Introduction
A sample design is a definite plan for obtaining a sample from a given population.
It refers to the technique or the procedure the researcher would adopt in selecting items for the sample.
Sample design may as well lay down the number of items to be included in the sample i.e., the size of the sample.
Sample design is determined before data is collected. There are many sample designs from which a researcher can choose.
Some designs are relatively more precise and easier to apply than others.
Researcher must select/prepare a sample design which should be reliable and appropriate for his research study.
Census survey and Sample survey
There are two ways of survey- Census survey and Sample survey through which data can be collected.
Census survey means total enumeration i.e., collecting data from each and every unit of the universe, whereas sample survey concentrates on collecting data from a few units of the universe selected scientifically for the purpose.
Since census method is more time taking, expensive and labor intensive, it becomes impractical to depend on it.
Therefore, sample survey is preferred which is scientific, less expensive, less time taking and less labor intensive too.
Steps In Sampling Design
While developing a sampling design, the researcher must pay attention to the following points:
Type of universe: The first step in developing any sample design is to clearly define the set of objects, technically called the Universe, to be studied.
The universe can be finite or infinite. In finite universe the number of items is certain, but in case of an infinite universe the number of items is infinite, i.e., we cannot have any idea about the total number of items.
The population of a city, the number of workers in a factory and the like are examples of finite universes, whereas the number of stars in the sky, listeners of a specific radio programme, number of birds in the world etc. are examples of infinite universes.
Sampling unit: A decision has to be taken concerning a sampling unit before selecting sample.
Sampling unit may be a geographical one such as state, district, village, etc., or a construction unit such as house, flat, etc., or it may be a social unit such as family, club, school, etc., or it may be an individual.
The researcher will have to decide one or more of such units that he has to select for his study.
Source list: It is also known as ‘sampling frame’ from which sample is to be drawn. It contains the names of all items of a universe (in case of finite universe only).
If source list is not available, researcher has to prepare it.
Such a list should be comprehensive, correct, reliable and appropriate. It is extremely important for the source list to be as representative of the population as possible.
Size of sample: This refers to the number of items to be selected from the universe to constitute a sample.
This a major problem before a researcher.
The size of sample should neither be excessively large, nor too small.
It should be optimum. An optimum sample is one which fulfills the requirements of efficiency, representativeness, reliability and flexibility.
The size of population variance needs to be considered as in case of larger variance usually a bigger sample is needed.
The size of population must be kept in view for this also limits the sample size.
Costs too dictates the size of sample that we can draw.
As such, budgetary constraint must invariably be taken into consideration when we decide the sample size.
Parameters of interest: In determining the sample design, one must consider the question of the specific population parameters which are of interest.
For instance, we may be interested in estimating the proportion of persons with some characteristic in the population, or we may be interested in knowing some average or the other measure concerning the population.
There may also be important sub-groups in the population about whom we would like to make estimates.
All this has a strong impact upon the sample design we would accept.
Budgetary constraint: Cost considerations, from practical point of view, have a major impact upon decisions relating to not only the size of the sample but also to the type of sample.
This fact can even lead to the use of a non-probability sample.
Sampling procedure: Finally, the researcher must decide the type of sample he will use i.e., he must decide about the technique to be used in selecting the items for the sample.
In fact, this technique or procedure stands for the sample design itself. Obviously, he must select that design which, for a given sample size and for a given cost, has a smaller sampling error.
Characteristics of a good sample design
(1) Goal-oriented: A sample design should be goal oriented. It should be oriented to the research objectives and fitted to the survey conditions.
(2) Accurate representative of the universe: A sample should be an accurate representative of the universe from which it is taken.
There are different methods for selecting a sample. It will be truly representative only when it represents all types of units or groups in the total population in fair proportions.
In brief sample should be selected carefully as improper sampling is a source of error in the survey.
(3) Proportional: A sample should be proportional. It should be large enough to represent the universe properly. The sample size should be sufficiently large to provide statistical stability or reliability. The sample size should give accuracy required for the purpose of particular study.
(4) Random selection: A sample should be selected at random. This means that any item in the group has a full and equal chance of being selected and included in the sample.
This makes the selected sample truly representative in character.
(5) Economical: A sample should be economical. The objectives of the survey should be achieved with minimum cost and effort.
(6) Practical: A sample design should be practical. The sample design should be simple i.e. it should be capable of being understood and followed in the fieldwork.
Characteristics of a good sample design
(7) Actual information provider: A sample should be designed so as to provide actual information required for the study and also provide an adequate basis for the measurement of its own reliability.
In brief, a good sample should be selected at random and should be adequately proportional.
Different types of Sample design
The process of deriving a sample is called a sampling method.
Sampling forms an integral part of the research design as this method derives the quantitative data and the qualitative data that can be collected as part of a research study.
Sampling methods are characterized into two distinct approaches: Probability sampling and Non-probability sampling.
Probability Sampling Methods
Probability sampling is a method of deriving a sample where the objects are selected from a population-based on the theory of probability.
This method includes everyone in the population, and everyone has an equal chance of being selected. Hence, there is no bias whatsoever in this type of sample.
Each person in the population can subsequently be a part of the research.
Probability sampling can be further classified into four distinct types of samples. They are:
Simple random sampling: The most straightforward way of selecting a sample is simple random sampling.
In this method, each member has an equal chance of being a part of the study.
The objects in this sample population are chosen purely on a random basis, and each member has the same probability of being selected.
For example, if a university dean would like to collect feedback from students about their perception of the teachers and level of education, all 1000 students in the University could be a part of this sample. Any 100 students can be selected at random to be a part of this sample.
Cluster sampling: Cluster sampling is a type of sampling method where the respondent population is divided into equal clusters.
Clusters are identified and included in a sample based on defining demographic parameters such as age, location, sex, etc.
This makes it extremely easy for a survey creator to derive practical inferences from the feedback.
For example, if the FDA wants to collect data about adverse side effects from drugs, they can divide the mainland US into distinctive clusters, like states.
Research studies are then administered to respondents in these clusters. This type of generating a sample makes the data collection in-depth and provides easy to consume and act upon, insights.
Systematic sampling: Systematic sampling is a sampling method where the researcher chooses respondents at equal intervals from a population.
The approach to select the sample is to pick a starting point and then pick respondents at a pre-defined sample interval.
For example, while selecting 1,000 volunteers for the Olympics from an application list of 10,000 people, each applicant is given a count of 1 to 10,000.
Then starting from 1 and selecting each respondent with an interval of 10, a sample of 1,000 volunteers can be obtained.
Stratified random sampling: Stratified random sampling is a method of dividing the respondent population into distinctive but pre-defined parameters in the research design phase.
In this method, the respondents don’t overlap but collectively represent the whole population.
For example, a researcher looking to analyze people from different socioeconomic backgrounds can distinguish respondents into their physical activity levels.
This forms smaller groups of people or samples, and then some objects from these samples can be used for the research study.
Uses of probability sampling
There are multiple uses of probability sampling. They are:
- Reduce Sample Bias: Using the probability sampling method, the bias in the sample derived from a population is negligible to non-existent. The selection of the sample mainly depicts the understanding and the inference of the researcher. Probability sampling leads to higher quality data collection as the sample appropriately represents the population.
- Diverse Population: When the population is vast and diverse, it is essential to have adequate representation so that the data is not skewed towards one demographic.
- Create an Accurate Sample: Probability sampling helps the researchers plan and create an accurate sample. This helps to obtain well-defined data.
Non-probability method
The non-probability method is a sampling method that involves a collection of feedback based on a researcher or statistician’s sample selection capabilities and not on a fixed selection process.
In some situations, the output of a survey conducted with a non-probable sample leads to skewed results, which may not represent the desired target population. But, there are situations such as the preliminary stages of research or cost constraints for conducting research, where non-probability sampling will be much more useful than the other type.
Four types of non-probability sampling explain the purpose of this sampling method in a better manner:
Convenience Sampling
This method is dependent on the ease of access to subjects such as surveying customers at a mall or passers-by on a busy street.
It is usually termed as convenience sampling, because of the researcher’s ease of carrying it out and getting in touch with the subjects.
Researchers have nearly no authority to select the sample elements, and it’s purely done based on proximity and not representativeness.
This non-probability sampling method is used when there are time and cost limitations in collecting feedback.
For example, startups and NGOs usually conduct convenience sampling at a mall to distribute leaflets of upcoming events or promotion of a cause – they do that by standing at the mall entrance and giving out pamphlets randomly.
Judgmental or Purposive sampling
Judgmental or purposive samples are formed by the discretion of the researcher. Researchers purely consider the purpose of the study, along with the understanding of the target audience.
For instance, when researchers want to understand the thought process of people interested in studying for their master’s degree. The selection criteria will be: “Are you interested in doing your masters in …?” and those who respond with a “No” are excluded from the sample.
Snowball Sampling
Snowball sampling is a sampling method that researchers apply when the subjects are difficult to trace.
For example, it will be extremely challenging to survey shelterless people or illegal immigrants.
In such cases, using the snowball theory, researchers can track a few categories to interview and derive results.
Researchers also implement this sampling method in situations where the topic is highly sensitive and not openly discussed—for example, surveys to gather information about HIV Aids.
Not many victims will readily respond to the questions.
Still, researchers can contact people they might know or volunteers associated with the cause to get in touch with the victims and collect information.
Quota Sampling
Quota sampling is defined as a method in which researchers create a sample involving individuals that represent a population.
Researchers choose these individuals according to specific traits or qualities.
They decide and create quotas so that the market research samples can be useful in collecting data.
It is a rapid method of collecting samples.
For example, a cigarette company wants to find out what age group prefers what brand of cigarettes in a particular city.
He/she applies quotas on the age groups of 21-30, 31-40, 41-50, and 51+.
From this information, the researcher gauges the smoking trend among the population of the city.
Uses of non-probability sampling
Non-probability sampling is used for the following:
- Create a hypothesis: Researchers use the non-probability sampling method to create an assumption when limited to no prior information is available. This method helps with the immediate return of data and builds a base for further research.
- Exploratory research: Researchers use this sampling technique widely when conducting qualitative research, exploratory research.
- Budget and time constraints: The non-probability method when there are budget and time constraints, and some preliminary data must be collected. Since the survey design is not rigid, it is easier to pick respondents at random and have them take the survey or questionnaire.
How do you decide on the type of sampling to use?
For any research, it is essential to choose a sampling method accurately to meet the goals of your study. The effectiveness of your sampling relies on various factors. Here are some steps expert researchers follow to decide the best sampling method.
- Jot down the research goals.
- Identify the effective sampling techniques that might potentially achieve the research goals.
- Test each of these methods and examine whether they help in achieving your goal.
- Select the method that works best for the research.
Measurement Scales
Scales of measurement refer to ways in which variables/numbers are defined and categorized.
Each scale of measurement has certain properties which in turn determines the appropriateness for use of certain statistical analyses.
The four scales of measurement are nominal, ordinal, interval, and ratio.
Nominal Scale: 1st Level of Measurement
Nominal Scale is defined as a scale used for labeling variables into distinct classifications and doesn’t involve a quantitative value or order. This scale is the simplest of the four variable measurement scales. Calculations done on these variables will be futile as there is no numerical value of the options.
There are cases where this scale is used for the purpose of classification – the numbers associated with variables of this scale are only tags for categorization or division.
For a question such as:
Where do you live?
1- Suburbs
2- City
3- Town
Nominal scale is often used in research surveys and questionnaires where only variable labels hold significance.
For instance, a customer survey asking –
“Which brand of smartphones do you prefer?”
Options : “Apple”- 1 , “Samsung”-2, “OnePlus”-3.
In this survey question, only the names of the brands are significant for the researcher conducting consumer research. There is no need for any specific order for these brands.
Ordinal Scale: 2nd Level of Measurement
The ordinal scale functions on the concept of the relative position of the objects or labels based on the individual’s choice or preference.
For example, At Amazon.in, every product has a customer review section where the buyers rate the listed product according to their buying experience, product features, quality, usage, etc.
The ratings so provided are as follows:
5 Star – Excellent
4 Star – Good
3 Star – Average
2 Star – Poor
1 Star – Worst
Ordinal Scale Examples
Status at workplace, order of product quality, and order of agreement or satisfaction are some of the most common examples of the ordinal Scale.
These scales are generally used in market research to gather and evaluate relative feedback about product satisfaction, changing perceptions with product upgrades, etc.
For example:
How satisfied are you with our services?
Very Unsatisfied – 1
Unsatisfied – 2
Neutral – 3
Satisfied – 4
Very Satisfied – 5
Interval Scale: 3rd Level of Measurement
An interval scale is also called a cardinal scale which is the numerical labelling with the same difference among the consecutive measurement units. With the help of this scaling technique, researchers can obtain a better comparison between the objects.
For example; A survey conducted by an automobile company to know the number of vehicles owned by the people living in a particular area who can be its prospective customers in future.
It adopted the interval scaling technique for the purpose and provided the units as 1, 2, 3, 4, 5, 6 to select from.
In the scale mentioned above, every unit has the same difference, i.e., 1, whether it is between 2 and 3 or between 4 and 5.
Ratio Scale: 4th Level of Measurement
One of the most superior measurement technique is the ratio scale. Similar to an interval scale, a ratio scale is an abstract number system. It allows measurement at proper intervals, order, categorization and distance, with an added property of originating from a fixed zero point
For example, A health product manufacturing company surveyed to identify the level of obesity in a particular locality. It released the following survey questionnaire: Select a category to which your weight belongs to:
Less than 40 kilograms
40-59 Kilograms
60-79 Kilograms
80-99 Kilograms
100-119 Kilograms
120 – 139 Kilograms
Ratio scale provides the most detailed information as researchers and statisticians can calculate the central tendency using statistical techniques such as mean, median, mode.
Ratio scale accommodates the characteristic of three other variable measurement scales, i.e. labeling the variables, the significance of the order of variables, and a calculable difference between variables (which are usually equidistant).
Because of the existence of true zero value, the ratio scale doesn’t have negative values.
To decide when to use a ratio scale, the researcher must observe whether the variables have all the characteristics of an interval scale along with the presence of the absolute zero value.
Mean, mode and median can be calculated using the ratio scale.
Important Scaling Techniques
Paired Comparison Scaling
A paired comparison symbolizes two variables from which the respondent needs to select one. This technique is mainly used at the time of product testing, to facilitate the consumers with a comparative analysis of the two major products in the market.
To compare more than two objects say comparing P, Q and R, one can first compare P with Q and then the superior one (i.e., one with a higher percentage) with R.
For example, A market survey was conducted to find out consumer’s preference for the network service provider brands, A and B. The outcome of the survey was as follows:
Brand ‘A’ = 57%
Brand ‘B’ = 43%
Thus, it is visible that the consumers prefer brand ‘A’, over brand ‘B’.
Rank Order Scaling
In rank order scaling the respondent needs to rank or arrange the given objects according to his or her preference.
For example, A soap manufacturing company conducted a rank order scaling to find out the orderly preference of the consumers. It asked the respondents to rank the following brands in the sequence of their choice:
The above scaling shows that soap ‘Y’ is the most preferred brand, followed by soap ‘X’, then soap ‘Z’ and the least preferred one is the soap ‘V’.
Constant Sum Scaling
It is a scaling technique where a continual sum of units like dollars, points, etc. is given to the features, attributes and importance of a particular product or service by the respondents.
For example, The respondents belonging to 3 different segments were asked to allocate 50 points to the following attributes of a cosmetic product ‘P’:
Example of a Constant Sum Scale
Q Sort Scaling
A type of comparative scale where respondents are asked to sort between 60-90 objects (usually) into piles according to some criteria. It is a useful technique for sorting relatively large numbers of objects quite quickly.
Ideally, the objects to be sorted shall not be less than 60 and not more than 140. While, the range between 60 to 90 is considered as the most reasonable range. The number of objects to be placed in each pile is pre specified, such that the resulting data represent a normal distribution of objects over the whole set under analysis.
Thus, Q-Sort Scaling helps in assigning ranks to different objects within the same group, and the differences among the groups (piles) is clearly visible.
Example of Q – Sort Scaling
Preference of TV serials using Q – Sort Scaling
Instructions: You are given the names of 60 TV serials.
Please choose 5 serials you ‘prefer most’,
10 serials you ‘like’,
20 to which you are ‘neutral’ (neither like nor dislike).
10 serials you ‘dislike’ and
5 serials you ‘prefer least’. Please list the sorted serial names in the respective columns of the form provided to you.
Continuous Rating Scale
It is a graphical rating scale where the respondents are free to place the object at a position of their choice.
It is done by selecting and marking a point along the vertical or horizontal line which ranges between two extreme criteria.
For example, A mattress manufacturing company used a continuous rating scale to find out the level of customer satisfaction for its new comfy bedding. The response can be taken in the following different ways (stated as versions here):
Example
Itemized Rating Scale
Itemized scale is another essential technique under the non-comparative scales.
It emphasizes on choosing a particular category among the various given categories by the respondents.
Each class is briefly defined by the researchers to facilitate such selection.
The three most commonly used itemized rating scales are as follows:
Likert Scale
Likert Scale: In the Likert scale, the researcher provides some statements and ask the respondents to mark their level of agreement or disagreement over these statements by selecting any one of the options from the five given alternatives.
For example, A shoes manufacturing company adopted the Likert scale technique for its new sports shoe range named Psports shoes. The purpose is to know the agreement or disagreement of the respondents. For this, the researcher asked the respondents to circle a number representing the most suitable answer according to them, in the following representation:
1 – Strongly Disagree
2 – Disagree
3 – Neither Agree Nor Disagree
4 – Agree
5 – Strongly Agree
Semantic Differential Scale
A semantic differential scale is a survey or questionnaire rating scale that asks people to rate a product, company, brand, or any 'entity' within the frames of a multi-point rating option.
These survey answering options are grammatically on opposite adjectives at each end.
For example, love-hate, satisfied-unsatisfied, and likely to return-unlikely to return with intermediate options in between.
Surveys or questionnaires using the semantic differential scale survey feature is the most reliable way to get information on people’s emotional attitude towards a topic of interest.
For example, A well-known brand for watches, carried out semantic differential scaling to understand the customer’s attitude towards its product. The pictorial representation of this technique is as follows:
Stapel Scale
A Stapel scale is that itemized rating scale which measures the response, perception or attitude of the respondents for a particular object through a unipolar rating.
The range of a Stapel scale is between -5 to +5 eliminating 0, thus confining to 10 units.
For example, these options can range from any scales like +2 to -2 or +5 to -5 or even +10 to -10 and they are purely dependent on the nature of the study and the required outcome, all without a neutral point.
Examples
For example, A tours and travel company asked the respondent to rank their holiday package in terms of value for money and user-friendly interface as follows:
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