroups of the population. For example, researchers might be interested in examining whether cognitive ability as a predictor of job performance is equally applicable across racial groups. SRS cannot accommodate the needs of researchers in this situation because it does not provide subsamples of the population. Stratified sampling, which is discussed below, addresses this weakness of SRS.
Simple random sampling is always an EPS design (equal probability of selection), but not all EPS designs are simple random sampling.
Systematic Sampling
Systematic sampling relies on arranging the study population according to some ordering scheme and then selecting elements at regular intervals through that ordered list. Systematic sampling involves a random start and then proceeds with the selection of every kth element from then onwards. In this case, k = (population size/sample size). It is important that the starting point is not automatically the first in the list, but is instead randomly chosen from within the first to the kth element in the list. A simple example would be to select every 10th name from the telephone directory (an 'every 10th' sample, also referred to as 'sampling with a skip of 10').
As long as the starting point is randomized, systematic sampling is a type of probability sampling. It is easy to implement and the stratification induced can make it efficient, ifthe variable by which the list is ordered is correlated with the variable of interest. 'Every 10th' sampling is especially useful for efficient sampling from databases.
For example, suppose we wish to sample people from a long street that starts in a poor are (house No. 1) and ends in an expensive district (house No. 1000). A simple random selection of addresses from this street could easily end up with too many from the high end and too few from the low end (or vice versa), leading to an unrepresentative sample. Selecting (e.g.) every 10th street number along the street ensures that the sample is spread evenly along the length of the street, representing all of these districts. (Note that if we always start at house #1 and end at #991, the sample is slightly biased towards the low end; by randomly selecting the start between #1 and #10, this bias is eliminated.
However, systematic sampling is especially vulnerable to periodicities in the list. If periodicity is present and the period is a multiple or factor of the interval used, the sample is especially likely to be unrepresentative of the overall population, making the scheme less accurate than simple random sampling.
For example, consider a street where the odd-numbered houses are all on the north (expensive) side of the road, and the even-numbered houses are all on the south (cheap) side. Under the sampling scheme given above, it is impossible to get a representative sample; either the houses sampled will all be from the odd-numbered, expensive side, or they will all be from the even numbered, cheap side.
Another drawback of systematic sampling is that even in scenarios where it is more accurate than SRS, its theoretical properties make it difficult to quantify that accuracy. (In the two examples of systematic sampling that are given above, much of the potential sampling error is due to variation between neighbouring houses - but because this method never selects two neighbouring houses, the sample will not give us any information on that variation.)
As described above, systematic sampling is an EPS method, because all elements have the same probability of selection (in the example given, one in ten). It is not 'simple random sampling' because different subsets of the same size have different selection probabilities - e.g. the set {4, 14, 24,..., 994} has a one-in-ten probability of selection, but the set {4,13,24,34,...} has zero probability of selection.
Systematic sampling can also be adapted to a non-EPS approach; for an example, see discussion of PPS samples below.
Stratified Sampling
Where the population embraces a number of distinct categories, the frame can be organized by these categories into separate "strata." Each stratum is then sampled as an independent subpopulation, out of which individual elements can be randomly selected. There are several potential benefits to stratified sampling.
First, dividing the population into distinct, independent strata can enable researchers to draw inferences about specific subgroups that may be lost in a more generalized random sample.
Second, utilizing a stratified sampling method can lead to more efficient statistical estimates (provided that strata are selected based upon relevance to the criterion in question, instead of availability of the samples). Even if a stratified sampling approach does not lead to increased statistical efficiency, such a tactic will not result in less efficiency than would simple random sampling, provided that each stratum is proportional to the group's size in the population.
Third, it is sometimes the case that data are more readily available for individual, preexisting strata within a population than for the overall population; in such cases, using a stratified sampling approach may be more convenient than aggregating data across groups (though this may potentially be at odds with the previously noted importance of utilizing criterion-relevant strata).
Finally, since each stratum is treated as an independent population, different sampling approaches can be applied to different strata, potentially enabling researchers to use the approach best suited (or most cost-effective) for each identified subgroup within the population.
There are, however, some potential drawbacks to using stratified sampling. First, identifying strata and implementing such an approach can increase the cost and complexity of sample selection, as well as leading to increased complexity of population estimates. Second, when examining multiple criteria, stratifying variables may be related to some, but not to others, further complicating the design, and potentially reducing the utility of the strata. Finally, in some cases (such as designs with a large number of strata, or those with a specified minimum sample size per group), stratified sampling can potentially require a larger sample than would other methods (although in most cases, the required sample size would be no larger than would be required for simple random sampling.
A stratified sampling approach is most effective when three conditions are met
1. Variability within strata are minimized
2. Variability between strata are maximized
3. The variables upon which the population is stratified are strongly correlated with the desired dependent variable.
Advantages over other sampling methods
1. Focuses on important subpopulations and ignores irrelevant ones.
2. Allows use of different sampling techniques for different subpopulations.
3. Improves the accuracy/efficiency of estimation.
4. Permits greater balancing of statistical power of tests of differences between strata by sampling equal numbers from strata varying widely in size.
Disadvantages
1. Requires selection of relevant stratification variables which can be difficult.
2. Is not useful when there are no homogeneous subgroups.
3. Can be expensive to implement.
Post stratification
Stratification is sometimes introduced after the sampling phase in a process called
"post stratification". This approach is typically implemented due to a lack of prior knowledge of an appropriate stratifying variable or when the experimenter lacks the necessary information to create a stratifying variable during the sampling phase. Although the method is susceptible to the pitfalls of post hoc approaches, it can provide several benefits in the right situation. Implementation usually follows a simple random sample. In addition to allowing for stratification on an ancillary variable, Post stratification can be used to implement weighting, which can improve the precision of a sample's estimates.
Oversampling
Choice-based sampling is one of the stratified sampling strategies. In choice-based sampling, the data are stratified on the target and a sample is taken from each stratum so that the rare target class will be more represented in the sample. The model is then built on this biased sample. The effects of the input variables on the target are often estimated with more precision with the choice-based sample even when a smaller overall sample size is taken compared to a random sample. The results usually must be adjusted to correct for the oversampling.
Probability-Proportional-To-Size Sampling
In some cases the sample designer has access to an "auxiliary variable" or "size measure", believed to be correlated to the variable of interest, for each element in the population. These data can be used to improve accuracy in sample design. One option is to use the auxiliary variable as a basis for stratification, as discussed above.
Another option is probability-proportional-to-size ('PPS') sampling, in which the selection probability for each element is set to be proportional to its size measure, up to a maximum of 1. In a simple PPS design, these selection probabilities can then be used as the basis for Poisson sampling.
However, this has the drawback of variable sample size, and different portions of the population may still be over- or under-represented due to chance variation in selections. To address this problem, PPS may be combined with a systematic approach.
Example: Suppose we have six schools with populations of 150, 180, 200, 220, 260, and 490 students respectively (total 1500 students), and we want to use student population as the basis for a PPS sample of size three. To do this, we could allocate the first school numbers 1 to 150, the second school 151 to 330 (= 150 + 180), the third school 331 to 530, and so on to the last school
(1011 to 1500). We then generate a random start between 1 and 500 (equal to 1500/3) and count through the school populations by multiples of 500. If our random start was 137, we would select the schools which have been allocated numbers 137, 637, and 1137, i.e. the first, fourth, and sixth schools.
The PPS approach can improve accuracy for a given sample size by concentrating sample on large elements that have the greatest impact on population estimates. PPS sampling is commonly used for surveys of businesses, where element size varies greatly and auxiliary information is often available - for instance, a survey attempting to measure the number of guest-nights spent in hotels might use each hotel's number of rooms as an auxiliary variable. In some cases, an older measurement of the variable of interest can be used as an auxiliary variable when attempting to produce more current estimates.
Cluster Sampling
Sometimes it is more cost-effective to select respondents in groups ('clusters'). Sampling is often clustered by geography, or by time periods. (Nearly all samples are in some sense 'clustered' in time - although this is rarely taken into account in the analysis.) For instance, if surveying households within a city, we might choose to select 100 city blocks and then interview every household within the selected blocks.
Clustering can reduce travel and administrative costs. In the example above, an interviewer can make a single trip to visit several households in one block, rather than having to drive to a different block for each household.
It also means that one does not need a sampling frame listing all elements in the target population. Instead, clusters can be chosen from a cluster-level frame, with an element-level frame created only for the selected clusters. In the example above, the sample only requires a block-level city map for initial selections, and then a household-level map of the 100 selected blocks, rather than a household-level map of the whole city.
Cluster sampling generally increases the variability of sample estimates above that of simple random sampling, depending on how the clusters differ between themselves, as compared with the within-cluster variation. For this reason, cluster sampling requires a larger sample than
SRS to achieve the same level of accuracy - but cost savings from clustering might still make this a cheaper option.
Cluster sampling is commonly implemented as multistage sampling. This is a complex form of cluster sampling in which two or more levels of units are embedded one in the other. The first stage consists of constructing the clusters that will be used to sample from. In the second stage, a sample of primary units is randomly selected from each cluster (rather than using all units contained in all selected clusters). In following stages, in each of those selected clusters, additional samples of units are selected, and so on. All ultimate units (individuals, for instance) selected at the last step of this procedure are then surveyed. This technique, thus, is essentially the process of taking random subsamples of preceding random samples.
Multistage sampling can substantially reduce sampling costs, where the complete population list would need to be constructed (before other sampling methods could be applied). By eliminating the work involved in describing clusters that are not selected, multistage sampling can reduce the large costs associated with traditional cluster sampling.
Quota Sampling
In quota sampling, the population is first segmented into mutually exclusive sub-groups, just as in stratified sampling. Then judgement is used to select the subjects or units from each segment based on a specified proportion. For example, an interviewer may be told to sample 200 females and 300 males between the age of 45 and 60.
It is this second step which makes the technique one of non-probability sampling. In quota sampling the selection of the sample is non-random. For example interviewers might be tempted to interview those who look most helpful. The problem is that these samples may be biased because not everyone gets a chance of selection. This random element is its greatest weakness and quota versus probability has been a matter of controversy for several years.
Accidental Sampling
Accidental sampling (sometimes known as grab, convenience or opportunity sampling) is a type of non probability sampling which involves the sample being drawn from that part of the population which is close to hand. That is, a population is selected because it is readily available and convenient. It may be through meeting the person or including a person in the sample when one meets them or chosen by finding them through technological means such as the internet or through phone. The researcher using such a sample cannot scientifically make generalizations about the total population from this sample because it would not be representative enough. For example, if the interviewer were to conduct such a survey at a shopping center early in the morning on a given day, the people that he/she could interview would be limited to those given there at that given time, which would not represent the views of other members of society in such an area, if the survey were to be conducted at different times of day and several times per week. This type of sampling is most useful for pilot testing. Several important considerations for researchers using convenience samples include:
1. Are there controls within the research design or experiment which can serve to lessen the impact of a non-random convenience sample, thereby ensuring the results will be more representative of the population?
2. Is there good reason to believe that a particular convenience sample would or should respond or behave differently than a random sample from the same population?
3. Is the question being asked by the research one that can adequately be answered using a convenience sample?
In social science research, snowball sampling is a similar technique, where existing study subjects are used to recruit more subjects into the sample. Some variants of snowball sampling, such as respondent driven sampling, allow calculation of selection probabilities and are probability sampling methods under certain conditions.
4. Duplication.
Sample Size
Sample size is the number of items to be selected from the universe. It should be optimum.
Formulas, tables, and power function charts are well known approaches to determine sample size.
Steps for Using Sample Size Tables
1. Postulate the effect size of interest, α, and β.
2. Check sample size table
1. Select the table corresponding to the selected α
2. Locate the row corresponding to the desired power
3. Locate the column corresponding to the estimated effect size.
4. The intersection of the column and row is the minimum sample size required.
The Factors Considering While Deciding The Size Of The Sample
a) Nature of the population.
b) Complexity of tabulation.
c) Problem relate with collection of data.
d) Type of sampling.
e) Basic information.
f) Degree of accuracy required for the study.
Characteristics of Good Sample Design
a. Representative.
b. Viable.
c. The selected sample design should not cause more errors.
d. A good sample design able to control systematic bias efficiently.
e. If the sample is well design and selected, decision makers can use this info with confidence.
Criteria of Selecting a Sampling Procedure
1. Nature of the problem.
2. Goal of researchers.
3. Geographical area covered by the survey.
4. Size of the population under study.
5. Extent of fact available about population.
6. Availability of funds
7. Available time for study.
8. Desired reliability of the result.
Criteria Used For Selecting Sampling Techniques
 The purpose of the survey.
 Measurability.
 Degree of precision.
 Information about population.
 The nature of the population.
 The geographical area covered by the survey.
 Fund availability.
 Time.
 Economy.
Errors in Sample Surveys
Survey results are typically subject to some error. Total errors can be classified into sampling errors and non-sampling errors. The term "error" here includes systematic biases as well as random errors.
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Sampling Errors and Biases
Sampling errors and biases are induced by the sample design. They include:
1. Selection Bias: When the true selection probabilities differ from those assumed in
calculating the results.
2. Random Sampling Error: Random variation in the results due to the elements in the
sample being selected at random.
Sampling Bias
Sampling analysis involve to type of cost namely cost of collecting data and cost of an
incorrect inference resulting from the data. They are to causes for incorrect inference resulting
from data. They are
i. Systematic bias
ii. Sampling errors
Causes of systematic bias
 Unsuitable sample frame or source list.
 Faulty measuring device.
 Non respondent
 Indeterminacy principle.
 Usual bias in reporting data.
Sampling errors
The errors which arise due to the use of sampling survey are known as sampling errors. These are random variation in the sample estimate around the true population parameters.
Type of sampling errors
Biased errors: These errors are occurring due to the faulty selection of sampling method due to the prejudice of the researchers.
Unbiased errors: This type of bias is occurring due to chance difference between the items included in the sample.
Causes of bias
Bias may arise due to,
1. Faulty process selection.
2. Faulty work during the collection of information.
3. Faulty method of analysis.
Non-Sampling Error
Non-sampling errors are other errors which can impact the final survey estimates, caused by problems in data collection, processing, or sample design. They include:
1. Over coverage: Inclusion of data from outside of the population.
2. Under coverage: Sampling frame does not include elements in the population.
3. Measurement error: e.g. when respondents misunderstand a question, or find it difficult to answer.
4. Processing error: Mistakes in data coding.
5. Non-response: Failure to obtain complete data from all selected individuals.
After sampling, a review should be held of the exact process followed in sampling, rather than that intended, in order to study any effects that any divergences might have on subsequent analysis.
A particular problem is that of non-response.
Two major types of non-response exist: unit non-response (referring to lack of completion of any part of the survey) and item non-response (submission or participation in survey but failing to complete one or more components/questions of the survey). In survey sampling, many of the individuals identified as part of the sample may be unwilling to participate, not have the time to participate (opportunity cost), or survey administrators may not have been able to contact them. In this case, there is a risk of differences, between respondents and non respondents, leading to biased estimates of population parameters. This is often addressed by improving survey design, offering incentives, and conducting follow-up studies which make a repeated attempt to contact the unresponsive and to characterize their similarities and differences with the rest of the frame. The effects can also be mitigated by weighting the data when population benchmarks are available or by imputing data based on answers to other questions.
Non-response is particularly a problem in internet sampling. Reasons for this problem include improperly designed surveys,] over-surveying (or survey fatigue), and the fact that potential participants hold multiple e-mail addresses, which they don't use anymore or don't check regularly.
Sampling and Data Collection
Good data collection involves:
 Following the defined sampling process
 Keeping the data in time order
 Noting comments and other contextual events
Methods of Data Collection
1. Primary data collection
2. Secondary data collection
Collection of Primary Data
Primary data are those data which are collected for the first time and these are in original in character.
Methods of Collecting Primary Data
1. Observation
2. Interview
3. Questionnaire
4. Schedule
5. Experimentation
6. Simulation
7. Use of telephone
8. Panel method
9. Mail survey
10. Projective technique
11. Sociometry
12. Focus group discussion
13. Content analysis
Observation
Observation is the systematic viewing of specific phenomenon in its proper setting for the specific purpose of gathering data for a particular study.
Features of observation
• Physical & mental activity
• Selective
• Purposive & not informal
• Grasps the significant events & occurrences
• Should be exact & based on standardized tools of research
Types of observation
1. Simple and systematic
2. Subjective and objective
3. Casual and scientific
4. Intra subjective and inter subjective
5. Factual and inferential
6. Direct and indirect
7. Participant and non participant
8. Structured and unstructured
Advantages
• Actual or habits of person are observed
• Obtain information from those who are unable to effectively communicate in written or oral form
• No better way to gather information than through observation
• Most reliable method of data collection
Disadvantages
• Result of observation depends on the skill of the observer
• Options and attitudes cannot be obtained by observation
• It should be expensive to tie up personnel in such tasks
• The researcher’s findings are limited to those observed
Component of process of observation
1. Sensation
2. Attention
3. perception
Experimental method
it is the least used method for collecting primary data. This method is commonly used
by marketers in test marketing.
Types;
1. Laboratory experiments
2. Field experiments
Laboratory experiment
A laboratory experiment is an investigation conducted in situation created specifically for that purpose
Field experiment
This is an experiment conducted in real life situation in which the experiments manipulate an independent variable in order to test a hypothesis
Advantages of experimental method • the power to determine the causal relationship between variables is more compared with other methods
• The human errors can be reduced to the minimum
• It helps to produce exact measurement
Limitations of experimental method
• Difficult to establish comparable control & experimental group
• Limited scope
• Lacks realism
• Cannot be used for future study
• Not used for determine opinion ,motive & intention of individual
Simulation
Simulation is a recent research technique. It is a realistic enactment of roles in an imagined situation. There are three uses;
1. Assessment of a situation
2. Understanding a situation
3. Decision making in a situation
Types of Simulation
1. Computer simulation
2. Man simulation
3. Man computer simulation
 

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