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Research Methodology Question and Answers
Contents
What is research design? 3
Types of research design: 3
Exploratory Research Design: 4
Causal (Experimental) Research Design: 4
Descriptive Research Design: 5
Case Study Method: 5
What do you mean by sampling? 7
Systematic sampling: 8
Cluster sampling: 9
Difference between Systematic and cluster sampling: 9
Non Probability Sampling (When probability sampling can’t be used): 9
How sample size is determined? 10
Probability Sampling: 10
Probability Sampling Methods: 11
Difference between probability and non probability sampling: 11
What are the issues that one must consider before starting the research process? When is it appropriate to commence the research? 12
What do you mean by research? Briefly describe the different steps involved in research process. 13
What is research measurement? Explain measurement scales. 20
Explain rating and ranking scales. 23
What are the types of variables? Explain with examples. 23
The Dependent and Independent Variables 23
Extraneous and Confounding Variables 23
Operationally Defining a Variable 24
Discuss guidelines for selection of variables and Hypotheses 25
What is hypothesis? Explain in detail different types of Hypothesis and testing of Hypothesis. 27
Elements of a Good Hypothesis 27
What is Hypothesis Testing? 28
Statistical Hypotheses 28
Can We Accept the Null Hypothesis? 28
Hypothesis Tests 28
Decision Errors 29
Decision Rules 29
One-Tailed and Two-Tailed Tests 29
What is data? What are types of data? How are data collected? 29
Secondary Participation 32
Case Studies And Content Analysis 32
What is internal and external validity? What are the factors affecting validity? How the research design is selected depending upon the choice of the type of validity? 33
Write note on research proposal. 35
Discuss content and layout of research report: 36
Important considerations of questionnaire design? 38
Determine the Questions to be Asked 39
Decide on a Layout and Sequence 39
Question Types 40
Open vs. Closed Questions 40
Discuss Business Research: 44
What is research design?
Research design is the plan that promotes systematic management of data collection. The research design is the master plan specifying the methods and procedures for collecting and analyzing the needed information.
Types of research design:
The choice of the appropriate design depends on the objectives of the research and how much is known about the problem and these objectives.
The overall design for a project may include one or more of these three designs as a part(s) of it. Further if more than one design id to be used, typically it progresses from exploratory toward casual.
Three basic types of research designs:
Exploratory:
Objective: To gain background information, to define terms, to clarify problems and develop hypotheses, to establish priorities, to develop questions to be answered.
It is most commonly unstructured, “informal” research that is undertaken to gain background information about the general nature of the research problem. It is usually conducted when the researcher does not know how much the problem and needs additional information or desires new or more recent information.
Methods: Secondary data analysis, Experience surveys, Case analysis, Focus groups, Projective techniques
Descriptive:
Objective: To describe and measure any phenomena at a point in time
It is undertaken to provide answers to questions of who, what, where, and how- but not why.
Two basic classifications:
Cross-sectional studies: These measures units from a sample of the population at only one point of time. Sample surveys are cross-sectional studies whose samples are drawn in such a way as to be representative of a specific population.
Longitudinal studies: These 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.
Causal:
Objective: To determine causality, test hypotheses, to make “if-then” statements, to answer questions
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.
Exploratory Research Design:
Objective: To gain background information, to define terms, to clarify problems and develop hypotheses, to establish priorities, to develop questions to be answered.
Exploratory research is a type of research conducted for a problem that has not been clearly defined. Exploratory research helps determine the best research design, data collection method and selection of subjects. It should draw definitive conclusions only with extreme caution. Given its fundamental nature, exploratory research often concludes that a perceived problem does not actually exist.
Exploratory research often relies on secondary research such as reviewing available literature and/or data, or qualitative approaches such as informal discussions with consumers, employees, management or competitors, and more formal approaches through in-depth interviews, focus groups, projective methods, case studies or pilot studies. The Internet allows for research methods that are more interactive in nature. For example, RSS feeds efficiently supply researchers with up-to-date information; major search engine search results may be sent by email to researchers by services such as Google Alerts; comprehensive search results are tracked over lengthy periods of time by services such as Google Trends; and websites may be created to attract worldwide feedback on any subject.
The results of exploratory research are not usually useful for decision-making by themselves, but they can provide significant insight into a given situation. Although the results of qualitative research can give some indication as to the "why", "how" and "when" something occurs, it cannot tell us "how often" or "how many". Exploratory research is not typically generalizable to the population at large.
Social exploratory research "seeks to find out how people get along in the setting under question, what meanings they give to their actions, and what issues concern them. The goal is to learn 'what is going on here?' and to investigate social phenomena without explicit expectations." This methodology is also at times referred to as a grounded theory approach to qualitative research or interpretive research, and is an attempt to unearth a theory from the data itself rather than from a predisposed hypothesis.
Exploratory research is used when problems are in a preliminary stage. Exploratory research is used when the topic or issue is new and when data is difficult to collect. Exploratory research is flexible and can address research questions of all types (what, why, how). Exploratory research is often used to generate formal hypotheses.
It is most commonly unstructured, “informal” research that is undertaken to gain background information about the general nature of the research problem. It is usually conducted when the researcher does not know how much the problem and needs additional information or desires new or more recent information.
Methods:
Secondary data analysis,
Experience surveys,
Case analysis,
Focus groups,
Projective techniques
Causal (Experimental) Research Design:
Objective: To determine causality, test hypotheses, to make “if-then” statements, to answer questions
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.
If the objective is to determine which variable might be causing a certain behavior, i.e. whether there is a cause and effect relationship between variables, causal research must be undertaken. In order to determine causality, it is important to hold the variable that is assumed to cause the change in the other variable(s) constant and then measure the changes in the other variable(s). This type of research is very complex and the researcher can never be completely certain that there are not other factors influencing the causal relationship, especially when dealing with people’s attitudes and motivations. There are often much deeper psychological considerations that even the respondent may not be aware of.
There are two research methods for exploring the cause and effect relationship between variables:
1. Experimentation: One way of establishing causality between variables is through the use of experimentation. This highly controlled method allows the researcher to manipulate a specific independent variable in order to determine what effect this manipulation would have on other dependent variables. Experimentation also calls for a control group as well as an experimentation group, and subjects would be assigned randomly to either group. The researcher can further decide whether the experiment should take place in a laboratory or in the field, i.e. the "natural" setting as opposed to an "artificial" one. Laboratory research allows the researcher to control and/or eliminate as many intervening variables as possible. For example, the restaurant décor could possibly influence response to a taste test, but a neutral setting would be seen as eliminating this extraneous variable.
In the hospitality and tourism industries, experimentation is used relatively rarely, except perhaps in test marketing a new or revised product or service. The experimental design is conclusive research that is primary research in nature. Experimentation is a quantitative research technique, but depending on how the experiment is set up, it may relate more to observation than direct communication.
2. Simulation: Another way of establishing causality between variables is through the use of simulation.
A sophisticated set of mathematical formula are used to simulate or imitate a real life situation. By changing one variable in the equation, it is possible to determine the effect on the other variables in the equation.
In the hospitality and tourism industries, computer simulation and model building is used extremely rarely. Its use tends to be limited to a few rare impact and forecasting studies. The simulation design is conclusive research that is secondary research in nature. Simulation is a quantitative research technique.
Descriptive Research Design:
Objective: To describe and measure any phenomena at a point in time
It is undertaken to provide answers to questions of who, what, where, and how- but not why.
Descriptive research, also known as statistical research, describes data and characteristics about the population or phenomenon being studied. Descriptive research answers the questions who, what, where, when, "why" and how...
Although the data description is factual, accurate and systematic, the research cannot describe what caused a situation. Thus, Descriptive research cannot be used to create a causal relationship, where one variable affects another. In other words, descriptive research can be said to have a low requirement for internal validity.
The description is used for frequencies, averages and other statistical calculations. Often the best approach, prior to writing descriptive research, is to conduct a survey investigation. Qualitative research often has the aim of description and researchers may follow-up with examinations of why the observations exist and what the implications of the findings are.
In short descriptive research deals with everything that can be counted and studied. But there are always restrictions to that. Your research must have an impact to the lives of the people around you. For example, finding the most frequent disease that affects the children of a town. The reader of the research will know what to do to prevent that disease thus; more people will live a healthy life.
Two basic classifications:
Cross-sectional studies: These measures units from a sample of the population at only one point of time. Sample surveys are cross-sectional studies whose samples are drawn in such a way as to be representative of a specific population.
Longitudinal studies: These 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.
Case Study Method:
Case study research excels at bringing us to an understanding of a complex issue or object and can extend experience or add strength to what is already known through previous research. Case studies emphasize detailed contextual analysis of a limited number of events or conditions and their relationships. Researchers have used the case study research method for many years across a variety of disciplines. Social scientists, in particular, have made wide use of this qualitative research method to examine contemporary real-life situations and provide the basis for the application of ideas and extension of methods
ATTACKING THE CASE
Your first reaction upon reading a case will probably be to feel over whelmed by all the information. Upon closer reading, you may feel that the case is missing some information that is vital to your decision. Don't despair. Case writers do this on purpose to make the cases represent as closely as possible the typical situations faced by agribusiness managers. In this age of computers, managers often have to sift through an excessive amount of information to glean the facts needed to make a decision. In other situations, there is too little information and too little time or money to collect all the information desired. One definition of management is "the art of using scanty information to make terribly important, semi-permanent decisions under time pressure." One reason for using the case-study method is for you to learn how to function effectively in that type of decision-making environment.
When assigned a case that does not contain all the information you need, you can do two things: First, seek additional information. Library research or a few telephone calls may provide the necessary facts. Second, you can make assumptions when key facts or data are not available. Your assumptions should be reasonable and consistent with the situation because the "correctness" of your solution may depend upon the assumptions you make. This is one reason that a case can have more than one right solution. In fact, your teacher may be more interested in the analysis and process you used to arrive at the decision than in its absolute correctness.
The Seven Steps of Problem Analysis
Using an organized seven-stem approach in analyzing a case will make the entire process easier and can increase your learning benefits.
1. Read the case thoroughly. To understand fully what is happening in a case, it is necessary to read the case carefully and thoroughly. You may want to read the case rather quickly the first time to get an overview of the industry, the company, the people, and the situation. Read the case again more slowly, making notes as you go.
2. Define the central issue. Many cases will involve several issues or problems. Identify the most important problems and separate them from the more trivial issues. After identifying what appears to be a major underlying issue, examine related problems in the functional areas (for example, marketing, finance, personnel, and so on). Functional area problems may help you identify deep-rooted problems that are the responsibility of top management.
3. Define the firm's goals. Inconsistencies between a firm's goals and its performance may further highlight the problems discovered in step 2. At the very least, identifying the firm's goals will provide a guide for the remaining analysis.
4. Identify the constraints to the problem. The constraints may limit the solutions available to the firm. Typical constraints include limited finances, lack of additional production capacity, personnel limitations, strong competitors, relationships with suppliers and customers, and so on. Constraints have to be considered when suggesting a solution.
5. Identify all the relevant alternatives. The list should all the relevant alternatives that could solve the problem(s) that were identified in step 2. Use your creativity in coming up with alternative solutions. Even when solutions are suggested in the case, you may be able to suggest better solutions.
6. Select the best alternative. Evaluate each alternative in light of the available information. If you have carefully taken the proceeding five steps, a good solution to the case should be apparent. Resist the temptation to jump to this step early in the case analysis. You will probably miss important facts, misunderstand the problem, or skip what may be the best alternative solution. You will also need to explain the logic you used to choose one alternative and reject the others.
7. Develop an implementation plan. The final step in the analysis is to develop a plan for effective implementation of your decision. Lack of an implementation plan even for a very good decision can lead to disaster for a firm and for you. Don't overlook this step. Your teacher will surely ask you or someone in the class to explain how to implement the decision.
The Report
The course instructor may require a written or an oral report describing your solution to the case. The high quality of your analysis or the brilliance of your insights will do you little good if your solution is not expressed clearly. The teacher is more likely to accept your solution even if he or she does not agree with it, if you are able to identify the issues, explain the analysis and logic that led you to choose a particular alternative, and lay out a good plan for implementing the decision.
What do you mean by sampling?
What is sampling?
It is not possible, nor it is necessary, to collect information from the total population. Instead, a smaller subgroup of the target population or a sample is selected for the purpose of study. Sampling is the strategy of selecting a smaller section of the population that will accurately represent the patterns of the target population at large.
Why Take a Sample?
|The main purposes of sampling are : | |
|Economies on the resources required for collecting and managing the data from a smaller | |
|sub-group | |
|Improve quality of data by focusing on a smaller group | |
What is Sampling Frame?
First, define your sampling frame i.e. what group of persons or households or farms are relevant for you and will be eligible to be drawn for the sample? Is it only the farmers who have land holdings or the larger population of all farmers including landless farmers? Is it only the persons who watch TV regularly or all those including those who watch occasionally? The results would be different in each case.
Sampling Procedures
Different procedures are used for selecting a sample for the purpose of data collection. Broadly, these are of two major types:-
• Probability Sampling
• Purposive Sampling
Probability Sampling
In this, sample is taken in such a manner that each and every unit of the population has an equal and positive chance of being selected. In this way, it is ensured that the sample would truly represent the overall population.
Probability sampling can be achieved by random selection of the sample among all the units of the population.
If you intend to establish baseline data or want to assess the changes, effects or the impact that has taken place after the project has been in operation for some time, you go in for probability sample design. This design is generally used in quantitative studies. Random selection of the sample units enables you to confidently generalize results from the small sample to the larger population.
Major random sampling procedures are:
▪ Simple Random Sample
▪ Systematic Random Sample
▪ Stratified Random Sample
▪ Quota Sample, and
▪ Cluster Sample
THE ADVANTAGES OF SAMPLING
* It involves a smaller amount of subjects, which reduces investment in time and money.
* Sampling can actually be more accurate than studying an entire population, because it affords researchers a lot more control over the subjects. Large studies can bury interesting correlations amongst the ‘noise.’
* Statistical manipulations are much easier with smaller data sets, and it is easier to avoid human error when inputting and analyzing the data.
THE DISADVANTAGES OF SAMPLING
* There is room for potential bias in the selection of suitable subjects for the research. This may be because the researcher selects subjects that are more likely to give the desired results, or that the subjects tend to select themselves.
For example, if an opinion poll company canvasses opinion by phoning people between 9am and 5pm, they are going to miss most people who are out working, totally invalidating their results. These are called determining factors, and also include poor experiment design, confounding variables and human error.
* Sampling requires knowledge of statistics, and the entire design of the experiment depends upon the exact sampling method required.
Systematic sampling:
Systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equal-probability method, in which every kth element in the frame is selected, where k, the sampling interval (sometimes known as the skip), is calculated as:[1]
k = \frac Nn
where n is the sample size, and N is the population size.This is one of the method that has been used.
Using this procedure each element in the population has a known and equal probability of selection. This makes systematic sampling functionally similar to simple random sampling. It is however, much more efficient (if variance within systematic sample is more than variance of population).
The researcher must ensure that the chosen sampling interval does not hide a pattern. Any pattern would threaten randomness. A random starting point must also be selected.
Systematic sampling is to be applied only if the given population is logically homogeneous, because systematic sample units are uniformly distributed over the population.
Example: Suppose a supermarket wants to study buying habits of their customers, then using systematic sampling they can choose every 10th or 15th customer entering the supermarket and conduct the study on this sample.
This is random sampling with a system. From the sampling frame, a starting point is chosen at random, and choices thereafter are at regular intervals. For example, suppose you want to sample 8 houses from a street of 120 houses. 120/8=15, so every 15th house is chosen after a random starting point between 1 and 15. If the random starting point is 11, then the houses selected are 11, 26, 41, 56, 71, 86, 101, and 116.
If, as more frequently, the population is not evenly divisible (suppose you want to sample 8 houses out of 125, where 125/8=15.625), should you take every 15th house or every 16th house? If you take every 16th house, 8*16=128, so there is a risk that the last house chosen does not exist. On the other hand, if you take every 15th house, 8*15=120, so the last five houses will never be selected. The random starting point should instead be selected as a non integer between 0 and 15.625 (inclusive on one endpoint only) to ensure that every house has equal chance of being selected; the interval should now be non integral (15.625); and each non integer selected should be rounded up to the next integer. If the random starting point is 3.6, then the houses selected are 4, 19, 35, 51, 66, 82, 98, and 113, where there are 3 cyclic intervals of 15 and 5 intervals of 16.
To illustrate the danger of systematic skip concealing a pattern, suppose we were to sample a planned neighborhood where each street has ten houses on each block. This places houses #1, 10, 11, 20, 21, 30... on block corners; corner blocks may be less valuable, since more of their area is taken up by street front etc. that is unavailable for building purposes. If we then sample every 10th household, our sample will either be made up only of corner houses (if we start at 1 or 10) or have no corner houses (any other start); either way, it will not be representative.
Systematic sampling may also be used with non-equal selection probabilities. In this case, rather than simply counting through elements of the population and selecting every kth unit, we allocate each element a space along a number line according to its selection probability. We then generate a random start from a uniform distribution between 0 and 1, and move along the number line in steps of 1.
Example: We have a population of 5 units (A to E). We want to give unit A a 20% probability of selection, unit B a 40% probability, and so on up to unit E (100%). Assuming we maintain alphabetical order, we allocate each unit to the following interval: qwewqeqe qw eqw e qw e qw e wq e qwe
A: 0 to 0.2
B: 0.2 to 0.6 (= 0.2 + 0.4)
C: 0.6 to 1.2 (= 0.6 + 0.6)
D: 1.2 to 2.0 (= 1.2 + 0.8)
E: 2.0 to 3.0 (= 2.0 + 1.0)
If our random start was 0.156, we would first select the unit whose interval contains this number (i.e. A). Next, we would select the interval containing 1.156 (element C), then 2.156 (element E). If instead our random start was 0.350, we would select from points 0.350 (B), 1.350 (D), and 2.350 (E).
Cluster sampling:
Cluster sampling refers to a sampling method that has the following properties.
* The population is divided into N groups, called clusters.
* The researcher randomly selects n clusters to include in the sample.
* The number of observations within each cluster Mi is known, and M = M1 + M2 + M3 + ... + MN-1 + MN.
* Each element of the population can be assigned to one, and only one, cluster.
This tutorial covers two types of cluster sampling methods.
* One-stage sampling. All of the elements within selected clusters are included in the sample.
* Two-stage sampling. A subset of elements within selected clusters are randomly selected for inclusion in the sample.
Cluster Sampling: Advantages and Disadvantages
Assuming the sample size is constant across sampling methods, cluster sampling generally provides less precision than either simple random sampling or stratified sampling. This is the main disadvantage of cluster sampling.
Given this disadvantage, it is natural to ask: Why use cluster sampling? Sometimes, the cost per sample point is less for cluster sampling than for other sampling methods. Given a fixed budget, the researcher may be able to use a bigger sample with cluster sampling than with the other methods. When the increased sample size is sufficient to offset the loss in precision, cluster sampling may be the best choice.
When to Use Cluster Sampling
Cluster sampling should be used only when it is economically justified - when reduced costs can be used to overcome losses in precision. This is most likely to occur in the following situations.
* Constructing a complete list of population elements is difficult, costly, or impossible. For example, it may not be possible to list all of the customers of a chain of hardware stores. However, it would be possible to randomly select a subset of stores (stage 1 of cluster sampling) and then interview a random sample of customers who visit those stores (stage 2 of cluster sampling).
* The population is concentrated in "natural" clusters (city blocks, schools, hospitals, etc.). For example, to conduct personal interviews of operating room nurses, it might make sense to randomly select a sample of hospitals (stage 1 of cluster sampling) and then interview all of the operating room nurses at that hospital. Using cluster sampling, the interviewer could conduct many interviews in a single day at a single hospital. Simple random sampling, in contrast, might require the interviewer to spend all day traveling to conduct a single interview at a single hospital.
Difference between Systematic and cluster sampling:
Non Probability Sampling (When probability sampling can’t be used):
Sampling is the use of a subset of the population to represent the whole population. Probability sampling, or random sampling, is a sampling technique in which the probability of getting any particular sample may be calculated. Non probability sampling does not meet this criterion and should be used with caution. Non probability sampling techniques cannot be used to infer from the sample to the general population. Any generalizations obtained from a non probability sample must be filtered through one's knowledge of the topic being studied. Performing non probability sampling is considerably less expensive than doing probability sampling, but the results are of limited value.
Examples of non probability sampling include:
* Convenience, Haphazard or Accidental sampling - members of the population are chosen based on their relative ease of access. To sample friends, co-workers, or shoppers at a single mall, are all examples of convenience sampling.
* Snowball sampling - The first respondent refers a friend. The friend also refers a friend, etc.
* Judgmental sampling or Purposive sampling - The researcher chooses the sample based on who they think would be appropriate for the study. This is used primarily when there is a limited number of people that have expertise in the area being researched.
* Deviant Case - Get cases that substantially differ from the dominant pattern (a special type of purposive sample).
* Case study - The research is limited to one group, often with a similar characteristic or of small size.
* ad hoc quotas - A quota is established (say 65% women) and researchers are free to choose any respondent they wish as long as the quota is met.
Even studies intended to be probability studies sometimes end up being non-probability studies due to unintentional or unavoidable characteristics of the sampling method. In public opinion polling by private companies (or other organizations unable to require response), the sample can be self-selected rather than random. This often introduces an important type of error: self-selection bias. This error sometimes makes it unlikely that the sample will accurately represent the broader population. Volunteering for the sample may be determined by characteristics such as submissiveness or availability. The samples in such surveys should be treated as non-probability samples of the population, and the validity of the estimates of parameters based on them unknown.
How sample size is determined?
Sample size determination is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample. In practice, the sample size used in a study is determined based on the expense of data collection, and the need to have sufficient statistical power. In complicated studies there may be several different sample sizes involved in the study: for example, in as survey sampling involving stratified sampling there would be different sample sizes for each population. In a census, data are collected on the entire population, hence the sample size is equal to the population size. In experimental design, where a study may be divided into different treatment groups, there may be different sample sizes for each group.
Sample sizes may be chosen in several different ways:
* expedience - For example, include those items readily available or convenient to collect. A choice of small sample sizes, though sometimes necessary, can result in wide confidence intervals or risks of errors in statistical hypothesis testing.
* using a target variance for an estimate to be derived from the sample eventually obtained
* using a target for the power of a statistical test to be applied once the sample is collected.
Steps to determine effective sample size:
1. Population: The reach or total number of people to whom you want to apply the data.
2. Probability or percentage: The percentage of people you expect to respond to your survey or campaign.
3. Confidence: How confident you need to be that your data is accurate. Expressed as a percentage, the typical value is 95% or 0.95.
4. Margin of Error or Confidence Interval: The amount of sway or potential error you will accept. It’s the “+/-” value you see in media polls. The smaller the percentage, the larger your sample size will need to be.
For example, if 45% of your survey respondees choose a particular answer and you have a 5% (+/- 5) margin of error, then you can assume that 40%-50% of the entire population will choose the same answer.
Probability Sampling:
A probability sampling scheme 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, 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.
Probability Sampling Methods:
Simple Random Sampling
Simple random sampling is the simplest form of random sampling. It is the basic sampling technique where you select a group of subjects, a sample, for study from a larger group, a population. Each individual is chosen entirely by chance and each member of the population has an equal chance of being included in the sample. Every possible sample of a given size has the same chance of selection. As a result, each member of the population is equally likely to be chosen at any stage in the sampling process.
For example, the thingamajig at the top is an ideal model of simple random sampling. Press the "Start" button to start the random selection. You will notice that at every second the thingamabob will pick up one of the three numbers 1, 2, or 3. You can terminate the process anytime by pressing the "Stop" button.
Randomly picking clients from a list of clients is another example of simple random sampling.
Simple random sampling is simple to accomplish and is easy to explain to others because it is a fair way to select a sample, it is reasonable to generalize the results from the sample back to the population. However, it is not the most statistically efficient method of sampling. It does not get a good representation of subgroups in a population because of the luck of the draw. To deal with these issues, we have to turn to other sampling methods.
Stratified Random Sampling
A stratified random sample, also called proportional or quota random sample, is obtained by taking samples from each stratum or sub-group of a population. It involves dividing your population into homogeneous subgroups and then taking a simple random sample in each subgroup. Stratified sampling techniques are generally used when the population is heterogeneous, or dissimilar, where certain homogeneous, or similar, sub-populations can be isolated. Simple random sampling is most appropriate when the entire population from which the sample is taken is homogeneous. There are several reasons why you would prefer stratified sampling over simple random sampling. Firstly, it assures that you will be able to represent not only the overall population, but also key subgroups of the population, especially small minority groups. Secondly, the cost per observation in the survey may be reduced and lastly, it provides each sub-population estimates of the population parameters.
Splitting clients into three different groups and picking from them is another example of stratified random sampling.
Take a farmer for example. Suppose he wishes to work out the average milk yield of each cow type in his herd which consists of Ayrshire, Friesian, Galloway and Jersey cows. He could divide up his herd into the four sub-groups and take samples from these.
Cluster Random Sampling
Cluster sampling is a sampling technique where the entire population is divided into groups, or clusters, and a random sample of these clusters are selected. All observations in the selected clusters are included in the sample. It is typically used when the researcher cannot get a complete list of the members of a population they wish to study but can get a complete list of groups or clusters of the population. It is also used when a random sample would produce a list of subjects so widely scattered that surveying them would prove to be far too expensive, for example, people who live in different postal districts in the UK. This sampling technique is more practical and economical than simple random sampling or stratified sampling. The problem with random sampling methods when we have to sample a population that's disbursed across a wide geographic region is that you will have to cover a lot of ground geographically in order to get to each of the units you sampled. Imagine taking a simple random sample of all the residents of New York State in order to conduct personal interviews. By the luck of the draw you will wind up with respondents who come from all over the state. Your interviewers are going to have a lot of traveling to do.
For instance, in the figure we see a map of the counties in New York State. Let's say that we have to do a survey of town governments that will require us going to the towns personally. If we do a simple random sample state-wide we'll have to cover the entire state geographically. Instead, we decide to do a cluster sampling of five counties, marked in red in the figure. Once these are selected, we go to every town government in the five areas. Clearly this strategy will help us to economize on our mileage. Cluster or area sampling, then, is useful in situations like this, and is done primarily for efficiency of administration.
Take this as another example, suppose that the Department of Agriculture wishes to investigate the use of pesticides by farmers in England. A cluster sample could be taken by identifying the different counties in England as clusters. A sample of these counties, clusters, would then be chosen at random, so all farmers in those counties selected would be included in the sample. It can be seen here then that it is easier to visit several farmers in the same county than it is to travel to each farm in a random sample to observe the use of pesticides.
Difference between probability and non probability sampling:
The difference is just that non-probability sampling does not involve random selection, but probability sampling does.
Probability methods require a sample frame (a comprehensive list of the population of interest). Probability methods rely on random selection in a variety of ways from the sample frame of the population. They permit the use of higher level statistical techniques which require random selection, and allow you to calculate the difference between your sample results and the population equivalent values so that you can confidently state that you know the population values. Non-probability methods do not.
However non-probability samples cannot be dismissed by this apparent lack of rigor. They are available even when you have no sample frame. They are generally less complicated to undertake. They may minimize the preparation costs of a survey, and be employed when you are actually unsure of the population of interest.
What are the issues that one must consider before starting the research process? When is it appropriate to commence the research?
Although it is desirable for research to be thoroughly grounded in management decision priorities, studies can wander off target or be less effective than they should be.
Some researchers are method-bound. They recast the management question so it is amenable to their favorite methodology—a survey, for example. Others might prefer to emphasize the case study, while still others wouldn’t consider either approach. Not all researchers are comfortable with experimental designs. The past reluctance of most social scientists to use experimental designs is believed to have retarded the development of scientific research in the social science arena.
The availability of technique is an important factor in determining how research will be done or whether a given study can be done. Persons knowledgeable about and skilled in some techniques but not in others are too often blinded by their special competencies. Their concern for technique dominates the decisions concerning what will be studied (both investigative and measurement questions) and how (research design).
Since the advent of Total Quality Management (TQM), numerous, standardized customer satisfaction questionnaires have been developed. Jason may have done studies using these instruments for any number of his clients. Myra should be cautious. She must not let Jason steamroll her into the use of an instrument he has developed for another client, even though he might be very persuasive about its success in the past.
Such a technique might not be appropriate for MindWriter’s search to resolve postpurchase service dissatisfaction.
The existence of a pool of information or a database can distract a manager, seemingly reducing the need for other research. As evidence of the research-as-expense-notinvestment mentality mentioned in Chapter 1, managers frequently hear from superiors, “We should use the information we already have before collecting more.” Modern management information systems are capable of providing massive volumes of data. This is not the same as saying modern management information systems provide substantial knowledge.
Each field in a database was originally created for a specific reason, a reason that may or may not be compatible with the management question facing the organization.
The MindWriter service department’s database, for example, probably contains several fields about the type of problem, the location of the problem, the remedy used to correct the problem, and so forth. Jason and Myra can accumulate facts concerning the service, and they can match each service problem with a particular MindWriter model and production sequence (from a production database), and, using yet another database (generated from warranty registration), they can match each problem to a name and address of an owner. But, having done all that, they still aren’t likely to know how a particular owner uses his or her laptop or how satisfied an owner was with MindWriter’s postpurchase service policies and practices.
Mining management information databases is fashionable and all types of organizations increasingly value the ability to extract meaningful information. While such data mining is often a starting point in decision-based research, rarely will such activity answer all management questions related to a particular management dilemma.
Not all management questions are researchable, and not all research questions are answerable. To be researchable, a question must be one for which observation or other data collection can provide the answer. Many questions cannot be answered on the basis of information alone.
Questions of value and policy often must be weighed in management decisions. In the MetalWorks study, management may be asking, “Should we hold out for a liberalization of the seniority rules in our new labor negotiations?” While information can be brought to bear on this question, such additional considerations as “fairness to the workers” or “management’s right to manage” may be important to the decision. It may be possible for many of these questions of value to be transformed into questions of fact. Concerning “fairness to the workers,” one might first gather information from which to estimate the extent and degree to which workers will be affected by a rule change; then one could gather opinion statements by the workers about the fairness of seniority rules. Even so, substantial value elements remain. Questions left unanswered include, “Should we argue for a policy that will adversely affect the security and wellbeing of older workers who are least equipped to cope with this adversity?” Even if a question can be answered by facts alone, it might not be researchable because currently accepted and tested procedures or techniques are inadequate.
Some categories of problems are so complex, value-laden, and bound by constraints that they prove to be intractable to traditional forms of analysis. These questions have characteristics that are virtually the opposite of those of well-defined problems. One author describes the differences like this:
To the extent that a problem situation evokes a high level of agreement over a specified community of problem solvers regarding the referents of the attributes in which it is given, the operations that are permitted, and the consequences of those operations, it may be termed unambiguous or well defined with respect to that community. On the other hand, to the extent that a problem evokes a highly variable set of responses concerning referents of attributes, permissible operations, and their consequences, it may be considered ill-defined or ambiguous with respect to that community.2
Another author points out that ill-defined research questions are least susceptible to attack from quantitative research methods because such problems have too many interrelated facets for measurement to handle with accuracy.3 Yet another authority suggests there are some research questions of this type for which methods do not presently exist or, if the methods were to be invented, they still might not provide the data necessary to solve them.4 Novice researchers should avoid ill-defined problems. Even seasoned researchers will want to conduct a thorough exploratory study before proceeding with the latest approaches.
It is important to remember that a manager’s motivations for seeking research are not always obvious. Managers might express a genuine need for specific information on which to base a decision. This is the ideal scenario for quality research. Sometimes, however, a research study may not really be desirable but is authorized anyway, chiefly because its presence may win approval for a certain manager’s pet idea. At other times, research may be authorized as a measure of personal protection for a decision maker in case he or she is criticized later. In these less-than-ideal cases, the researcher may find it more difficult to win the manager’s support for an appropriate research design.
What do you mean by research? Briefly describe the different steps involved in research process.
DEFINITION OF RESEARCH
When you say that you are undertaking a research study to find answers to a question, you are implying that the process;
1. is being undertaken within a framework of a set of philosophies ( approaches);
2. uses procedures, methods and techniques that have been tested for their validity and reliability;
3. is designed to be unbiased and objective .
Philosophies mean approaches e.g. qualitative, quantitative and the academic discipline in which you have been trained.
Validity means that correct procedures have been applied to find answers to a question. Reliability refers to the quality of a measurement procedure that provides repeatability and accuracy.
Unbiased and objective means that you have taken each step in an unbiased manner and drawn each conclusion to the best of your ability and without introducing your own vested interest.
CHARACTERISTICS OF RESEARCH:
Research is a process of collecting, analyzing and interpreting information to answer questions.
But to qualify as research, the process must have certain characteristics: it must, as far as possible, be controlled, rigorous, systematic, valid and verifiable, empirical and critical.
-Controlled- in real life there are many factors that affect an outcome. The concept of control implies that, in exploring causality in relation to two variables (factors), you set up your study in a way that minimizes the effects of other factors affecting the relationship.
This can be achieved to a large extent in the physical sciences (cookery, bakery), as most of the research is done in a laboratory. However, in the social sciences (Hospitality and Tourism) it is extremely difficult as research is carried out on issues related to human beings living in society, where such controls are not possible.
Therefore in Hospitality and Tourism, as you cannot control external factors, you attempt to quantify their impact.
-Rigorous-you must be scrupulous in ensuring that the procedures followed to find answers to questions are relevant, appropriate and justified. Again, the degree of rigor varies markedly between the physical and social sciences and within the social sciences.
-Systematic-this implies that the procedure adopted to undertake an investigation follow a certain logical sequence. The different steps cannot be taken in a haphazard way. Some procedures must follow others.
-Valid and verifiable-this concept implies that whatever you conclude on the basis of your findings is correct and can be verified by you and others.
-Empirical-this means that any conclusions drawn are based upon hard evidence gathered from information collected from real life experiences or observations.
-Critical-critical scrutiny of the procedures used and the methods employed is crucial to a research enquiry. The process of investigation must be foolproof and free from drawbacks. The process adopted and the procedures used must be able to withstand critical scrutiny.
For a process to be called research, it is imperative that it has the above characteristics.
THE RESEARCH PROCESS
The research process is similar to undertaking a journey.
For a research journey there are two important decisions to make-
1) What you want to find out about or what research questions (problems) you want to find answers to;
2) How to go about finding their answers.
There are practical steps through which you must pass in your research journey in order to find answers to your research questions.
The path to finding answers to your research questions constitutes research methodology.
At each operational step in the research process you are required to choose from a multiplicity of methods, procedures and models of research methodology which will help you to best achieve your objectives.
This is where your knowledge base of research methodology plays a crucial role.
Steps in Research Process:
1. Formulating the Research Problem
2. Extensive Literature Review
3. Developing the objectives
4. Preparing the Research Design including Sample Design
5. Collecting the Data
6. Analysis of Data
7. Generalisation and Interpretation
8. Preparation of the Report or Presentation of Results-Formal write ups of conclusions reached.
Step1. Formulating the research problem:
It is the first and most crucial step in the research process
- Main function is to decide what you want to find out about.
- The way you formulate a problem determines almost every step that follows.
Sources of research problems
Research in social sciences revolves around four Ps:
• People- a group of individuals
• Problems- examine the existence of certain issues or problems relating to their lives; to ascertain attitude of a group of people towards an issue
• Programs- to evaluate the effectiveness of an intervention
• Phenomena- to establish the existence of a regularity.
In practice most research studies are based upon at least a combination of two Ps.
Every research study has two aspects:
1. Study population-
• People: individuals, organizations, groups, communities ( they provide you with the information or you collect information about them)
2. Subject area-
• Problems: issues, situations, associations, needs, profiles
• Program : content, structure, outcomes, attributes, satisfactions, consumers, Service providers, etc.
• Phenomenon: cause-and-effect relationships, the study of a phenomenon itself
(Information that you need to collect to find answers to your research questions)
You can examine the professional field of your choice in the context of the four Ps in order to identify anything that looks interesting.
Considerations in selecting a research problem:
These help to ensure that your study will remain manageable and that you will remain motivated.
1. Interest: a research endeavor is usually time consuming, and involves hard work and possibly unforeseen problems. One should select topic of great interest to sustain the required motivation.
2. Magnitude: It is extremely important to select a topic that you can manage within the time and resources at your disposal. Narrow the topic down to something manageable, specific and clear.
3. Measurement of concepts: Make sure that you are clear about the indicators and measurement of concepts (if used) in your study.
4. Level of expertise: Make sure that you have adequate level of expertise for the task you are proposing since you need to do the work yourself.
5. Relevance: Ensure that your study adds to the existing body of knowledge, bridges current gaps and is useful in policy formulation. This will help you to sustain interest in the study.
6. Availability of data: Before finalizing the topic, make sure that data are available.
7. Ethical issues: How ethical issues can affect the study population and how ethical problems can be overcome should be thoroughly examined at the problem formulating stage.
Steps in formulation of a research problem :
Working through these steps presupposes a reasonable level of knowledge in the broad subject area within which the study is to be undertaken. Without such knowledge it is difficult to clearly and adequately ‘dissect’ a subject area.
Step 1 Identify a broad field or subject area of interest to you.
Step 2 Dissect the broad area into sub areas.
Step 3 Select what is of most interest to you.
Step 4 Raise research questions.
Step 5 Formulate objectives.
Step 6 Assess your objectives.
Step 7 Double check.
Step 2. Reviewing the literature:
-Essential preliminary task in order to acquaint yourself with the available body of knowledge in your area of interest.
-Literature review is integral part of entire research process and makes valuable contribution to every operational step.
-Reviewing literature can be time-consuming, daunting and frustrating, but is also rewarding. Its functions are:
a. Bring clarity and focus to your research problem;
b. Improve your methodology;
c. Broaden your knowledge;
d. Contextualise your findings.
a.Bring clarity and focus to your research problem;
The process of reviewing the literature helps you to understand the subject area better and thus helps you to conceptualise your research problem clearly and precisely. It also helps you to understand the relationship between your research problem and the body of knowledge in the area.
b.Improve your methodology:
A literature review tells you if others have used procedures and methods similar to the ones that you are proposing, which procedures and methods have worked well for them, and what problems they have faced with them. Thus you will be better positioned to select a methodology that is capable of providing valid answer to your research questions.
c.Broaden your knowledge base in your research area:
It ensures you to read widely around the subject area in which you intend to conduct your research study. As you are expected to be an expert in your area of study, it helps fulfill this expectation. It also helps you to understand how the findings of your study fit into the existing body of knowledge.
d..Contextualize your findings:
How do answers to your research questions compare with what others have found? What contribution have you been able to make in to the existing body of knowledge? How are your findings different from those of others? For you to be able to answer these questions, you need to go back to your literature review. It is important to place your findings in the context of what is already known in your field of enquiry.
Procedure for reviewing the literature:
i) search for existing literature in your area of study;
ii) review the literature selected;
iii) develop a theoretical framework;
iv) develop a conceptual framework.
Step 3 The formulation of objectives:
-Objectives are the goals you set out to attain in your study.
-They inform a reader what you want to attain through the study.
-It is extremely important to word them clearly and specifically.
Objectives should be listed under two headings:
a) main objectives ( aims);
b) sub-objectives.
• The main objective is an overall statement of the thrust of your study.
It is also a statement of the main associations and relationships that you seek to discover or establish.
• The sub-objectives are the specific aspects of the topic that you want to investigate within the main framework of your study.
-They should be numerically listed.
-Wording should clearly, completely and specifically
Communicate to your readers your intention.
-Each objective should contain only one aspect of the Study.
-Use action oriented words or verbs when writing objectives.
The objectives should start with words such as
‘to determine’,
‘to find out’,
‘to ascertain’,
‘to measure’,
‘to explore’ etc.
The wording of objectives determines the type of research (descriptive, correlational and experimental) and the type of research design you need to adopt to achieve them.
e.g.
Descriptive studies:
-To describe the types of incentives provides by Hotel XYZ to employees in Mumbai.
-To find out the opinion of the employees about the medical facilities provided by five star hotels in Mumbai.
Correlatinal studies:
-To ascertain the impact of training on employee retention.
-To compare the effectivenesss of different loyalty programmes on repeat clientele.
Hypothesis –testing studies:
-To ascertain if an increase in working hours will increase the incidence of drug/alchohol abuse.
-To demonstrate that the provision of company accommodation to employees in Mumbai hotels will reduce staff turnover.
Identifying Variables:
In a research study it is important that the concepts used should be operationalised in measurable terms so that the extent of variations in respondents’ understanding is reduced if not eliminated.
Techniques about how to operationalise concepts, and knowledge about variables, play an important role in reducing this variability.
Their knowledge, therefore is important in ‘fine tuning’ your research problem.
For example:
-‘Jet Airways’ is a perfect example of quality cabin service.
- Food in this restaurant is excellent.
- The middle class in India is getting more prosperous.
Step 4. PREPARING THE RESEARCH DESIGN
Research design is the conceptual structure within which research would be conducted.
The function of research design is to provide for the collection of relevant information with minimal expenditure of effort, time and money.
The preparation of research design, appropriate for a particular research problem, involves the consideration of the following :
1. Objectives of the research study.
2. Method of Data Collection to be adopted
3. Source of information—Sample Design
4. Tool for Data collection
5. Data Analysis-- qualitative and quantitative
1. Objectives of the Research Study: Objectives identified to answer the research questions have to be listed making sure that they are:
a) numbered, and
b) statement begins with an action verb.
2. Methods of Data Collection: There are two types of data
Primary Data— collected for the first time
Secondary Data—those which have already been collected and analysed by someone else.
Step 5: COLLECTING DATA :
Having formulated the research problem,, developed a study design, constructed a research instrument and selected a sample, you then collect the data from which you will draw inferences and conclusions for your study. Depending upon your plans, you might commence interviews, mail out a questionnaire, conduct experiments and/or make observations.
Collecting data through any of the methods may involve some ethical issues in relation to the participants and the researcher :
- Those from whom information is collected or those who are studied by a researcher become participants of the study.
- Anyone who collects information for a specific purpose, adhering to the accepted code of conduct, is a researcher.
a) Ethical issues concerning research participants: There are many ethical issues in relation to participants of a research activity.
i) Collecting information:
Your request for information may put pressure or create anxiety on a respondent. Is it ethical?
Research is required to improve conditions. Provided any piece of research is likely to help society directly or indirectly, it is acceptable to ask questions, if you first obtain the respondents’ informed consent.
If you cannot justify the relevance of the research you are conducting, you are wasting your respondents’ time, which is unethical.
ii)Seeking consent:
In every discipline it is considered unethical to collect information without the knowledge of the participant, and their expressed willingness and informed consent.
Informed consent implies that subjects are made adequately aware of the type of information you want from them, why the information is being sought, what purpose it will be put to, how they are expected to participate in the study, and how it will directly or indirectly affect them. It is important that the consent should be voluntary and without pressure of any kind.
iii) Providing incentives:
Most people do not participate in a study because of incentives, but because they realize the importance of the study.
Is it ethical to provide incentives to respondents to share information with you because they are giving their time?
Giving a present before data collection is unethical.
iv)Seeking sensitive information:
Certain types of information can be regarded as sensitive or confidential by some people and thus an invasion to their privacy, asking for such information may upset or embarrass a respondent.
For most people, questions on drug use, pilferage, income, age, marital status etc are intrusive. In collecting data you need to be careful about the sensitivities of your respondents.
It is not unethical to ask such questions provided that you tell your respondents the type of information you are going to ask clearly and frankly, and give them sufficient time to decide if they want to participate, without any major inducement.
v) The possibility of causing harm to participant:
When you collect data from respondents or involve subjects in an experiment, you need to examine carefully whether their involvement is likely to harm them in any way. Harm includes l research that might include hazardous experiments, discomfort, anxiety, harassment, invasion of privacy, or demeaning or dehumanizing procedures.
If it is likely to, you must make sure that the risk is minimal i.e. the extent of harm or discomfort is not greater that ordinarily encountered in daily life. If the way information is sought creates anxiety or harassment, you need to take steps to prevent this.
vi) Maintaining confidentiality:
Sharing information about a respondent with others for purposes other than research is unethical. Sometimes you need to identify your study population to put your findings into context. In such a situation you need to make sure that at least the information provided by respondents is kept anonymous.
It is unethical to identify an individual’s responses. Therefore you need to ensure that after the information has been collected, the source cannot be known.
b) Ethical issues relating to the researcher:
i) Avoiding bias:
Bias on the part of the researcher is unethical. Bias is a deliberate attempt to either to hide what you have found in your study, or highlight something disproportionately to its true existence.
ii) Provision or deprivation of a treatment:
Both the provision and deprivation of a treatment/ intervention may pose an ethical dilemma for you as a researcher. Is it ethical to provide a study population with an intervention/ treatment that has not yet been conclusively proven effective or beneficial? But if you do not test, how can you prove or disprove its effectiveness or benefits?
There are no simple answers to these dilemmas. Ensuring informed consent, ‘minimum risk’ and frank discussion as to the implications of participation in the study will help to resolve ethical issues.
iii) Using inappropriate research methodology:
It is unethical to use a method or procedure you know to be inappropriate e.g. selecting a highly biased sample, using an invalid instrument or drawing wrong conclusions.
iv) Incorrect reporting:
To report the findings in a way that changes or slants them to serve your own or someone else’s interest is unethical.
v) Inappropriate use of the information:
The use of information in a way that directly or indirectly adversely affects the respondents is unethical. If so, the study population needs to be protected.
Sometimes it is possible to harm individuals in the process of achieving benefits for the organizations. An example would be a study to examine the feasibility of restructuring an organization. Restructuring may be beneficial to the organization as a whole bur may be harmful to some individuals.
Should you ask respondents for information that is likely to be used against them?
It is ethical to ask questions provided you tell respondents of the potential use of the information, including the possibility of it being used against some of them, and you let them decide if they want to participate.
Step 6: PROCESSING AND ANALYSING DATA
Processing and analysing data involves a number of closely related operations which are performed with the purpose of summarizing the collected data and organizing these in a manner that they answer the research questions (objectives).
The Data Processing operations are:
1. Editing- a process of examining the collected raw data to detect errors and omissions and to correct these when possible.
2. Classification- a process of arranging data in groups or classes on the basis of common characteristics. Depending on the nature of phenomenon involved
a) Classification according to attributes: here data is analysed on the basis of common characteristics which can either be
: descriptive such as literacy, sex, religion etc. or
: numerical such as weight, height, income etc.
b) Classification according to class –intervals: is done with data relating to income, age, weight, tariff, production, occupancy etc. Such quantitative data are known as the statistics of variables and are classified on the basis of class –intervals.
e.g. persons whose income are within Rs 2001 to Rs 4000 can form one group or class, those with income within Rs 4001 t0 Rs 6000 can form another group or class and so on.
3. Tabulation-Tabulation is the process of summarizing raw data and displaying the same in compact form for further analysis. It is an orderly arrangement of data in columns and rows. Tabulation is essential because:
a) It conserves space and reduces explanatory and descriptive statement to a minimum.
b) It facilitates the process of comparison.
c) It facilitates the summation of items and the detection of errors and omissions.
d) It provides the basis for various statistical computations.
Step8: REPORTING THE FINDINGS:
Writing the report is the last, and for many, the most difficult step of the research process. The report informs the world what you have done, what you have discovered and what conclusions you have drawn from your findings. The report should be written in an academic style. Language should be formal and not journalistic.
What is research measurement? Explain measurement scales.
Measurement is at the core of doing research. Measurement is the assignment of numbers to things. In almost all research, everything has to be reduced to numbers eventually. Precision and exactness in measurement are vitally important. The measures are what are actually used to test the hypotheses. A researcher needs good measures for both independent and dependent variables.
Measurement consists of two basic processes called conceptualization and operationalization, then an advanced process called determining the levels of measurement, and then even more advanced methods of measuring reliability and validity.
Conceptualization is the process of taking a construct or concept and refining it by giving it a conceptual or theoretical definition. Ordinary dictionary definitions will not do. Instead, the researcher takes keywords in their research question or hypothesis and finds a clear and consistent definition that is agreed-upon by others in the scientific community. Sometimes, the researcher pushes the envelope by coming up with a novel conceptual definition, but such initiatives are rare and require the researcher to have intimate familiarity with the topic. More common is the process by which a researcher notes agreements and disagreements over conceptualization in the literature review, and then comes down in favor of someone else's conceptual definition. It's perfectly acceptable in science to borrow the conceptualizations and operationalizations of others. Conceptualization is often guided by the theoretical framework, perspective, or approach the researcher is committed to. For example, a researcher operating from within a Marxist framework would have quite different conceptual definitions for a hypothesis about social class and crime than a non-Marxist researcher. That's because there are strong value positions in different theoretical perspectives about how some things should be measured. Most criminal justice researchers at this point will at least decide what type of crime they're going to study.
Operationalization is the process of taking a conceptual definition and making it more precise by linking it to one or more specific, concrete indicators or operational definitions. These are usually things with numbers in them that reflect empirical or observable reality. For example, if the type of crime one has chosen to study is theft (as representative of crime in general), creating an operational definition for it means at least choosing between petty theft and grand theft (false taking of less or more than $150). I don't want to give the impression from this example that researchers should rely upon statutory or legal definitions. Some researchers do, but most often, operational definitions are also borrowed or created anew. They're what link the world of ideas to the world of everyday reality. It's more important that ordinary people would agree on your indicators than other scientists or legislators, but again, avoid dictionary definitions. If you were to use legalistic definitions, then it's your duty to provide what is called an auxiliary theory, which is a justification for the research utility of legal hair-splitting (as in why less or more than $150 is of theoretical significance). The most important thing to remember at this point, however, is your unit of analysis. You want to make absolutely sure that everything you reduce down is defined at the same unit of analysis: societal, regional, state, communal, individual, to name a few. You don't want to end up with a research project that has to collect political science data, sociological data, and psychological data. In most cases, you should break it all down so that each variable is operationally defined at the same level of thought, attitude, trait, or behavior, although some would call this psychological reductionism and are more comfortable with group-level units or psychological units only as a proxy measure for more abstract, harder-to-measure terms.
LEVELS OF MEASUREMENT
A level of measurement is the precision by which a variable is measured. For 50 years, with few detractors, science has used the Stevens (1951) typology of measurement levels. There are three things to remember about this typology: (1) anything that can be measured falls into one of the four types; (2) the higher the type, the more precision in measurement; and (3) every level up contains all the properties of the previous level. The four levels of measurement, from lowest to highest, are:
* Nominal
* Ordinal
* Interval
* Ratio
The nominal level of measurement describes variables that are categorical in nature. The characteristics of the data you're collecting fall into distinct categories. If there are a limited number of distinct categories (usually only two), then you're dealing with a discrete variable. If there are an unlimited or infinite number of distinct categories, then you're dealing with a continuous variable. Nominal variables include demographic characteristics like sex, race, and religion.
The ordinal level of measurement describes variables that can be ordered or ranked in some order of importance. It describes most judgments about things, such as big or little, strong or weak. Most opinion and attitude scales or indexes in the social sciences are ordinal in nature.
The interval level of measurement describes variables that have more or less equal intervals, or meaningful distances between their ranks. For example, if you were to ask somebody if they were first, second, or third generation immigrant, the assumption is that the distance, or number of years, between each generation is the same. All crime rates in criminal justice are interval level measures, as is any kind of rate.
The ratio level of measurement describes variables that have equal intervals and a fixed zero (or reference) point. It is possible to have zero income, zero education, and no involvement in crime, but rarely do we see ratio level variables in social science since it's almost impossible to have zero attitudes on things, although "not at all", "often", and "twice as often" might qualify as ratio level measurement.
Measurement Scales
Measurement scales are ubiquitous throughout scientific research, especially among the disciplines of social sciences. These are useful to record data and thus apply statistical or other scientific analysis on this data. In fact, all data analysis is broken down into four major measurement scales as described below.
1. NOMINAL
This type of measurement scale is used for mutually exclusive and exhaustive categories. This means that the variable under measurement can take one and only one value out of the given options. In addition, every observation must fall into one of the categories.
Examples:
* In a survey, the variable ‘sex’ is a nominal scale of measurement because there are two possibilities, male and female, which cover the entire population under study.
* In a medical test, a lab animal may be either dead or alive. Every animal under study is in one of the two states and there is no animal that cannot be described by these two states.
* In Quantum mechanics, the measured spin of an electron is either +1/2 or -1/2. The measurement cannot yield any other value and this is true for any electron under study.
The states of a nominal measurement scale can be assigned numerical values, e.g. 1 for female and 0 for male in the first example. These are usually arbitrary values and do not correspond to an inherent numerical value that is universally assignable.
2. ORDINAL
Unlike for a nominal case, here the numerical values associated with the measurement have some relevance in terms of ranking of the system. In a nominal measurement, the values are arbitrary. In our previous example, assigning 1 to female and 0 to male does not in any way mean that a female participant is “more” or “higher” than a male one. However, in an ordinal measurement, there is a ranking involved.
Examples:
* In an Olympic race, the participants are ranked according to the ascending order of the time taken to finish the race. In this, the number tells us something about the relative performance of an athlete.
* The grading system used in university is a measurement scale of the type ordinal because there is a hierarchy involved.
3. INTERVAL
The interval measurement scale tells us some quantitative data about the difference between the measurements. In an ordinal measurement scale, we only get qualitative information about the relative ranking.In our previous example of a race, we only know who was first, second and third, but we know nothing about how close the second was to the first and the second to the third. For this information, we need an interval scale.
Examples:
* In temperature measurement, we use scales that are interval measurement scales. The scales are also uniform in that the difference between 200C and 400C is the same as the difference between 400C and 600C.
* The number system that we use is another example of a uniform measurement scale.
* Not all interval measurement scales are uniform. A log scale is commonly used for plotting data, which is not uniform in nature.
In an interval scale, the ratio of values doesn’t make sense. You cannot say, for example, that 200C is twice as cold as 400C. (it would be absurd for example, to say that 0.0010C is 1000 times colder than 10C). The main reason for this is that the zero scale is chosen arbitrarily.
4. RATIO
The ratio measurement scale is most commonly used in physical sciences and engineering applications. Most physical measurements are ratio scales. Like the name suggests and unlike the interval scale, here the ratio between two values makes perfect sense. The zero scale is not chosen arbitrarily in this case.
For example, when we say that the mass of a body is 2kg, it means that it is twice as heavy as a 1kg object that is defined in some scientific way.
The ratio measurement reflects our physical world and is thus very common in science and engineering. On the other hand, it is very rare in social sciences and surveys.
Explain rating and ranking scales.
What are the types of variables? Explain with examples.
Answer:
A variable is something that can be changed, such as a characteristic or value. Variables are generally used in psychology experiments to determine if changes to one thing result in changes to another.
The Dependent and Independent Variables
In a psychology experiment:
• The independent variable is the variable that is controlled and manipulated by the experimenter. For example, in an experiment on the impact of sleep deprivation on test performance, sleep deprivation would be the independent variable.
• The dependent variable is the variable that is measured by the experimenter. In our previous example, the scores on the test performance measure would be the dependent variable.
Extraneous and Confounding Variables
The independent and dependent variables are not the only variables present in many experiments. In some cases, extraneous variables may also play a role. This type of variable is one that may have an impact on the relationship between the independent and dependent variables.
For example, in our previous description of an experiment on the effects of sleep deprivation on test performance, other factors such as age, gender and academic background may have an impact on the results. In such cases, the experimenter will note the values of these extraneous variables so this impact on the results can be controlled for.
There are two basic types of extraneous variables:
• Participant Variables: These extraneous variables are related to individual characteristics of each participant that may impact how he or she responds. These factors can include background differences, mood, anxiety, intelligence, awareness and other characteristics that are unique to each person.
• Situational Variables: These extraneous variables are related to things in the environment that may impact how each participant responds. For example, if a participant is taking a test in a chilly room, the temperature would be considered an extraneous variable. Some participants may not be affected by the cold, but others might be distracted or annoyed by the temperature of the room.
In many cases, extraneous variables are controlled for by the experimenter. In the case of participant variables, the experiment might select participants that are the same in background and temperament to ensure that these factors do not interfere with the results. If, however, a variable cannot be controlled for, it becomes what is known as a confounding variable. This type of variable can have an impact on the dependent variable, which can make it difficult to determine if the results are due to the influence of the independent variable, the confounding variable or an interaction of the two.
Operationally Defining a Variable
Before conducting a psychology experiment, it is essential to create firm operational definitions for both the independent variable and dependent variable. An operational definition describes how the variables are measured and defined within the study.
For example, in our imaginary experiment on the effects of sleep deprivation on test performance, we would need to create very specific operational definitions for our two variables. If our hypothesis is “Students who are sleep deprived will score significantly lower on a test,” then we would have a few different concepts to define. First, what do we mean by students? In our example, let’s define students as participants enrolled in a introductory university-level psychology course.
Next, we need to operationally define the sleep deprivation variable. In our example, let’s say that sleep deprivation refers to those participants who have had less than five hours of sleep the night before the test. Finally, we need to create an operational definition for the test variable. For this example, the test variable will be defined as a student’s score on a chapter exam in the introductory psychology course.
Students often report problems with identifying the independent and dependent variables in an experiment. While the task can become more difficult as the complexity of an experiment increases, there are a few questions you can ask when trying to identify a variable. What is the experimenter manipulating? The things that change, either naturally or through direct manipulation from the experimenter, are generally the independent variables. What is being measured? The dependent variable is the one that the experimenter is measuring.
RESEARCH VARIABLES
A variable is a concept that can assume any one of a range of values. Factor and feature are common English synonyms for variable in the following sentences (outcome is another word that often means variable, but note that not all variables are outcomes):
1. The parents in the school system are aware that AIDS is a factor (variable) that is a serious health risk for their children.
2. The teacher needs to know what features (variables) of his method of discipline might promote negative self-concepts among his students.
3. The teacher needs to know whether his method of discipline might promote negative outcomes (variables) among his students.
A variable varies in the sense that it can take on different values or conditions. It is a characteristic that can be the focus of a research study.
The definition of the term variable makes the concept seem more difficult than it really is. This is because it is a definition of an abstraction; and definitions of abstractions sometimes become -very abstract (for example, it's easy to exist, but it's difficult to define existence). Therefore, it may be useful to look at some examples.
• In the set of examples we started examining on page 18, corporal punishment is a variable. More specifically, whether corporal punishment is permitted is a variable. It can vary by either being permitted or not being permitted.
• The frequency of corporal punishment could also be a variable. It can vary from, say, not happening at all to happening once a day to happening several times a day.
• If a researcher wondered whether corporal punishment might cause more anxiety than suspension from school, then corporal punishment, anxiety, and suspension from school could all become variables for this researcher to study.
• If the researcher focused attention on any other factor, such as child abuse, self-discipline, religious beliefs, gender of the students or teachers, or ethnic background, these could become variables in a research study, or even merely in a thought process.
Variables are developed theoretically and abstractly in the mind of the researcher. The researcher draws on previous experience and theoretical knowledge to identify and specify variables, which serve as unifying factors to help organize the research process. All human thinking employs concepts, which are called variables when we focus attention on them, measure them, or deal with them in some other way as part of the research process.
Discuss guidelines for selection of variables and Hypotheses
THE IMPORTANCE OF STUDYING RELATIONSHIPS
- Identifying relationships among variables enhances understanding where we may learn what happened, or where or when (and even how) something happened, not only why it happened.
- Understanding of relationships helps us to explain the nature of the world in which we live and how parts of it are relate by detect connection between them.
VARIABLES
What is a Variable?
- A variable is any characteristic or quality that varies within a class of objects. The individual members in the class of objects must differ or vary to qualify the class as variable.
- A constant is any characteristic or quality that is identical within a class of objects. Individual members in the class are held constant and not allowed to vary.
Quantitative versus Categorical Variables
- A quantitative variable is a variable that varies in amount or degree (rather than all or none) along continuum from less to more, but not in kind. Two obvious examples are height and weight. Quantitative variable can often (but not always) be subdivided into smaller unit, for example: length. Besides it, we can assign numbers to different individuals or objects to indicate how much of the variable they posses, for example: variable “interest” of students toward a subject.
- A categorical variable is a variable that varies only in kind (qualitatively different), not in degree, amount or quantity. Examples: Eye color, gender, religious preference, occupation, position on a baseball team, political party, teaching method, and most kinds of research “treatments” or “methods.”
- Researchers in education often study the relationship between (or among) either (1) two (or more) qualitative variables; (2) one categorical and one quantitative; or (3) two or more categorical variable.
Independent versus Dependent Variables
- An independent variable is a variable presumed to affect or influence one or more other variables. Independent variable may be either manipulated or selected. A manipulated variable (experimental or treatment variable) is one that the researcher creates which typically found in experimental studies. In case researchers select an independent variable that already exist, they must locate and select examples of it, rather than creating it.
- A dependent (or outcome) variable is a variable presumed to be affected by one or more independent variables.
Moderator Variables
A moderator variable is a secondary independent variable that has been selected for study in order to determine if it affects or modifies the basic relationship between the primary independent variable and the dependent variable. The inclusion of this variable in a study (whenever appropriate) can provide considerably more information than just studying a single independent variable.
Extraneous Variables
An extraneous variable is an independent variable that may have unintended effects on a dependent variable in a particular study. There are many possible extraneous variable which their effect need to be controlled or somehow to be eliminated or minimized by holding them constant (doesn’t vary).
HYPOTHESES
What is a Hypothesis?
- A hypothesis is a prediction of the possible outcomes of study which made before a study commences.
- Many different hypotheses can come from a single research question.
Advantages of Stating Hypotheses in addition to Research Questions
- Hypothesis forces us to think more deeply and specifically about the possible outcome of a study. Elaborating on a question by formulating hypothesis can lead to a more sophisticated understanding of what the question implies and exactly what variables are involved.
- It can involve a philosophy of science. This enables to make specific predictions based on prior evidence or theoretical argument.
- It helps us see if we are, or are not, investigating a relationship.
Disadvantages of Stating Hypotheses
- It may lead to a bias, either conscious or unconscious, on the part of the researcher. This is because by stating hypotheses investigators may be tempted to arrange the procedures or manipulate the data in such a way as to bring about a desired outcome.
- It may sometimes be unnecessary, or even inappropriate, in research project of certain types, such as descriptive surveys and ethnographic studies.
- It may prevent researchers from noticing other phenomena that might be important to study.
Significant Hypotheses
A significant hypothesis is one that is likely to lead, if it is supported, to a greater amount of important knowledge than a nonsignificant hypothesis. In addition, a significant hypothesis give information thar will be more use for people interested in the research question.
Directional versus Nondirectional Hypotheses
- A directional hypothesis is a prediction about the specific nature of a relationship (such as higher, lower, more or less), for example: method A is more effective than method B.
- A nondirectional hypothesis is a prediction that a relationship exists without specifying its exact nature, for example: there will be a difference between method A and method B (without saying which will be more effective). This is usually made when researchers suspect that a relationship exists but has no basic for predicting the direction of the relationship.
IDENTIFYNG VARIABLES
The following working definitions of variable can be outlined;
An entity that varies from one observation to the next, an empirical property thatis capable of taking two or more values
A property that takes on different values(Kumar 2005:56)Thus from the above definitions it can be deduced that a variable is a measurable factor that can assume more than one value, hence age,income,gender,pay,job satisfactioncan be classified as values.
HYPOTHESES FORMULATION
Hypotheses, though important, are not essential for a study. A perfectly valid study can be conducted without constructing a single hypothesis. There are however many definitions of a hypothesis but for the purpose of this assignment the following definitions can be given;
a conjectural statement of the relationship between two or more variables
a tentative assumption or preliminary statement about the relationship between two or more things that needs to be examined
tentative statement about something, the validity of which is usually unknown(Black and Champion 1976:126).
a proposition that is stated in a testable form and that predicts a particular relationship between two (or more) variables. ln other words, if we think that a relationship exists, we first state it as a hypothesis and then test the hypothesis in the field (Bailey 1978:35).Thus from the given definitions it can be deduced that a hypothesis is a premade statement of the results of an investigation indicating the relationship between two or more variables that awaits verification
What is hypothesis? Explain in detail different types of Hypothesis and testing of Hypothesis.
HYPOTHESES
Definition:
Definition: A hypothesis is a tentative statement about the relationship between two or more variables. A hypothesis is a specific, testable prediction about what you expect to happen in your study. For example, a study designed to look at the relationship between sleep deprivation and test performance might have a hypothesis that states, "This study is designed to assess the hypothesis that sleep deprived people will perform worse on a test than individuals who are not sleep deprived."
Unless you are creating a study that is exploratory in nature, your hypothesis should always explain what you expect to happen during the course of your experiment or research.
Remember, a hypothesis does not have to be right. While the hypothesis predicts what the researchers expect to see, the goal of research is to determine whether this guess is right or wrong. When conducting an experiment, researchers might explore a number of different factors to determine which ones might contribute to the ultimate outcome.
In many cases, researchers may find that the results of an experiment do not support the original hypothesis. When writing up these results, the researchers might suggest other options that should be explored in future studies.
Elements of a Good Hypothesis
When trying to come up with a good hypothesis for your own psychology research or experiments, ask yourself the following questions:
• Is you hypothesis based on your research of a topic?
• Can your hypothesis be tested?
• Does you hypothesis include independent and dependent variables?
Before you come up with a specific hypothesis, spend some time doing background research on your topic. Once you have completed a literature review, start thinking of potential questions you still have. Pay attention to the discussion section in the journal articles you read. Many authors will suggest questions that still need to be explored.
What is a Hypothesis?
- A hypothesis is a prediction of the possible outcomes of study which made before a study commences.
- Many different hypotheses can come from a single research question.
Advantages of Stating Hypotheses in addition to Research Questions
- Hypothesis forces us to think more deeply and specifically about the possible outcome of a study. Elaborating on a question by formulating hypothesis can lead to a more sophisticated understanding of what the question implies and exactly what variables are involved.
- It can involve a philosophy of science. This enables to make specific predictions based on prior evidence or theoretical argument.
- It helps us see if we are, or are not, investigating a relationship.
Disadvantages of Stating Hypotheses
- It may lead to a bias, either conscious or unconscious, on the part of the researcher. This is because by stating hypotheses investigators may be tempted to arrange the procedures or manipulate the data in such a way as to bring about a desired outcome.
- It may sometimes be unnecessary, or even inappropriate, in research project of certain types, such as descriptive surveys and ethnographic studies.
- It may prevent researchers from noticing other phenomena that might be important to study.
Significant Hypotheses
A significant hypothesis is one that is likely to lead, if it is supported, to a greater amount of important knowledge than a nonsignificant hypothesis. In addition, a significant hypothesis give information thar will be more use for people interested in the research question.
Directional versus Nondirectional Hypotheses
- A directional hypothesis is a prediction about the specific nature of a relationship (such as higher, lower, more or less), for example: method A is more effective than method B.
- A nondirectional hypothesis is a prediction that a relationship exists without specifying its exact nature, for example: there will be a difference between method A and method B (without saying which will be more effective). This is usually made when researchers suspect that a relationship exists but has no basic for predicting the direction of the relationship.
What is Hypothesis Testing?
A statistical hypothesis is an assumption about a population parameter. This assumption may or may not be true. Hypothesis testing refers to the formal procedures used by statisticians to accept or reject statistical hypotheses.
Statistical Hypotheses
The best way to determine whether a statistical hypothesis is true would be to examine the entire population. Since that is often impractical, researchers typically examine a random sample from the population. If sample data are not consistent with the statistical hypothesis, the hypothesis is rejected.
There are two types of statistical hypotheses.
• Null hypothesis. The null hypothesis, denoted by H0, is usually the hypothesis that sample observations result purely from chance.
• Alternative hypothesis. The alternative hypothesis, denoted by H1 or Ha, is the hypothesis that sample observations are influenced by some non-random cause.
For example, suppose we wanted to determine whether a coin was fair and balanced. A null hypothesis might be that half the flips would result in Heads and half, in Tails. The alternative hypothesis might be that the number of Heads and Tails would be very different. Symbolically, these hypotheses would be expressed as
H0: P = 0.5
Ha: P ≠ 0.5
Suppose we flipped the coin 50 times, resulting in 40 Heads and 10 Tails. Given this result, we would be inclined to reject the null hypothesis. We would conclude, based on the evidence, that the coin was probably not fair and balanced.
Can We Accept the Null Hypothesis?
Some researchers say that a hypothesis test can have one of two outcomes: you accept the null hypothesis or you reject the null hypothesis. Many statisticians, however, take issue with the notion of "accepting the null hypothesis." Instead, they say: you reject the null hypothesis or you fail to reject the null hypothesis.
Why the distinction between "acceptance" and "failure to reject?" Acceptance implies that the null hypothesis is true. Failure to reject implies that the data are not sufficiently persuasive for us to prefer the alternative hypothesis over the null hypothesis.
Hypothesis Tests
Statisticians follow a formal process to determine whether to reject a null hypothesis, based on sample data. This process, called hypothesis testing, consists of four steps.
• State the hypotheses. This involves stating the null and alternative hypotheses. The hypotheses are stated in such a way that they are mutually exclusive. That is, if one is true, the other must be false.
• Formulate an analysis plan. The analysis plan describes how to use sample data to evaluate the null hypothesis. The evaluation often focuses around a single test statistic.
• Analyze sample data. Find the value of the test statistic (mean score, proportion, t-score, z-score, etc.) described in the analysis plan.
• Interpret results. Apply the decision rule described in the analysis plan. If the value of the test statistic is unlikely, based on the null hypothesis, reject the null hypothesis.
Decision Errors
Two types of errors can result from a hypothesis test.
• Type I error. A Type I error occurs when the researcher rejects a null hypothesis when it is true. The probability of committing a Type I error is called the significance level. This probability is also called alpha, and is often denoted by α.
• Type II error. A Type II error occurs when the researcher fails to reject a null hypothesis that is false. The probability of committing a Type II error is called Beta, and is often denoted by β. The probability of not committing a Type II error is called the Power of the test.
Decision Rules
The analysis plan includes decision rules for rejecting the null hypothesis. In practice, statisticians describe these decision rules in two ways - with reference to a P-value or with reference to a region of acceptance.
• P-value. The strength of evidence in support of a null hypothesis is measured by the P-value. Suppose the test statistic is equal to S. The P-value is the probability of observing a test statistic as extreme as S, assuming the null hypotheis is true. If the P-value is less than the significance level, we reject the null hypothesis.
• Region of acceptance. The region of acceptance is a range of values. If the test statistic falls within the region of acceptance, the null hypothesis is not rejected. The region of acceptance is defined so that the chance of making a Type I error is equal to the significance level.
The set of values outside the region of acceptance is called the region of rejection. If the test statistic falls within the region of rejection, the null hypothesis is rejected. In such cases, we say that the hypothesis has been rejected at the α level of significance.
These approaches are equivalent. Some statistics texts use the P-value approach; others use the region of acceptance approach. In subsequent lessons, this tutorial will present examples that illustrate each approach.
One-Tailed and Two-Tailed Tests
A test of a statistical hypothesis, where the region of rejection is on only one side of the sampling distribution, is called a one-tailed test. For example, suppose the null hypothesis states that the mean is less than or equal to 10. The alternative hypothesis would be that the mean is greater than 10. The region of rejection would consist of a range of numbers located located on the right side of sampling distribution; that is, a set of numbers greater than 10.
A test of a statistical hypothesis, where the region of rejection is on both sides of the sampling distribution, is called a two-tailed test. For example, suppose the null hypothesis states that the mean is equal to 10. The alternative hypothesis would be that the mean is less than 10 or greater than 10. The region of rejection would consist of a range of numbers located located on both sides of sampling distribution; that is, the region of rejection would consist partly of numbers that were less than 10 and partly of numbers that were greater than 10.
What is data? What are types of data? How are data collected?
Data can be defined as the quantitative or qualitative values of a variable. Data is plural of Datum which literally means to give or something given. Data is thought to be the lowest unit of information from which other measurements and analysis can be done. Data can be numbers, images, words, figures, facts or ideas. Data in itself cannot be understood and to to get information from the data one must interpret it into meaningful information. There are various methods of interpreting data. Data sources are broadly classified into primary and secondary data.
Importance of Data and Data Collection:
Data is one of the most important and vital aspect of any research studies. Researches conducted in different fields of study can be different in methodology but every research is based on data which is analyzed and interpreted to get information.
Data is the basic unit in statistical studies. Statistical information like census, population variables, health statistics, and road accidents records are all developed from data.
Data is important in computer science. Numbers, images and figures in computer are all data.
Types of Data
Primary Data:
Data that has been collected from first-hand-experience is known as primary data. Primary data has not been published yet and is more reliable, authentic and objective. Primary data has not been changed or altered by human beings, therefore its validity is greater than secondary data.
Importance of Primary Data:
Importance of Primary data cannot be neglected. A research can be conducted without secondary data but a research based on only secondary data is least reliable and may have biases because secondary data has already been manipulated by human beings. In statistical surveys it is necessary to get information from primary sources and work on primary data: for example, the statistical records of female population in a country cannot be based on newspaper, magazine and other printed sources. One such sources are old and secondly they contain limited information as well as they can be misleading and biased.
Validity: Validity is one of the major concerns in a research. Validity is the quality of a research that makes it trustworthy and scientific. Validity is the use of scientific methods in research to make it logical and acceptable. Using primary data in research can improves the validity of research. First hand information obtained from a sample that is representative of the target population will yield data that will be valid for the entire target population.
Authenticity: Authenticity is the genuineness of the research. Authenticity can be at stake if the researcher invests personal biases or uses misleading information int he research. Primary research tools and data can become more authentic if the methods chosen to analyze and interpret data are valid and reasonably suitable for the data type. . Primary sources are more authentic because the facts have not been overdone. Primary source can be less authentic if the source hides information or alters facts due to some personal reasons. Their are methods that can be employed to ensure factual yielding of data from the source.
Reliability: Reliability is the certainty that the research is enough true to be trusted on. For example, if a research study concludes that junk food consumption does not increase the risk of cancer and heart diseases. This conclusion should have to be drawn from a sample whose size, sampling technique and variability is not questionable. Reliability improves with using primary data. In the similar research mentioned above if the researcher uses experimental method and questionnaires the results will be highly reliable. On the other hand, if he relies on the data available in books and on internet he will collect information that does not represent the real facts.
Sources of Primary Data:
Sources for primary data are limited and at times it becomes difficult to obtain data from primary source because of either scarcity of population or lack of cooperation. Regardless of any difficulty one can face in collecting primary data; it is the most authentic and reliable data source. Following are some of the sources of primary data.
Experiments: Experiments require an artificial or natural setting in which to perform logical study to collect data. Experiments are more suitable for medicine, psychological studies, nutrition and for other scientific studies. In experiments the experimenter has to keep control over the influence of any extraneous variable on the results.
Survey: Survey is most commonly used method in social sciences, management, marketing and psychology to some extent. Surveys can be conducted in different methods.
• Questionnaire: is the most commonly used method in survey. Questionnaires are a list of questions either open-ended or close -ended for which the respondent give answers. Questionnaire can be conducted via telephone, mail, live in a public area, or in an institute, through electronic mail or through fax and other methods.
• Interview: Interview is a face-to-face conversation with the respondent. In interview the main problem arises when the respondent deliberately hides information otherwise it is an in depth source of information. The interviewer can not only record the statements the interviewee speaks but he can observe the body language, expressions and other reactions to the questions too. This enables the interviewer to draw conclusions easily.
• Observations: Observation can be done while letting the observing person know that he is being observed or without letting him know. Observations can also be made in natural settings as well as in artificially created environment.
Secondary Data:
Data collected from a source that has already been published in any form is called as secondary data. The review of literature in nay research is based on secondary data. MNostly from books, journals and periodicals.
Importance of Secondary Data:
Secondary data can be less valid but its importance is still there. Sometimes it is difficult to obtain primary data; in these cases getting information from secondary sources is easier and possible. Sometimes primary data does not exist in such situation one has to confine the research on secondary data. Sometimes primary data is present but the respondents are not willing to reveal it in such case too secondary data can suffice: for example, if the research is on the psychology of transsexuals first it is difficult to find out transsexuals and second they may not be willing to give information you want for your research, so you can collect data from books or other published sources.
Sources of Secondary Data:
Secondary data is often readily available. After the expense of electronic media and internet the availability of secondary data has become much easier.
Published Printed Sources: There are variety of published printed sources. Their credibility depends on many factors. For example, on the writer, publishing company and time and date when published. New sources are preferred and old sources should be avoided as new technology and researches bring new facts into light.
• Books: Books are available today on any topic that you want to research. The use of books start before even you have selected the topic. After selection of topics books provide insight on how much work has already been done on the same topic and you can prepare your literature review. Books are secondary source but most authentic one in secondary sources.
• Journals/periodicals: Journals and periodicals are becoming more important as far as data collection is concerned. The reason is that journals provide up-to-date information which at times books cannot and secondly, journals can give information on the very specific topic on which you are researching rather talking about more general topics.
• Magazines/Newspapers: Magazines are also effective but not very reliable. Newspaper on the other hand are more reliable and in some cases the information can only be obtained from newspapers as in the case of some political studies.
Published Electronic Sources: As internet is becoming more advance, fast and reachable to the masses; it has been seen that much information that is not available in printed form is available on internet. In the past the credibility of internet was questionable but today it is not. The reason is that in the past journals and books were seldom published on internet but today almost every journal and book is available online. Some are free and for others you have to pay the price.
• e-journals: e-journals are more commonly available than printed journals. Latest journals are difficult to retrieve without subscription but if your university has an e-library you can view any journal, print it and those that are not available you can make an order for them.
• General websites; Generally websites do not contain very reliable information so their content should be checked for the reliability before quoting from them.
• Weblogs: Weblogs are also becoming common. They are actually diaries written by different people. These diaries are as reliable to use as personal written diaries.
Unpublished Personal Records: Some unpublished data may also be useful in some cases.
• Diaries: Diaries are personal records and are rarely available but if you are conducting a descriptive research then they might be very useful. The Anne Franks diary is the most famous example of this. That diary contained the most accurate records of Nazi wars.
• Letters: Letters like diaries are also a rich source but should be checked for their reliability before using them.
Governement Records: Government records are very important for marketing, management, humanities and social science research.
• Census Data/population statistics:
• Health records
• Educational institutes records
Public Sector Records:
• NGOs's survey data
• Other private companies records
The data collection process can be relatively simple depending on the type of data collection tools required and used during the research. Data collection tools are instruments used to collect information for performance assessments, self-evaluations, and external evaluations. The data collection tools need to be strong enough to support what the evaluations find during research. Here are a few examples of data collection tools used within three main categories.
Secondary Participation
Data collection tools involving secondary participation require no direct contact to gather information. Examples of secondary data collection tools would include:
• Postal mail
• Electronic mail
• Telephone
• Web-based surveys
These data collection tools not only allow for a true measurement of accuracy but also let the researcher obtain any unspoken observations about the participants while conducting research.
Case Studies And Content Analysis
Case studies and content analysis are data collection tools which are based upon pre-existing research or a search of recorded information which may be useful to the researcher in gaining the required information which fills in the blanks not found with the other two types during the datacollection process. Some examples of this type of data collection tool would include:
• Expert opinions – leaders in the field of study
• Case studies – previous findings of other researchers
• Literature searches – research articles and papers
• Content analysis of both internal and external records – documents created from internal origin or other documents citing occurrences within the research group
These three data collection tools are the primary sources for gaining information during research. The most effective being the In-Person Observations with the use of CaseStudies and analysis for verification resources. While each type of data collection tool can be used alone, most often they are used in either combination or conjunction with each other in various ways.
Step 5: COLLECTING DATA :
Having formulated the research problem,, developed a study design, constructed a research instrument and selected a sample, you then collect the data from which you will draw inferences and conclusions for your study. Depending upon your plans, you might commence interviews, mail out a questionnaire, conduct experiments and/or make observations.
Collecting data through any of the methods may involve some ethical issues in relation to the participants and the researcher :
- Those from whom information is collected or those who are studied by a researcher become participants of the study.
- Anyone who collects information for a specific purpose, adhering to the accepted code of conduct, is a researcher.
a) Ethical issues concerning research participants: There are many ethical issues in relation to participants of a research activity.
i) Collecting information:
Your request for information may put pressure or create anxiety on a respondent. Is it ethical?
Research is required to improve conditions. Provided any piece of research is likely to help society directly or indirectly, it is acceptable to ask questions, if you first obtain the respondents’ informed consent.
If you cannot justify the relevance of the research you are conducting, you are wasting your respondents’ time, which is unethical.
ii)Seeking consent:
In every discipline it is considered unethical to collect information without the knowledge of the participant, and their expressed willingness and informed consent.
Informed consent implies that subjects are made adequately aware of the type of information you want from them, why the information is being sought, what purpose it will be put to, how they are expected to participate in the study, and how it will directly or indirectly affect them. It is important that the consent should be voluntary and without pressure of any kind.
iii) Providing incentives:
Most people do not participate in a study because of incentives, but because they realize the importance of the study.
Is it ethical to provide incentives to respondents to share information with you because they are giving their time?
Giving a present before data collection is unethical.
iv)Seeking sensitive information:
Certain types of information can be regarded as sensitive or confidential by some people and thus an invasion to their privacy, asking for such information may upset or embarrass a respondent.
For most people, questions on drug use, pilferage, income, age, marital status etc are intrusive. In collecting data you need to be careful about the sensitivities of your respondents.
It is not unethical to ask such questions provided that you tell your respondents the type of information you are going to ask clearly and frankly, and give them sufficient time to decide if they want to participate, without any major inducement.
v) The possibility of causing harm to participant:
When you collect data from respondents or involve subjects in an experiment, you need to examine carefully whether their involvement is likely to harm them in any way. Harm includes l research that might include hazardous experiments, discomfort, anxiety, harassment, invasion of privacy, or demeaning or dehumanizing procedures.
If it is likely to, you must make sure that the risk is minimal i.e. the extent of harm or discomfort is not greater that ordinarily encountered in daily life. If the way information is sought creates anxiety or harassment, you need to take steps to prevent this.
vi) Maintaining confidentiality:
Sharing information about a respondent with others for purposes other than research is unethical. Sometimes you need to identify your study population to put your findings into context. In such a situation you need to make sure that at least the information provided by respondents is kept anonymous.
It is unethical to identify an individual’s responses. Therefore you need to ensure that after the information has been collected, the source cannot be known.
b) Ethical issues relating to the researcher:
i) Avoiding bias:
Bias on the part of the researcher is unethical. Bias is a deliberate attempt to either to hide what you have found in your study, or highlight something disproportionately to its true existence.
ii) Provision or deprivation of a treatment:
Both the provision and deprivation of a treatment/ intervention may pose an ethical dilemma for you as a researcher. Is it ethical to provide a study population with an intervention/ treatment that has not yet been conclusively proven effective or beneficial? But if you do not test, how can you prove or disprove its effectiveness or benefits?
There are no simple answers to these dilemmas. Ensuring informed consent, ‘minimum risk’ and frank discussion as to the implications of participation in the study will help to resolve ethical issues.
iii) Using inappropriate research methodology:
It is unethical to use a method or procedure you know to be inappropriate e.g. selecting a highly biased sample, using an invalid instrument or drawing wrong conclusions.
iv) Incorrect reporting:
To report the findings in a way that changes or slants them to serve your own or someone else’s interest, is unethical.
v) Inappropriate use of the information:
The use of information in a way that directly or indirectly adversely affects the respondents is unethical. If so, the study population needs to be protected.
Sometimes it is possible to harm individuals in the process of achieving benefits for the organizations. An example would be a study to examine the feasibility of restructuring an organization. Restructuring may be beneficial to the organization as a whole bur may be harmful to some individuals.
Should you ask respondents for information that is likely to be used against them?
It is ethical to ask questions provided you tell respondents of the potential use of the information, including the possibility of it being used against some of them, and you let them decide if they want to participate.
What is internal and external validity? What are the factors affecting validity? How the research design is selected depending upon the choice of the type of validity?
Internal validity/Experimental validity is the validity of (causal) inferences in scientific studies, usually based on experiments as experimental validity.[1]
Inferences are said to possess internal validity if a causal relation between two variables is properly demonstrated.[2][3] A causal inference may be based on a relation when three criteria are satisfied:
1. the "cause" precedes the "effect" in time (temporal precedence),
2. the "cause" and the "effect" are related (covariation), and
3. there are no plausible alternative explanations for the observed covariation (nonspuriousness).[3]
In scientific experimental settings, researchers often manipulate a variable (the independent variable) to see what effect it has on a second variable (the dependent variable)[4] For example, a researcher might, for different experimental groups, manipulate the dosage of a particular drug between groups to see what effect it has on health. In this example, the researcher wants to make a causal inference, namely, that different doses of the drug may be held responsible for observed changes or differences. When the researcher may confidently attribute the observed changes or differences in the dependent variable to the independent variable, and when he can rule out other explanations (or rival hypotheses), then his causal inference is said to be internally valid.[5]
In many cases, however, the magnitude of effects found in the dependent variable may not just depend on
* variations in the independent variable,
* the power of the instruments and statistical procedures used to measure and detect the effects, and
* the choice of statistical methods (see: Statistical conclusion validity).
Rather, a number of variables or circumstances uncontrolled for (or uncontrollable) may lead to additional or alternative explanations (a) for the effects found and/or (b) for the magnitude of the effects found. Internal validity, therefore, is more a matter of degree than of either-or, and that is exactly why research designs other than true experiments may also yield results with a high degree of internal validity.
In order to allow for inferences with a high degree of internal validity, precautions may be taken during the design of the scientific study. As a rule of thumb, conclusions based on correlations or associations may only allow for lesser degrees of internal validity than conclusions drawn on the basis of direct manipulation of the independent variable. And, when viewed only from the perspective of Internal Validity, highly controlled true experimental designs (i.e. with random selection, random assignment to either the control or experimental groups, reliable instruments, reliable manipulation processes, and safeguards against confounding factors) may be the "gold standard" of scientific research. By contrast, however, the very strategies employed to control these factors may also limit the generalizability or External Validity of the findings.
[edit] Threats to internal validity
[edit] Ambiguous Temporal Precedence
Lack of clarity about which variable occurred first may yield confusion about which variable is the cause and which is the effect.
[edit] Confounding
A major threat to the validity of causal inferences is Confounding: Changes in the dependent variable may rather be attributed to the existence or variations in the degree of a third variable which is related to the manipulated variable. Where Spurious relationships cannot be ruled out, rival hypothesis to the original causal inference hypothesis of the researcher may be developed.
[edit] Selection Bias
Selection bias refers to the problem that, at pre-test, differences between groups exist that may interact with the independent variable and thus be 'responsible' for the observed outcome. Researchers and participants bring to the experiment a myriad of characteristics, some learned and others inherent. For example, sex, weight, hair, eye, and skin color, personality, mental capabilities, and physical abilities, but also attitudes like motivation or willingness to participate.
During the selection step of the research study, if an unequal number of test subjects have similar subject-related variables there is a threat to the internal validity. For example, a researcher created two test groups, the experimental and the control groups. The subjects in both groups are not alike with regard to the independent variable but similar in one or more of the subject-related variables.
[edit] History
Events outside of the study/experiment or between repeated measures of the dependent variable may affect participants' responses to experimental procedures. Often, these are large scale events (natural disaster, political change, etc.) that affect participants' attitudes and behaviors such that it becomes impossible to determine whether any change on the dependent measures is due to the independent variable, or the historical event.
[edit] Maturation
Subjects change during the course of the experiment or even between measurements. For example, young children might mature and their ability to concentrate may change as they grow up. Both permanent changes, such as physical growth and temporary ones like fatigue, provide "natural" alternative explanations; thus, they may change the way a subject would react to the independent variable. So upon completion of the study, the researcher may not be able to determine if the cause of the discrepancy is due to time or the independent variable.
[edit] Repeated testing (also referred to as Testing Effects)
Repeatedly measuring the participants may lead to bias. Participants may remember the correct answers or may be conditioned to know that they are being tested. Repeatedly taking (the same or similar) intelligence tests usually leads to score gains, but instead of concluding that the underlying skills have changed for good, this threat to Internal Validity provides good rival hypotheses.
[edit] Instrument change (Instrumentality)
The instrument used during the testing process can change the experiment. This also refers to observers being more concentrated or primed, or having unconsciously changed the criteria they use to make judgments. This can also be an issue with self-report measures given at different times. In this case the impact may be mitigated through the use of retrospective pretesting. If any instrumentation changes occur, the internal validity of the main conclusion is affected, as alternative explanations are readily available.
[edit] Regression toward the mean
This type of error occurs when subjects are selected on the basis of extreme scores (one far away from the mean) during a test. For example, when children with the worst reading scores are selected to participate in a reading course, improvements at the end of the course might be due to regression toward the mean and not the course's effectiveness. If the children had been tested again before the course started, they would likely have obtained better scores anyway. Likewise, extreme outliers on individual scores are more likely to be captured in one instance of testing but will likely evolve into a more normal distribution with repeated testing.
[edit] Mortality/differential attrition
This error occurs if inferences are made on the basis of only those participants that have participated from the start to the end. However, participants may have dropped out of the study before completion, and maybe even due to the study or programme or experiment itself. For example, the percentage of group members having quit smoking at post-test was found much higher in a group having received a quit-smoking training program than in the control group. However, in the experimental group only 60% have completed the program. If this attrition is systematically related to any feature of the study, the administration of the independent variable, the instrumentation, or if dropping out leads to relevant bias between groups, a whole class of alternative explanations is possible that account for the observed differences.
[edit] Selection-maturation interaction
This occurs when the subject-related variables, color of hair, skin color, etc., and the time-related variables, age, physical size, etc., interact. If a discrepancy between the two groups occurs between the testing, the discrepancy may be due to the age differences in the age categories.
[edit] Diffusion
If treatment effects spread from treatment groups to control groups, a lack of differences between experimental and control groups may be observed. This does not mean, however, that the independent variable has no effect or that there is no relationship between dependent and independent variable.
[edit] Compensatory rivalry/resentful demoralization
Behaviour in the control groups may alter as a result of the study. For example, control group members may work extra hard to see that expected superiority of the experimental group is not demonstrated. Again, this does not mean that the independent variable produced no effect or that there is no relationship between dependent and independent variable. Vice-versa, changes in the dependent variable may only be effected due to a demoralized control group, working less hard or motivated, not due to the independent variable.
[edit] Experimenter bias
Experimenter bias occurs when the individuals who are conducting an experiment inadvertently affect the outcome by non-consciously behaving differently to members of control and experimental groups. It is possible to eliminate the possibility of experimenter bias through the use of double blind study designs, in which the experimenter is not aware of the condition to which a participant belongs.
For eight of these threats there exists the first letter mnemonic THIS MESS, which refers to the first letters of Testing (repeated testing), History, Instrument change, Statistical Regression toward the mean, Maturation, Experimental mortality, Selection and Selection Interaction[6].
External validity is the validity of generalized (causal) inferences in scientific studies, usually based on experiments as experimental validity.[1]
Inferences about cause-effect relationships based on a specific scientific study are said to possess external validity if they may be generalized from the unique and idiosyncratic settings, procedures and participants to other populations and conditions.[2][3] Causal inferences said to possess high degrees of external validity can reasonably be expected to apply (a) to the target population of the study (i.e. from which the sample was drawn) (also referred to as population validity), and (b) to the universe of other populations (e.g. across time and space).
The most common loss of external validity comes from the fact that experiments using human participants often employ small samples obtained from a single geographic location or with idiosyncratic features (e.g. volunteers). Because of this, one cannot be sure that the conclusions drawn about cause-effect-relationships do actually apply to people in other geographic locations or without these features.
Threats to external validity
"A threat to external validity is an explanation of how you might be wrong in making a generalization."[4] Generally, generalizability is limited when the cause (i.e. the independent variable) depends on other factors; therefore, all threats to external validity interact with the independent variable.
* Aptitude-Treatment-Interaction: The sample may have certain features that may interact with the independent variable, limiting generalizability. For example, inferences based on comparative psychotherapy studies often employ specific samples (e.g. volunteers, highly depressed, no comorbidity). If psychotherapy is found effective for these sample patients, will it also be effective for non-volunteers or the mildly depressed or patients with concurrent other disorders?
* Situation: All situational specifics (e.g. treatment conditions, time, location, lighting, noise, treatment administration, investigator, timing, scope and extent of measurement, etc. etc.) of a study potentially limit generalizability.
* Pre-Test Effects: If cause-effect relationships can only be found when pre-tests are carried out, then this also limits the generality of the findings.
* Post-Test Effects: If cause-effect relationships can only be found when post-tests are carried out, then this also limits the generality of the findings.
* Reactivity (Placebo, Novelty, and Hawthorne Effects): If cause-effect relationships are found they might not be generalizable to other settings or situations if the effects found only occurred as an effect of studying the situation.
* Rosenthal Effects: Inferences about cause-consequence relationships may not be generalizable to other investigators or researchers.
Write note on research proposal.
A research proposal is a document written by a researcher that provides a detailed description of the proposed program. It is like an outline of the entire research process that gives a reader a summary of the information discussed in a project.
Research proposals are written for various reasons, such as budget request for the research they describe, certification requirements for research example from an institutional review board committee if the experiment is to be done on human beings or animals protected by animal rights laws), as a task in tertiary education (e.g., before performing research for a dissertation), or as a condition for employment at a research institution (which usually requires sponsor-approved research proposals).
The phrasing of research proposals has many similarities to that of scientific articles. Research proposals are written in future tense and have different points of emphasis. Like scientific articles, research proposals have sections describing the research background, significance, methods, and references. The method section of research proposals is far more detailed than those of scientific articles, allowing profound understanding of the price and risks of the study and the plans for reducing them. Instead of a section describing the results, research proposals have a section describing the hypotheses or the expected results. A typical research proposal includes an extensive but focused literature review. A research proposal may also include preliminary results.
In contrast to scientific articles, research proposals usually contain the curriculum vitaes of the researchers. The curriculum vitaes are required for proving that the personnel asking to conduct the research are capable of doing so. For example, a research proposal for a study including injections would be expected to name at least one researcher qualified to inject human beings. Similarly, a research proposal in biology is not likely to receive funding when the entire staff consists of mathematicians only. In some academic institutes, a detailed resume of the thesis mentor is required on the research proposal in order to show that the mentor can help the student with the subject of the thesis.
Research sponsors publish calls for research proposals, specifying the topics into which they fund research and their detailed format requirements. Those sponsors may be governmental, nonprofit or business research foundations.
The research proposal drawn up by the investigator is the result i=of a planned, organized, and careful effort and basically contains the following:
1. The purpose of the study
2. The specific problem to be investigated
3. The scope of the study
4. The relevance of the study
5. The research design offering details on:
a. The sampling design
b. Data collection methods
c. Data analysis
6. Time frame of the study, including information on when the written report will be handed over to the sponsors
7. The budget, detailing the costs with reference to specific items of expenditure
8. Selected bibliography
Such a proposal containing the above features is presented to the manager, who might seek clarification on some points, want the proposal to be modified in certain respects, or accept it.
Discuss content and layout of research report:
Contents of Research Report:
The researcher must keep in mind that his research report must contain following aspects:
1. Purpose of study
2. Significance of his study or statement of the problem
3. Review of literature
4. Methodology
5. Interpretation of data
6. Conclusions and suggestions
7. Bibliography
8. Appendices
These can be discussed in detail as under:
(1) Purpose of study:
Research is one direction oriented study. He should discuss the problem of his study. He must give background of the problem. He must lay down his hypothesis of the study. Hypothesis is the statement indicating the nature of the problem. He should be able to collect data, analyze it and prove the hypothesis. The importance of the problem for the advancement of knowledge or removed of some evil may also be explained. He must use review of literature or the data from secondary source for explaining the statement of the problems.
(2) Significance of study:
Research is re-search and hence the researcher may highlight the earlier research in new manner or establish new theory. He must refer earlier research work and distinguish his own research from earlier work. He must explain how his research is different and how his research topic is different and how his research topic is important. In a statement of his problem, he must be able to explain in brief the historical account of the topic and way in which he can make and attempt. In his study to conduct the research on his topic.
(3) Review of Literature:
Research is a continuous process. He cannot avoid earlier research work. He must start with earlier work. He should note down all such research work, published in books, journals or unpublished thesis. He will get guidelines for his research from taking a review of literature. He should collect information in respect of earlier research work. He should enlist them in the given below:
1. Author/researcher
2. Title of research /Name of book
3. Publisher
4. Year of publication
5. Objectives of his study
6. Conclusion/suggestions
Then he can compare this information with his study to show separate identity of his study. He must be honest to point out similarities and differences of his study from earlier research work.
(4) Methodology:
It is related to collection of data. There are two sources for collecting data; primary and secondary. Primary data is original and collected in field work, either through questionnaire interviews. The secondary data relied on library work. Such primary data are collected by sampling method. The procedure for selecting the sample must be mentioned. The methodology must give various aspects of the problem that are studied for valid generalization about the phenomena. The scales of measurement must be explained along with different concepts used in the study.
While conducting a research based on field work, the procedural things like definition of universe, preparation of source list must be given. We use case study method, historical research etc. He must make it clear as to which method is used in his research work. When questionnaire is prepared, a copy of it must be given in appendix.
(5) Interpretation of data:
Mainly the data collected from primary source need to be interpreted in systematic manner. The tabulation must be completed to draw conclusions. All the questions are not useful for report writing. One has to select them or club them according to hypothesis or objectives of study.
(6) Conclusions/suggestions:
Data analysis forms the crux of the problem. The information collected in field work is useful to draw conclusions of study. In relation with the objectives of study the analysis of data may lead the researcher to pin point his suggestions. This is the most important part of study. The conclusions must be based on logical and statistical reasoning. The report should contain not only the generalization of inference but also the basis on which the inferences are drawn. All sorts of proofs, numerical and logical, must be given in support of any theory that has been advanced. He should point out the limitations of his study.
(7) Bibliography:
The list of references must be arranged in alphabetical order and be presented in appendix. The books should be given in first section and articles are in second section and research projects in the third. The pattern of bibliography is considered convenient and satisfactory from the point of view of reader.
(8) Appendices:
The general information in tabular form which is not directly used in the analysis of data but which is useful to understand the background of study can be given in appendix.
Layout of the Research Report:
There is scientific method for the layout of the research report. The layout of the report means as to what the research report should contain. The contents of the research report are noted below:
1. Preliminary Page
2. Main Text
3. End Matter
(1) Preliminary Pages:
These must be title of the research topic and data. There must be preface of foreword to the research work. It should be followed by table of contents. The list of tables, maps should be given.
(2) Main Text:
It provides the complete outline of research report along with all details. The title page is reported in the main text. Details of text are given continuously as divided in different chapters.
* (a) Introduction
* (b) Statement of the problem
* (c) The analysis of data
* (d) The implications drawn from the results
* (e) The summary
(a) Introduction:
Its purpose is to introduce the research topic to readers. It must cover statement of the problem, hypotheses, objectives of study, review of literature, and the methodology to cover primary and secondary data, limitations of study and chapter scheme. Some may give in brief in the first chapter the introduction of the research project highlighting the importance of study. This is followed by research methodology in separate chapter.
The methodology should point out the method of study, the research design and method of data collection.
(b) Statement of the problem:
This is crux of his research. It highlights main theme of his study. It must be in nontechnical language. It should be in simple manner so ordinary reader may follow it. The social research must be made available to common man. The research in agricultural problems must be easy for farmers to read it.
(c) Analysis of data:
Data so collected should be presented in systematic manner and with its help, conclusions can be drawn. This helps to test the hypothesis. Data analysis must be made to confirm the objectives of the study.
(d) Implications of Data:
The results based on the analysis of data must be valid. This is the main body of research. It contains statistical summaries and analysis of data. There should be logical sequence in the analysis of data. The primary data may lead to establish the results. He must have separate chapter on conclusions and recommendations. The conclusions must be based on data analysis. The conclusions must be such which may lead to generalization and its applicability in similar circumstances. The conditions of research work limiting its scope for generalization must be made clear by the researcher.
(e) Summary:
This is conclusive part of study. It makes the reader to understand by reading summary the knowledge of the research work. This is also a synopsis of study.
(3) End Matter:
It covers relevant appendices covering general information, the concepts and bibliography. The index may also be added to the report.
Important considerations of questionnaire design?
Design of the questionnaire can be split in to three elements:
1. determine the questions to be asked,
2. select the question type for each question and specify the wording, and
3. design the question sequence and overall questionnaire layout.
Available software tends to focus on support for (b) and (c).
Determine the Questions to be Asked
This step is a key one that seems not to be sufficiently stressed in the literature or conducted in practice. A key link needs to be established between the research aims and the individual questions via the research issues. Issues and questions can be determined through a combined process of exploring the literature and thinking creatively. A simple illustration of the outcome of such a process is given below.
Survey aims: to explore the factors that might explain the reasons that Leeds University candidates give for undertaking a MBA programme:
|Issue: |Question focus: |
|What reasons might candidates give for undertaking an MBA? |Is the candidate looking for: |
| |career change |
| |career advancement |
| |higher remuneration |
| |etc. |
|Could past experience affect the reasons? |How many years work experience does candidate have? |
|Could gender differences affect the reasons? |Is the candidate male or female? |
|Could educational background and attainment affect the reasons? |What is highest educational qualification obtained? |
| |What subject area(s) is this qualification in? |
|Etc. |Etc. |
The above process generates the focus for individual questions that can then be designed in detail.
Decide on a Layout and Sequence
Do not clutter the form up with unnecessary headings, and numbers. However, it is good practice to ensure that the questionnaire has a title and that the revision or date of the version is printed on the questionnaire. (This will particularly help as you take the draft through a series of revisions.) A brief introductory statement is useful, especially if the introductory letter could go adrift. Contact and return information should be included on the questionnaire, irrespective of whether addressed return envelopes are provided; these can easily become separated. Similarly it is good practice to number or otherwise identify individual questions for reference purposes; this is particularly helpful to deal with queries during the data entry and analysis stage.
Lay out the questions and answer choices attractively and neatly. Try to be consistent in aspects such as wording and try to standardise by using as few question types as possible. Avoid switching between landscape and portrait for the text layout. Be careful not to overfill the page. Avoid using lots of lines, borders and boxes since these can make the page look too 'dense'. A key factor that affects the response rate is the length of the questionnaire; questionnaires perceived as long will deter respondents. Using a small font can cut down the number of pages and hence make the questionnaire look shorter; but remember that small fonts can put people off - particularly those with less than perfect eyesight. Use a good legible font; a serif font like Times is easier to read than a sanserif one like Helvetica. Make good use of italics and bold types: think of using italics consistently to give instructions, e.g. tick the relevant box. Consider using bold for the questions themselves or for headings. Symbol fonts can be useful for characters such as boxes and ticks.
If you are relying on the respondent to complete the questionnaire, begin with questions that will raise interest. However, there are different views on sequencing of questions. For example, someone might argue that the easier questions to answer should be at the beginning to get the respondent in to the swing of things. However, someone else might suggest that questions about personal data, which are easy to answer, should be left until the end when the respondent has committed themselves to answering and they are less likely to object to giving such data. Whatever approach you choose you should try to have a logical sequence, e.g. group together all questions that relate to similar areas.
You should try to keep the flow through a questionnaire logical and very simple, i.e. avoid complex branching. Although some questions may be consequent upon earlier answers, keep the number of branches to the minimum. If necessary, use two or three versions of the questionnaire for respondents in different situations.
Question Types
Different types of questions can be used, e.g. open vs. closed, single vs. multiple responses, ranking, and rating.
Open vs. Closed Questions
Many advise against using open-ended questions and advocate using closed questions. However, open questions can be useful. For example, the open question:
What do you think are the reasons for football hooliganism?
would elicit a whole range of replies of varying length and articulation. If you are interested in making very precise judgements of each individual respondent this may well be useful. If, however, you are concerned, as most surveys are, in summarising replies to produce a picture of your population, a better approach may be ...
Do you think football hooliganism is caused by: (tick if appropriate)
|Lack of discipline at home | |
|Players' behaviour on pitch | |
|Family breakdown | |
|Youth unemployment | |
|Poor schooling | |
|Violence on T.V. | |
|Other (please specify) | |
Plan to make your categories exhaustive, i.e. covering all possibilities, by making fairly broad suggestions that will still satisfy your objectives. However, you could include the catch-all type option (Other) as above.
If you still feel that your questions cannot be categorised until all the replies are returned then ensure that sufficient space is included for the question and leave a space in the margin to code a numeric response.
e.g. What was the main problem you encountered
with your wheelchair?
Single vs. Multiple Response
When designing questions make sure you have thought through whether you want the respondent to give a single or a multiple response. For example, if you ask the question:
Which of the following means do you use to travel to college?
|Bus | |
|Car | |
|Bike | |
You might get someone who thinks that only one box should be ticked while another respondent might believe they are at liberty to tick as many boxes as they like. If you intend that the respondent treat these as a series of independent dichotomous yes/no questions then the question could be clarified by inserting the text "tick all boxes that apply". Note that treating the question in this way would require three separate variables to be set up on the computer and for each variable coding might be 0 for no and 1 for yes.
Consider the following question:
What is your most usual means of travelling to college?
(Tick one box only)
|Bus | |
|Car | |
|Bike | |
The above should elicit one response, i.e. the answers form mutually exclusive categories. For the computer the above would be coded as one variable with Bus represented by 1, Car by 2 and Bike by 3. If the respondent omitted to answer then this could be coded as 0 or some other missing value. However, note the problem with both the above questions if someone travels by train or just walks. Either all the possibilities have to be anticipated in advance or an additional box has to be offered for the respondent to tick and specify the mode of transport.
Never be tempted to use the following structure:
Select up to three of the options below and enter in the boxes opposite
|Option A |Option B |Option C |
|Option D |Option E |Option F | | | |
Whilst this may be logically sound you will have problems when summarising these results. A very tedious and finally unsatisfactory search is required to determine how any given option performs.
Ranked Responses
Sometimes it is useful for the respondent to rank a set of options by numbering them in order from 1 to the maximum number you are interested in.
For example, to a question like:
Place in order of importance to you the following features of a camping holiday
(Indicate by numbering from 1-4 in order where 1 is the most important)
|Open air | |
|Mobility | |
|Cost | |
|People | |
|Atmosphere | |
Note that each option will need to be coded as a separate variable and in the above case five variables are required (even though only four ranks are to be identified). This approach can generate a lot of data and so the number of options used should not be excessive. Apart from this respondents find it difficult to discriminate meaningfully between lots of options.
Rated Responses
A popular approach in the social sciences is to use Likert scales such as the example below:
(Circle the number under the initials that applies.
VI=Very important; I=Important; N=Neutral; U=Unimportant;
VU=Very Unimportant).
Indicate your view of the following aspects of a camping holiday
VI I N U VU
Community life 1 2 3 4 5
Low cost 1 2 3 4 5
Outdoor life 1 2 3 4 5
Ability to move around 1 2 3 4 5
Note that each of the four rows will form a separate variable that contains the appropriate numeric code from 1 to 5.
Decide on Question Wording
Some general rules can be stated on question wording:
• Be concise and unambiguous
• Avoid double questions
• Avoid questions involving negatives
• Ask for precise answers
• Avoid leading questions
Be Concise and Unambiguous
Make questions brief and clear. Avoid jargon; e.g. asking "do you believe that the UK should have upper and lower houses of parliament?" is more likely to elicit an informed response than "do you believe that the UK should have a bicameral parliament?"
Check for ambiguity and make sure that the answer may be competently answered. E.g. asking "have you been to the cinema recently" is more ambiguous than "have you been to the cinema in the last two weeks?"
Avoid Double Questions
Sometimes questions hide a dual question, for example:
Do you think the British should eat less and exercise more?
instead ask:
(Please circle relevant number)
Yes No
Do you think the British should eat less 1 2
Do you think the British should exercise more 1 2
Notice the consistent use of circled number responses for Yes/No questions.
Avoid Questions Involving Negatives
Don't confuse the respondent by language like this:
(Please circle relevant number)
Yes No
Are you against a ban on smoking 1 2
Ask for Precise Answers
Ask for precise answers if you think the information is available and there are no other constraints (e.g. too intrusive on privacy). For example:
|Give your age on lst September 2001 | |years |
is preferable to:
Are you...
|Under 18 | |
|18-65 | |
|Over 65 | |
In this example although age groups may be all that is required, asking for the exact age will also suffice and have the added advantages that:
There is less room for error, i.e. ticking the wrong box.
Exact ages may very simply be recoded, by a computer program, into groups.
The researcher can alter these groups by keeping the exact ages. Not only could you inspect different age groups than at first envisaged, but you could backdate your age groups.
You can obtain continuous descriptive statistics, e.g. mean and standard deviation.
Using a computer you can transform dates of birth into ages as long as you have the date on which the information was given. Of course an obvious advantage of offering groups to the respondent is that it can reduce the time taken to complete the question or, in some cases, improve the response rate to that particular question.
Avoid Leading Questions
Leading questions such as "Do you agree with the majority of people that the health service is failing?" should be avoided for obvious reasons that any right-minded individual can see. Don't you agree?
Using the Computer to Design and Construct the Questionnaire
Standard applications, such as a word-processing package, can help in designing and producing professional-quality questionnaires. However, specific packages are available that support the design (and subsequent analysis) of questionnaires, e.g.
Snap by Mercator
SphinxSurvey by Scolari (Sage)
Merlin Software by Merlinco
In fact there is a wide range of products and services available, many via the Internet. A good list of what is available can be obtained from on their pages: Business and Economy > Companies> Computers> Business to Business> Software> Surveys and Polling
Some further web resources that also have lists of available software are listed later in the references section. Apart from design software that you can buy to run on your computer, some companies offer software and services for free; but these are often tied to the company's proprietary data entry and analysis processes that you must pay to use. This is particularly the case where companies host the questionnaire on their web site for respondents to fill on-line. Notwithstanding these comments, such packages may also enable survey data to be imported and exported in SPSS and other file formats, e.g. survey interchange standard. If you can pay, companies will provide a full service from design through to analysis.
For the adventurous or the adept HTML author, a questionnaire can be built using HTML and CGI script to publish on a website (see e.g. chapter 11 on forms in Castro, 2000).
Confidentiality and Ethics
Firstly allow for privacy and do not ask questions which may offend, or ask for data that is not essential. Apart from anything else, your response rate will suffer.
Secondly, especially if you need to ask some personally searching questions, it helps to explain as much as you are able about your research to the respondent, both at the beginning and throughout the questionnaire.
For example having asked most of your general questions you might at the end write:
Finally could you give us a few bits of information about yourself so that we can put your other replies in greater context.
|Age in years | |
|Sex: Male | |
|Female | |
and so on.
In surveys promises of confidentiality are often made to the respondents to reassure and encourage replies. The researcher should comply with any such promises. However, it might help to explain to the potential respondent what is meant rather than give a blanket assurance of confidentiality, e.g. "your responses will be treated with confidence and at all times data will presented in such a way that your identity cannot be connected with specific published data". This is still compatible with publishing, with respondents' permissions, a list of participants who have co-operated in the survey.
In connection with issues of privacy researchers should be aware that the Data Protection and Human Rights legislation has implications for privacy and confidentiality of survey data. For example, researchers should only use data for the purposes that were declared on the questionnaire. Where the data is particularly sensitive or substantial you may want to include a statement in the questionnaire granting permission for the data to be used in connection with the research and ask respondents to sign. Apart from the legal issues, it is unethical to obtain data from respondents by misleading them about the survey purpose and the method of analysis.
Discuss Business Research:
In general, business research refers to any type of researching done when starting or running any kind of business. For example, starting any type of business requires research into the target customer and the competition to create a business plan. Conducting business market research in existing businesses is helpful in keeping in touch with consumer demand. Small business research begins with researching an idea and a name and continues with research based on customer demand and other businesses offering similar products or services. All business research is done to learn information that could make the company more successful.
Business research methods vary depending on the size of the company and the type of information needed. For instance, customer research may involve finding out both a customer’s feelings about and experiences using a product or service. The methods used to gauge customer satisfaction may be questionnaires, interviews or seminars. Researching public data can provide businesses with statistics on financial and educational information in regards to customer demographics and product usage, such as the hours of television viewed per week by people in a certain geographic area. Business research used for advertising purposes is common because marketing dollars must be carefully spent to increase sales and brand recognition from ads.
An organized,systematic,databased, critical,objective, scientific inquiry or investigation into a specific problem, undertaken with the propose of finding answers or solutions to it.
Research provides the needed information that guides managers to make informed decisions to successfully deal with problems.
The information provided could be the result of a careful analysis of data gathered firsthand or of data that are already available (in the company).
Types of Business research.
1.Applied research
Is to solve a current problem faced by the manager in the work setting,demanding a timely solution.
2.Basic research (fundamental, pure)
Is to generate a body of knowledge by trying to comprehend how certain problems that occur in organizations can be solved.
The findings of such research contribute to the building of knowledge in the various functional areas of business.
Basic Research
Basic (fundamental or pure ) research is driven by a scientist's curiosity or interest in a scientific question. The main motivation is to expand man's knowledge , not to create or invent something. There is no obvious commercial value to the discoveries that result from basic research.
For example, basic science investigations probe for answers to questions such as:
How did the universe begin? What are protons, neutrons, and electrons composed of? How do slime molds reproduce? What is the specific genetic code of the fruit fly?
Applied research
Applied research is designed to solve practical problems of the modern world, rather than to aqcquire knowledge for knowledge's sake. One might say that the goal of the applied scientist is to improve the human condition .
For example, applied researchers may investigate ways to:
improve agricultural crop production , treat or cure a specific disease, improve the energy efficiency of homes, offices, or modes of transportation
Importance:
• Solve problems
• Decision making tool
• Competition
• Risk
• Investment
• Hire researchers and consultants more effectively
Ethics and business research
• Ethics in business research refers to a code of conduct or expected societal norm of behavior while conducting research.
• Ethical conduct applies to the organization and the members that sponsor the research, the researchers who undertake the research, and the respondents who provide them with the necessary data.
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