want to decide something major, or just establish a course of action? The sample size is crucial if you plan to extrapolate your survey results to a larger population. Make sure it's fair and accurately represents the neighborhood as a whole. The sample size is less important if you're seeking to gauge preferences. You might be asking US homeowners how much it costs to cool their homes throughout the summer. You might be asking US homeowners how much it costs to cool their homes throughout the summer. Compared to Denver, where the environment is more dry and temperate, a homeowner in the South undoubtedly spends a lot more money cooling their home in the humid heat. You must collect replies from people in all US regions and environments if you want the most accurate results. Your results will be distorted if you just gather opinions from one extreme, such as the hot South. 2. Precision level How accurately do you want the survey results to reflect the genuine value if everyone participated? Again, your sample size decision should be precise if this survey will determine how you will spend millions of dollars. The larger the sample you wish to use and the more accurately you need to be able to reflect the entire population, respectively. You might prefer to survey the full population rather than a sample it if your population is tiny, let's say 200 people. 3. Confidence level Consider confidence from the standpoint of danger. What level of risk are you willing to accept? Your Confidence Interval numbers become crucial at this point. What level of confidence do you want to have—98%, 95%? Be aware that the number of completions you'll need for accuracy depends significantly on the confidence % you select. This could lengthen the survey and increase the number of replies you require, which would raise the cost of the survey. Knowing the precise numbers and amounts underlying percentages might make it easier to compare survey expenses and sample size requirements. For instance, you should have 99% confidence. You discover you need to gather an additional 1000 responders after applying the sample size calculation formula. As a result, you'll either have to pay for samples or extend the duration of your survey by a week or two. You must decide whether the cost is more significant than the improved precision. 4. Population variability: 101 CU IDOL SELF LEARNING MATERIAL (SLM)
What kind of variation can you find in your population? Or to put it another way, how similar or unlike is the population? If you are doing a customer survey on a large subject, there can be many variations. To achieve the most accurate representation of the population, you'll need a larger sample size. However, your variability will be lower and you can sample fewer people if you are surveying a community that shares your demographics. Less variability results in a smaller sample, whereas greater variability results in a larger sample. You can start with 50% variability if you're unsure. 5. Response rate: You desire a 100% response rate for your survey. Unfortunately, targeted respondents in every survey either never open the study or stop participating halfway through. The level of consumer engagement with your brand, company, or product will determine your response rate. The amount of involvement from your population increases with response rate. The minimum number of replies required for a valid sample size is for a survey. 6. Think about your audience: In addition to the variation within your population, you must ensure that no one in your sample will gain anything from the findings. Forgetting to take your actual audience into account when determining sample sizes is one of the worst blunders you can make. For instance, you shouldn't send a survey to a group of homeowners asking about the standard of neighborhood apartment amenities. 7. Concentrate on the goals of your survey: You might begin with broad demographics and traits, but is it possible to go more specific with those traits? You can obtain a more precise result from a small sample size by focusing on a smaller audience. For instance, you are interested in how society will respond to new automotive technologies. Anyone who owns an automobile in a specific market is part of your current demographic. You are aware that the folks who drive fewer than five-year-old cars are your target market, though. Anyone driving an older car should be excluded from your sample because they are unlikely to buy your goods. You can choose how to compute the sample size once you are aware of what you expect to learn from your survey and the variables that are present in your population. To acquire accurate findings, using the method for calculating sample size is an excellent place to start. 102 CU IDOL SELF LEARNING MATERIAL (SLM)
Finding a trustworthy customer survey software can help you effectively gather survey responses and transform them into evaluated reports after you've calculated your sample size. 7.4 PROBABILITY AND NON –PROBABILITY SAMPLING METHODS Probability Sampling: Using an approach based on probability theory, the researcher selects samples from a broader population using the probability sampling methodology. A participant must be chosen at random in order for them to be taken into account as a probability sample. The most important prerequisite for probability sampling is that each member of your population has an equal and known chance of being chosen. Probability sampling is the process of selecting a small sample of people at random from a large population and then predicting that all of their responses will be representative of the entire population. Any sampling technique that makes use of a random selection in any way is referred to as a probability sampling technique. You must build up a strategy or system that ensures that the various units in your population have equal chances of being chosen in order to have a random selection approach. Numerous random selection techniques have been used by humans for a very long time, including drawing names out of a hat and choosing the short straw. Nowadays, we frequently employ computers to generate random numbers that serve as the foundation for random selection. Every member of the population has a possibility of getting chosen when sampling using probability. Mostly quantitative research uses it. Probability sampling techniques are the best option if you wish to generate findings that are inclusive of the entire population. Types of Probability Sampling: 1. Simple random sampling: As the name implies, simple random sampling is a completely arbitrary way of selecting the sample. Using an automated process, randomly choose individuals from the sample by assigning them numbers is how this sampling method works. Finally, the members of the sample are the numbers that were selected. 103 CU IDOL SELF LEARNING MATERIAL (SLM)
In this sampling technique, the samples are chosen by the researchers using either the lottery system or a random number generator/table. This sampling method typically works with a sizable population and has both benefits and drawbacks. You want to select a simple random sample of 100 employees of Company X. You assign a number to every employee in the company database from 1 to 1000, and use a random number generator to select 100 numbers. Simple random sampling is the random sample technique that is most frequently employed. An equal chance of being included in the sample exists for each item in the population as a whole in a basic random sample. Furthermore, the choice of one item for the sample should not in any way affect the choice of another item. When a population is homogeneous, or when the items in the population share the same characteristics that the researcher is interested in, simple random sampling should be utilized. Age, sex, income, social/religious/political affiliation, geographic region, and other factors can all be considered as homogeneity traits. Use of a random number table is the best method for selecting a straightforward random sample. The following requirements should be met by a random sampling technique. a) Each person in the population must have a fair chance of being selected for the sample. b) The decision regarding one member is unaffected by the decision regarding the other members. Simple random sampling is the easiest method of choosing a sample. Each participant has an equal probability of participating in the study using this approach. Each object in this sample population has the same chance of being chosen, and the objects are chosen entirely at random. For instance, if a university dean wanted to gather input from students about how they felt about the professors and their level of education, this sample could include all 1000 students at the university. To create this sample, 100 students can be chosen at random from the entire class. 2. Systematic sampling Simple random sample and systematic sampling are comparable, but systematic sampling is typically a little simpler to carry out. Every person in the population is assigned a number, but instead of assigning numbers at random, people are picked at predetermined intervals. 104 CU IDOL SELF LEARNING MATERIAL (SLM)
When using systematic sampling, a population is sampled by randomly selecting respondents at equal intervals. Picking a starting point and then choosing responders at a predetermined sample interval is the method used to choose the sample. As an illustration, when choosing 1,000 volunteers for the Olympics from a list of 10,000 applicants, each applicant is assigned a count between 1 and 10,000. Then, a sample of 1,000 volunteers can be obtained by counting backwards from 1 and choosing each respondent with a 10-second interval. All employees of the company are listed in alphabetical order. From the first 10 numbers, you randomly select a starting point: number 6. From number 6 onwards, every 10th person on the list is selected (6, 16, 26, 36, and so on), and you end up with a sample of 100 people. When you choose every \"nth\" person to be a member of the sample, you are said to be using systematic sampling. As an illustration, you may choose every fifth person to make up the sample. An expanded version of the well-known probability strategy, systematic sampling involves selecting a representative sample from the entire group on a regular basis. Using this sampling strategy, each member of a population has an equal chance of being chosen. It's crucial to check the list for any hidden patterns that could bias the sample if you employ this strategy. There is a chance that your interval might skip over persons in junior jobs, leading to a sample that is skewed towards senior employees, for instance, if the HR database groups employees by team and team members are listed in order of seniority. 3. Stratified sampling Stratified sampling entails breaking the population up into smaller groups that might have significant differences. By ensuring that each subgroup is fairly represented in the sample, it enables you to reach more accurate findings. By dividing the population into smaller groups, or strata, according to the relevant trait, you can employ this sampling technique (e.g. gender, age range, income bracket, job role). You determine the appropriate number of individuals to sample from each subgroup based on the population's overall proportions. Then you choose a sample from each subgroup using random or systematic sampling. The company has 800 female employees and 200 male employees. You want to ensure that the sample reflects the gender balance of the company, so you sort the population into two strata based on gender. Then you use random sampling on each group, selecting 80 women and 20 men, which gives you a representative sample of 100 people. 105 CU IDOL SELF LEARNING MATERIAL (SLM)
With stratified random sampling, the researcher separates a larger population into more manageable groups that often don't overlap but yet accurately reflect the overall population. Create these groups before sampling, and then take a sample from each group independently. Organizing or categorizing by sex, age, ethnicity, and other factors is a common practice. dividing topics into groups that are mutually exclusive, then selecting individuals from each category by simple random sampling. Members of these groups should be unique from one another to ensure that each group member has an equal chance of being chosen using basic probability. \"Random quota sampling\" is another name for this sampling strategy. 4. Cluster sampling The population is also divided into smaller groups for cluster sampling, although each smaller group should share traits with the larger sample. You choose complete subgroups at random rather than picking a representative sample of each subgroup. You could, if it is practically feasible, include each and every member of each sampled cluster. You can also sample people from each cluster using one of the aforementioned methods if the clusters are large. Multistage sampling is the name for this. Although this strategy is effective for handling big and dispersed populations, there is a higher chance of mistake in the sample due to the possibility of significant differences between clusters. It is challenging to ensure that the sampled clusters accurately reflect the entire population. The company has offices in 10 cities across the country (all with roughly the same number of employees in similar roles). You don’t have the capacity to travel to every office to collect your data, so you use random sampling to select 3 offices – these are your clusters. A sample technique called cluster sampling divides the respondent population into equal clusters. Based on defining demographic factors like age, location, sex, etc., clusters are found and included in a sample. This makes it incredibly simple for a survey developer to draw useful conclusions from the responses. For instance, the FDA might divide the US mainland into distinct clusters, such as states, in order to gather data on the negative side effects of medications. Respondents in these clusters are subsequently given research surveys. This method of creating a sample allows for in-depth data collection and offers insights that are simple to understand and put into practice. 106 CU IDOL SELF LEARNING MATERIAL (SLM)
Non-Probability Sampling: People are chosen for inclusion in a non-probability sample using non-random criteria, therefore not everyone has the same chance of doing so. Although it is simpler and less expensive to obtain this kind of sample, there is a greater chance of sampling bias. As a result, your conclusions may be more constrained and the inferences you can draw about the population are weaker than with random samples. Even if you choose a non-probability sample, you should still try to reflect the population as accurately as you can. Exploratory and qualitative research frequently employ non-probability sampling methods. The goal of this kind of research is to gain a preliminary understanding of a small or understudied population rather than to test a theory about a large population. The non-probability sampling methods are another name for non-random sampling techniques. The likelihood that any specific unit of the population will be selected via a non- random sampling process is uncertain. Here, choosing the sampling units is done fairly arbitrarily because the researchers mainly rely on their own judgment. Typically, non-random sampling techniques don't yield samples that are representative of the community at large. The biggest mistake is made when a researcher tries to extrapolate data from a sample to the entire population. Such a mistake is sneaky because it cannot be detected by simply glancing at the data or even the sample. 1. Convenience sampling Simply said, a convenience sample consists of those who are easiest to reach by the researcher. Although it is quick and affordable, this method cannot yield generalizable conclusions because it is impossible to determine whether the sample is typical of the population. You are researching opinions about student support services in your university, so after each of your classes, you ask your fellow students to complete a survey on the topic. This is a convenient way to gather data, but as you only surveyed students taking the same classes as you at the same level, the sample is not representative of all the students at your university. Convenience sampling is the process of getting a sample such that the researcher can use it as quickly and easily as possible. For instance, it would be practical and cost-effective to sample contact center employees in the neighborhood if we are interested in learning how much 107 CU IDOL SELF LEARNING MATERIAL (SLM)
overtime pay is given to workers at call centers. Additionally, the television stations frequently feature on-the-street interviews with people to reflect public opinion on a variety of matters of public concern, such as the budget, election, price increase, etc. It should be noted that it might not be suitable to generalize results from convenience sampling beyond that specific sample. It is recommended to employ convenience samples for exploratory research when further investigation will be done using a random sample. Testing the pilot-designed questionnaires also makes advantage of convenience sampling. Marketing studies frequently employ convenience sampling. In plain English, convenience sampling refers to the ease with which a researcher can contact a respondent. The method used to create this sample is not scientific. Researchers have very little control over the sample elements' selection, which is solely based on proximity rather than representativeness. When gathering feedback is time- and money-constrained, this non- probability sampling technique is used. As an illustration, consider researchers who are conducting a mall-intercept study to determine the likelihood that people will use a fragrance produced by a perfume business. With this sampling technique, sample respondents are selected solely based on their accessibility to the survey desk and their interest in taking part in the study. 3. Judgemental/purposive sampling: A judgment sample is another name for a purposive sample. This kind of sample was chosen because there are solid grounds for thinking that it accurately represents the entire population. Based on prior experience with or understanding of the target group, the researcher chooses a sample. For instance, in a study of \"gifted\" kids, the researcher chooses some kids who perform very well in class while eliminating everyone else from the sample based on past experience. In contrast to a convenience sample, a purposive sample is chosen because the researcher has certain objectives in mind and doesn't just choose participants based on availability. The basis for characterizing and justifying purposive samples is the set of unambiguous criteria. Since the primary goal is to choose individuals who can provide rich material for in-depth study on the specific topic and setting, rather than participants who necessarily represent some larger community, a major portion of sampling in qualitative research is purposeful. The quality of 108 CU IDOL SELF LEARNING MATERIAL (SLM)
the participants' capacity to supply the needed information takes precedence above representativeness. There are roughly 16 different sorts of particular procedures that can be utilized in qualitative research that fall under the purposive sampling category. Some of these include: Purposive random sampling One may choose a necessary number of subjects from the purposively selected subjects when the purposive sample is larger than one can handle. This strategy is known as random purposive sampling. For instance, if the researcher purposefully found 20 possible volunteers but could only study 10, a random sample of 10 from the 20 persons would be picked. The judgmental or purposive sampling method is a technique for creating a sample solely at the researcher's discretion and in accordance with the nature of the study and his or her comprehension of the target audience. Only those individuals who meet the research criteria and end goals are chosen using this sample technique, while the rest are excluded. For instance, if the question presented is, \"Would you like to take your Masters?\" and the response option is not \"Yes,\" then everyone else is not included in the study. The research topic is, \"Understanding What University a Student Prefers for Masters.\" In this sampling technique, the researcher's assessment of a necessary attribute for the sample units serves as the basis for selecting the sample. For instance, a judgment sample of a basket of consumer goods, together with other relevant commodities and services, is used to calculate the consumer price index. This sample is meant to reflect a representative sample of goods used by the people. Prices for these goods are gathered from a few cities that are considered representative of the nation in terms of their demographic features. Judgment sampling is frequently used in business to evaluate the effectiveness of salespeople and saleswomen. Based on specific criteria, the salespeople are divided into high, medium, and low performance. After that, the sales manager may really group the salespeople and saleswomen who report to him or her according on the category they believe they belong to. Forecasting election results frequently makes use of judgment sampling. We might frequently ponder how a pollster could forecast an election with only 2% to 3% of the total votes cast. It goes without saying that the methodology is flawed and lacks a scientific foundation. However, one may use this kind of non-random sampling if there are no representative data available. 109 CU IDOL SELF LEARNING MATERIAL (SLM)
It is frequently employed in qualitative research when the researcher prefers to learn in-depth information on a particular occurrence versus drawing general conclusions from statistics or when the population is relatively tiny and focused. An successful purposive sample must have precise inclusion requirements and justifications. You want to know more about the opinions and experiences of disabled students at your university, so you purposefully select a number of students with different support needs in order to gather a varied range of data on their experiences with student services. 3. Snowball sampling: Snowball sampling or chain-referral sampling is defined as a non-probability sampling technique in which the samples have traits that are rare to find. This sampling method uses recommendations from current participants to find the sample populations needed for a study. For instance, when asked for feedback on a touchy subject like AIDS, respondents are reticent to provide details. In this situation, the researcher can enlist others who understand or are familiar with such individuals and ask them to gather information on behalf of the researcher. You are looking into local homeless people's experiences. Probability sampling is not feasible because there is no comprehensive list of all homeless people in the city. One person you meet agrees to take part in the study, and she connects you with other local homeless persons she knows. It entails choosing a small group of people who can point out more individuals who could be excellent study subjects. For instance, while conducting demographic interviews, you might ask those being questioned to suggest other people who might be contacted for information or opinions on the subject. Interviewing these new people follows, and you carry on in this manner until the material is exhausted—that is, until the new people offer no fresh perspectives. For instance, a researcher who wants to look into how distance learners feel about the caliber of Gyanvani programs can only discover five listeners. She inquires further of the students. They present her with numerous further recommendations, who then connect her with other contacts. This is how she manages to contact sufficient Gyanvani listeners. When we do not have access to enough individuals who fit the criteria we are looking for, such as when potential participants are dispersed or not located in clusters, or for populations that are neither well defined nor well enumerated, like the homeless, snowball sampling is most helpful. 110 CU IDOL SELF LEARNING MATERIAL (SLM)
The disadvantage is that you are unable to determine the precise distribution of viewpoints within the target population. Furthermore, people frequently suggest those who they know well and who have their own opinions, thus smaller groups of interest frequently go overlooked. Asking individuals to nominate both such people who hold the same ideas and such people who hold the opposite view could be one way to make up for this. Another strategy is to have multiple people, perhaps from other social circles, begin the snowball chain instead than just one. 5. Quota sampling: Quota sampling is a method of collecting a sample where the researcher has the liberty to select a sample on their stratification. This method's main characteristic is that two persons cannot coexist in two different environments. For instance, a shoemaker would want to know how millennials view the brand in relation to other factors like comfort, cost, etc. Given that the goal of the survey is to seek clarity on women's shoes, it exclusively selects millennial females for the study. 5. Consecutive sampling: This non-probability sampling method is very similar to convenience sampling. Here, the researcher chooses one subject or a group of subjects as a sample, conducts research over time, evaluates the findings, and if necessary, shifts to a different individual or group. The researcher can work with a variety of themes and fine-tune his or her research by gathering results that provide important insights using the consecutive sampling technique. 6. Intensity sampling: Selecting cases with rich information that exhibit the phenomenon intensely and allow study of various levels of research topic, but not extreme or deviant cases, such as good students/poor students, above average/below average, experienced/inexperienced distance tutors, is known as intensity sampling. For intensity sampling to be able to find intense examples, background knowledge and exploratory work are needed. Intensity sampling could be used in conjunction with other sampling techniques. For more in-depth study, one can, for instance, gather 50 cases and then choose a subset of the most intense cases. 7. Homogenous sampling: 111 CU IDOL SELF LEARNING MATERIAL (SLM)
This is the process of choosing participants who have a great deal in common in terms of background, outlook, or perspective. This lowers variation and makes gathering and analyzing data easier. For example, it may concentrate on one program, such as B.Ed. exclusively, rather than having the greatest number of students enrolled in all professional programmes available through distant mode. 7.5 SUMMARY Sampling mistakes can be removed by increasing sample sizes. However, a fourfold increase in sample size is required to cut them in half. The analysts can choose a larger number of samples if the chosen samples are small and do not effectively represent the entire set of data. Larger mistakes result from changes in the estimates produced from different samples due to population variability. By making the samples bigger so they can more accurately represent the population, the impact of population variability can be lessened. Simple random sampling is the random sample technique that is most frequently employed. An equal chance of being included in the sample exists for each item in the population as a whole in a basic random sample. Furthermore, the choice of one item for the sample should not in any way affect the choice of another item. Simple random sample and systematic sampling are comparable, but systematic sampling is typically a little simpler to carry out. Every person in the population is assigned a number, but instead of assigning numbers at random, people are picked at predetermined intervals. The number of persons who fit your demography is your population size. For instance, you might wish to learn more about physicians practicing in North America. The total number of physicians in North America is the amount of your population. Not to worry! It's not necessary to continually have a large population. As long as you are aware of who you are aiming to represent, smaller populations can still provide you with reliable data. A good study design is thought to minimise the biasness while maximising the dependability of the data being gathered and analysed. The opportunity should be offered in accordance with the numerous parts of the study challenge in a solid 112 CU IDOL SELF LEARNING MATERIAL (SLM)
research design. It should deliver the most information while minimising experimental error. Thus, it can be inferred that the choice of research design depends on the nature of the research and the research topic A sample technique called cluster sampling divides the respondent population into equal clusters. Based on defining demographic factors like age, location, sex, etc., clusters are found and included in a sample. This makes it incredibly simple for a survey developer to draw useful conclusions from the responses. For instance, the FDA might divide the US mainland into distinct clusters, such as states, in order to gather data on the negative side effects of medications. Respondents in these clusters are subsequently given research surveys. This method of creating a sample allows for in-depth data collection and offers insights that are simple to understand and put into practice. Simple or unrestricted random sampling, systematic sampling, stratified sampling, cluster sampling, multi-stage sampling and probability proportion to size sampling are the six main types of probability sampling. In all these types each unit in the sample has some known probability of entering the sample. In simple or unrestricted random sampling each unit of the population is given an equal chance of being selected, and the selection of any one unit is in no way tied to the selection of any other. The law of chance is allowed to operate freely in the selection of such samples and carefully controlled conditions are created to ensure that each unit in the population has an equal chance of being included in the sample. The researcher may use the lottery method or a table of random numbers for drawing a simple random sample. Simple random sampling ensures best results. However, it is neither feasible nor possible if the lists of the units do not exist or if such lists are incomplete. If there is more heterogeneity among the units of the population, a simple random sample may not necessarily represent the characteristics of the total population even if all selected units participate in the investigation 7.6 KEYWORDS Census : A complete survey of population is called census. Convenient Sampling : Here the units of the population are included in the sample as per the convenience of the researcher. 113 CU IDOL SELF LEARNING MATERIAL (SLM)
Cluster Sampling: In cluster sampling method we divide the population into groups called clusters, selective sample of clusters using simple random sampling and then cover all the units in each of the clusters included in the sample. Judgment Sampling: In this sampling method the selection of sample is based on the researcher’s judgment about some appropriate characteristics required of the sample units. Multi-stage Sampling: Here we select the sample units in a number of stages using one or more random sampling methods. Non-sampling Errors : The non-sampling errors arise from faulty research design and mistakes in executing the research. Non-random Sampling/Non-ProbabilitySampling : In this sampling method the probability of any particular unit of the population being included in the sample is unknown. Parameters : The numerical characteristics of a population are called parameters. 7.7LEARNING ACTIVITY 1. Define Parameters ___________________________________________________________________________ ___________________________________________________________________________ 2. State the meaning of sampling error ___________________________________________________________________________ ___________________________________________________________________________ 3. Explain the various types of probability sampling. ___________________________________________________________________________ ___________________________________________________________________________ 4. Write a note on systematic sampling. ___________________________________________________________________________ ___________________________________________________________________________ 114 CU IDOL SELF LEARNING MATERIAL (SLM)
7.8 UNIT END QUESTIONS A. Descriptive Questions Short Questions: 1. Define systematic sampling 2. Explain what is stratified sampling? 3. Describe briefly about non-probability sampling ? 4. What do you understand by cluster sampling? 5. What is a sampling error? Long Questions: 1. Write a note on probability sampling. 2. Write a note non-probability sampling 3. Write a note on sampling error 4. What to do for sample sizedetermination? B. Multiple Choice Questions 1_________ is the random sample technique that is most frequently employed. a. Simple random sampling b. Cluster sampling c. Non-probability sampling d. Convenience sampling 2. A sample technique called _________ sampling divides the respondent population into equal clusters a. cluster 115 b. simple random c. stratified d. none of the above 3. The number of persons who fit your demography is your _______ CU IDOL SELF LEARNING MATERIAL (SLM)
a. population size b. Sample size c. stratified sampling d. simple random sampling 4. ______ sampling is the process of getting a sample such that the researcher can use it as quickly and easily as possible a. Convenience b. stratified c. cluster d. random Answers 1-a, 2-a, 3-a, 4-a 7.9 REFERENCES Shukla, Satishprakash, (2020) Research Methodology and Statistics. Ahmedabad: Rishit Publications. Shukla, Satishprakash, (2014) Research – An Introduction (Gujarati) Ahmedabad: KshitiPrakashan Kothari C.R. : Research Methodology, New Age International, 2011. Shajahan S. : Research Methods for Management, 2004. Thanulingom N : Research Methodology, Himalaya Publishing C. Rajendar Kumar : Research Methodology , APH Publishing Kumar Ranjit: Research Methodology: A Step by Step Guide for Beginners, Sage Publication, 2014 Website https://corporatefinanceinstitute.com/resources/knowledge/other/sampling-errors/ https://www.scribbr.com/category/methodology/ https://www.slideshare.net/aditigarg.aditigarg/research-process-14719283 116 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT – 8HYPOTHESIS-I STRUCTURE 8.0 Learning Objectives 8.1 Introduction 8.2 Functions of Hypothesis 8.3 Characteristics of Good Hypothesis 8.4 Summary 8.5 Keywords 8.6 Learning Activity 8.7 Unit End Questions 8.8 References 8.0 LEARNING OBJECTIVES After studying this unit, you will be able to: Describe meaning of hypothesis Identify function of hypothesis State the need and importance of hypothesis List the characteristics of hypothesis 8.1INTRODUCTION TOHYPOTHESIS The creation of a hypothesis is the second crucial factor to be taken into account when formulating a research problem in quantitative research. Although they are not necessary for a study, hypothese help research problems gain clarity, specificity, and concentration. A valid research can be carried out without generating even a single formal hypothesis. On the other hand, you are free to develop as many hypotheses as you see fit within the framework of a research project. Some people think that in order to do research, a hypothesis must be developed; the author disagrees with this view. It is possible to conduct a completely valid investigation without having any of the 'hunches' or'speculations' that serve as the basis of hypotheses. In epidemiological investigations, it's crucial to develop hypotheses in order to focus the investigation's scope. 117 CU IDOL SELF LEARNING MATERIAL (SLM)
The value of hypotheses resides in their capacity to give a research project direction, specificity, and emphasis. They provide a researcher more direction by outlining the precise data they should gather. Let's say you place a wager when you are at the races. You place a wager in hopes that a specific horse will triumph. Only after the race will you know if your intuition was accurate. Using another illustration Consider the possibility that you suspect your class contains more smokers than non-smokers. You enquire whether or not any students in the class smoke in order to verify your suspicion. Then you may decide if your intuition was correct or incorrect. Let's look at a different example now. Let's say you are employed in the field of public health. Your clinical impression is that people who belong to a given population subgroup have a higher prevalence of a particular illness. You need to determine what is probably to blame for this disease. There may be numerous causes. It would take a tremendous amount of time and resources to investigate every imaginable scenario. In order to reduce the options, you might choose what you believe to be the most likely cause based on your expertise in the area. Then you may plan a research to get the data required to confirm your suspicion. If after checking, you were able to determine that the presumed cause indeed caused the condition, your assumption would have been right In each of these instances, you made an initial guess or intuition. In two of the cases—horse racing and the other two—you developed a research to evaluate the validity of your assumption, and it was only after thorough examination that you came to a conclusion about the validity of your assumptions. Similarly, hypotheses are built on reasoning. As a researcher, you may not be aware of a phenomenon, a circumstance, the prevalence of a condition in a population, or the results of a programme, but you may have an intuition that can serve as the foundation for some assumptions or educated estimates. You test these, typically one at a time, by gathering data that will allow you to determine whether your intuition was correct. There are three possible results from the verification process. Your instinct may turn out to be entirely wrong, somewhat right, or right. You cannot draw any conclusions regarding the veracity of your assumption without undergoing this procedure of verification. In light of this, a hypothesis is a hunch, assumption, suspicion, assertion, or concept regarding a phenomenon, relationship, or circumstance about which you are unsure of the 118 CU IDOL SELF LEARNING MATERIAL (SLM)
reality or truth. These presumptions, claims, declarations, or hunches are referred to as hypotheses by researchers and serve as the foundation for inquiries. In the majority of investigations, the hypothesis will be founded on either earlier studies, your own findings, or the observations of others. There are many definitions of a hypothesis. According to Kerlinger, ‘A hypothesis is a conjectural statement of the relationship between two or more variables’ (1986: 17). Webster’s Third New International Dictionary (1976) defines a hypothesis as: a proposition, condition, or principle which is assumed, perhaps without belief, in order to draw out its logical consequences and by this method to test its accord with facts which are known or may be determined According to Grinnell: A hypothesis is written in such a way that it can be proven or disproven by valid and reliable data – it is in order to obtain these data that we perform our study From the above definitions it is apparent that a hypothesis has certain characteristics: 1. It is a tentative proposition. 2. Its validity is unknown. 3. In most cases, it specifies a relationship between two or more variables 8.2 FUNCTION OF HYPOTHESIS Although some researchers think that having a hypothesis is necessary in order to perform a study, as was already noted, having a hypothesis is not required. A hypothesis is crucial, though, in that it clarifies the research problem. A hypothesis specifically accomplishes the following tasks: • The development of a hypothesis gives a study direction. It outlines the precise components of a study subject that you should look into. • A hypothesis gives the study focus by indicating which data to gather and which to leave out. • The development of a hypothesis improves objectivity in a study by giving it a focus. • Using a hypothesis may help you add to the development of a theory. It allows you to determine precisely what is true or false. 119 CU IDOL SELF LEARNING MATERIAL (SLM)
• An observation or experiment can be made possible with the aid of a hypothesis. • It serves as the investigation's starting point. • Verifying hypotheses aids in confirming observations. • It assists in guiding the appropriate enquiries. What is sought after in an experiment is stated in a hypothesis. A hypothesis is created when facts are put together, arranged, and seen in connection to one another. For further factual verification, this theory must be deduced; the formulation of the deductions itself is a hypothesis. Because a theory asserts a logical connection between the facts, the conclusions drawn from it should hold true. These derived prepositions are therefore known as hypotheses. 8.3 CHARACTERISTICS OF GOOD HYPOTHESIS When formulating a hypothesis, there are certain things to keep in mind because they are crucial for reliable verification. Therefore, a hypothesis' formulation must possess specific qualities that make it simple for you to determine its validity. These characteristics are: 1. A hypothesis should be simple, specific and conceptually clear: A hypothesis needs to be theoretically sound, concise, and explicit. There should be no room for ambiguity while building a hypothesis because it will make it nearly impossible to verify your claim. It should be \"unidimensional\" in the sense that it should only test one hypothesis or relationship at a time. You need to be knowledgeable about the topic in order to be able to construct a solid hypothesis (the literature review is of immense help). The process of developing a hypothesis is made simpler the more understanding you have of a topic. For instance: The male students in this class are older on average than the female students. The aforementioned claim is precise, straightforward to test, and clear. It explains what you are comparing (the average age of this class), which demographic categories are being compared (female and male students), and what you are aiming to establish (higher average age of the male students). Take this as another illustration: Social cohesion and suicide rates are adversely correlated. 120 CU IDOL SELF LEARNING MATERIAL (SLM)
Although this hypothesis is more challenging to evaluate, it is straightforward and explicit. Suicide rates, \"vary inversely,\" which specifies the direction of the link, and \"social cohesion\" are the three components of this theory. Finding the suicide rates and determining if there is an inverse relationship or not is rather simple, but determining social cohesion is much more challenging. How is social cohesion determined? How might it be quantified? Testing this hypothesis is made more challenging by this issue. 2. A hypothesis should be capable of verification It should be possible to test a theory. Techniques and methods must be provided for gathering and analysing data. A hypothesis cannot be tested because there are no methods for doing so, hence there is no purpose in putting one forth. This does not imply that you should never make a hypothesis for which there is no way to verify it, though. As you conduct your investigation, you might come up with fresh ways to confirm it. 3. A hypothesis should be related to the existing body of knowledge A hypothesis needs to be relevant to the corpus of existing knowledge. It is crucial that your hypothesis builds upon the corpus of information already in existence because this is a crucial role that research plays. This is only possible if the theory is grounded in the body of existing knowledge. 4. A hypothesis should be operationalizable A hypothesis need to be testable. It can therefore be expressed in terms that can be measured, according to this. There can be no conclusions reached if it cannot be measured because it cannot be tested. 5. Complete Clarity: A solid hypothesis should have two key components: concepts that are precisely defined and definitions that can be communicated to and accepted by a wider audience. To further the cause, a variety of resources and friends may be employed. 6. Empirical Referents: A strong hypothesis should include the most important empirical referent together with scientific notions. The purpose should be distinct from moral preaching and the adoption of principles; it cannot be founded on moral judgement, even though it may examine them. Analyzing ideas that represent attitudes as opposed to explaining or making references to empirical phenomena might be an useful place to start. 121 CU IDOL SELF LEARNING MATERIAL (SLM)
7. Specific Objective: Given the difficulty of conducting large-scale studies, the hypothesis' objective and method should be clear-cut. The likelihood of testing the hypothesis rises with the mapping of all activities and predictions. This not only makes it possible to describe any utilised indexes, but also to make concepts more clear. These indices serve as variables in larger-scale hypothesis testing. A specialised prognosis yields a better outcome, making it more accurate than a general prediction. 8. Relation to Available Techniques: The method used to test a hypothesis is extremely important, so careful research should be done before the experiment to determine the appropriate course of action. Regarding his well- known views, Karl Marx can be used as an example; he developed his theory by observing people, which allowed him to prove it. So, developing the appropriate technique may be essential to a test's success. 9. Relation to a Body of Theory: Social relations theories can never be formed independently; rather, they are an extension of ideas that have previously been developed or are still being developed. For instance, if a person's \"intellectual quotient\" is to be determined, certain factors like caste, ethnicity, country, etc. are picked; thus, deductions are periodically made in an effort to identify the component that affects intelligence. 8.4 SUMMARY Although significant, hypothese are not necessary for a study. It is possible to conduct a completely legitimate investigation without developing a single hypothesis. A research study needs hypotheses to be clear, explicit, and well-focused. A hypothesis is an assertion that is hypothetical and is tested through investigation. It is crucial to make a hypothesis that can be validated, is based on a body of existing information, can be operationalized, and is straightforward, specific, and conceptually clear. A research hypothesis and an alternate hypothesis are the two main categories of hypothesis. A research hypothesis can be further divided into the following categories: null hypothesis, difference hypothesis, point-prevalence hypothesis, and association hypothesis. 122 CU IDOL SELF LEARNING MATERIAL (SLM)
The use of hypotheses and the weight that is placed on them in qualitative and quantitative research are two key differences. Hypotheses are rarely employed and given very little weight in qualitative research because of the investigation's objective and the techniques used to gather data. However, its use is much more common in quantitative research, even though it varies greatly amongst academic disciplines and researchers. Overall, it may be said that when a study seeks to investigate an area where little is known, hypotheses are typically not developed; nevertheless, when a study seeks to test a claim about causation or association, confirm the occurrence of something, or establish its existence, hypotheses can be constructed. If any one of your study's design, sampling procedure, data collection method, data analysis, statistical procedures used, or conclusions obtained is flawed or improper, the testing of a hypothesis is rendered useless. Type I mistake occurs when you reject a null hypothesis when it is true and should not have been rejected, while Type II error is introduced when you accept a null hypothesis when it is false and should not have been accepted. Both of these scenarios can lead to incorrect verification of a hypothesis. 8.5 KEYWORDS No directional: As the name suggests, a non-directional alternative hypothesis doesn't suggest any direction for the expected outcomes. For example, Attending physiotherapy sessions influence the on-field performance of athletes. A null hypothesis is denoted as H0. A null hypothesis exists as opposed to an alternative hypothesis. It is a statement that defines the opposite of the expected results or outcomes throughout your research. In simpler terms, a null hypothesis is used to establish a claim that no relationship exists between the variables defined in the hypothesis. A complex hypothesis implies the relationship between multiple dependent or independent variables stated in the research problem. Follow through the below examples for better clarity on this: a. Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism. b. Including short breaks during work hours can lead to higher concentration and boost productivity. 123 CU IDOL SELF LEARNING MATERIAL (SLM)
A statement claiming an explanation after studying a sample of the population is called a statistical hypothesis. It is a type of logic-based analysis where you research a specific population and gather evidence through a particular sample size. 8.6 LEARNING ACTIVITY 1. Define Hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 2. State the functions of hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 3. Explain the characteristics of good hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 4. Explain the various types of Hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 8.7 UNIT END QUESTIONS 124 A. Descriptive Questions Short Questions: 1. What is Null hypothesis? 2. What is alternate Hypothesis? 3. List the characteristics of hypothesis. 4. What is statistical hypothesis? Long Questions: 1. Explain the characteristics of hypothesis. 2. Explain the function of hypothesis CU IDOL SELF LEARNING MATERIAL (SLM)
3. Write a note on null Hypothesis and alternate hypothesis. B. Multiple Choice Questions 1. A _______ is a hunch, assumption, suspicion, assertion, or concept regarding a phenomenon, relationship, or circumstance about which you are unsure of the reality or truth. a. hypothesis b. Sampling c. verification d. null hypothesis 4. __________ are built on reasoning. a. Hypotheses b. Sampling c. Sample d. Census 5. A hypothesis need to be ______ a. Testable b. Good c. Bad d. None of the above Answers 1-a, 2-a, 3-a, 4-a 9.8 REFERENCES References book Shukla, Satishprakash, (2020) Research Methodology and Statistics. Ahmedabad: Rishit Publications. Shukla, Satishprakash, (2014) Research – An Introduction (Gujarati) Ahmedabad: KshitiPrakashan Kothari C.R. : Research Methodology, New Age International, 2011. 125 CU IDOL SELF LEARNING MATERIAL (SLM)
Shajahan S. : Research Methods for Management, 2004. Thanulingom N : Research Methodology, Himalaya Publishing C. Rajendar Kumar : Research Methodology , APH Publishing Kumar Ranjit: Research Methodology: A Step by Step Guide for Beginners, Sage Publication, 2014 Website https://typeset.io/resources/how-to-write-research-hypothesis-definition-types- examples-and-quick-tips/ https://www.slideshare.net/aditigarg.aditigarg/research-process-14719283 https://byjus.com/physics/hypothesis/ 126 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT – 9HYPOTHESIS-II STRUCTURE 9.0 Learning Objectives 9.1 Introduction 9.2 Functions of Hypothesis 9.3 Characteristics of Good Hypothesis 9.4 Summary 9.5 Keywords 9.6 Learning Activity 9.7 Unit End Questions 9.8 References 9.0 LEARNING OBJECTIVES After studying this unit, you will be able to: Describe various types of hypothesis Identify null hypothesis and alternate hypothesis State the type 1 error and type 2 error 9.1 INTRODUCTION A specific, verifiable description of what the researcher(s) predict will happen in the study is called a hypothesis (plural: hypothese). It is stated at the study's beginning. In most cases, this entails speculating on a potential correlation between two variables: the independent variable (what the researcher modifies) and the dependent variable (what the research measures). The null hypothesis and the alternative hypothesis are the two variants of the hypothesis that are typically used in research (called the experimental hypothesis when the method of investigation is an experiment). A hypothesis must be able to be put to the test against reality and either be confirmed or disproved. The researcher makes the initial assumption that there is no difference in the populations from which they are drawn before conducting the test. The null hypothesis is understood to be this. The alternative hypothesis is another name for the research hypothesis. 127 CU IDOL SELF LEARNING MATERIAL (SLM)
One of the key components of a scientific research article is the development of a hypothesis. It is a presumption or idea based on your comprehension of the evidence that you must support with pertinent details and examples. A hypothesis describes what outcomes are anticipated from an experiment. In scientific procedures, a hypothesis serves as the basis for further investigation. You won't find it more difficult to generate a hypothesis once you completely comprehend the idea behind it and its proper structure. To be an early-stage researcher for the first time, though, can be a demanding and unpleasant undertaking. An supposition or even a tentative explanation for a certain procedure or occurrence that has been noticed during investigation constitutes a hypothesis. A hypothesis and a guess are frequently treated equally. But a hypothesis is merely an informed estimate that can be supported or refuted by research techniques. An initial research topic can be changed into a reasonable and rational prediction, or hypothesis, based on the information and data you obtain during your investigation. Every study project aims to address a particular issue. To do that, one must first identify the problem, conduct preliminary research, and then determine the solution by running numerous tests and evaluating the results. However, you must comprehend and admit what you want from the outcomes before carrying out any tests or surveys linked to the research. You are now expected to formulate your thoughtful and well-considered hypothesis into a scientific claim that you will either confirm or disprove throughout your research. A hypothesis serves as a type of knowledge development and displays your comprehension of the problem statement. The qualities and causes of the phenomenon in the research topic should thus be studied, so you must formulate your hypothesis in a way that makes it seem like a legitimate assumption. A working assertion or idea that is supported by scant data is called a hypothesis. It so makes room for additional testing and experiments. Either a truthful or false conclusion could result from the experiment. The independent variable is the two groups under comparison. One thing must be established in our minds before testing a hypothesis: \"Is there something causing something else?\" If you believe the answer to be affirmative, consider \"what is causing what.\" To further grasp this, let's use an illustration. Higher paid teachers are supposed to have a favorable attitude toward the students, whereas lower paid teachers are expected to have a negative attitude. 128 CU IDOL SELF LEARNING MATERIAL (SLM)
It is customary to refer to the researcher's prediction as the alternative hypothesis and any other result as the null hypothesis, or, more simply put, the opposite of what was anticipated. (However, the phrases are flipped if the researchers are speculating that there won't be any difference or change, speculating, for instance, that the incidence of one variable won't increase or decrease in tandem with the other.) The ability for a proposition to be shown to be incorrect, which certain schools of thought deem crucial to the scientific method, is met by the null hypothesis. Others, however, contend that testability is sufficient because it is not required to be able to imagine a scenario in which the hypothesis would be incorrect. 9.2 TYPES OF HYPOTHESIS You must fully comprehend the notion of hypotheses in order to formulate an effective hypothesis. Therefore, it's crucial that you comprehend the many kinds of hypotheses before you start writing. There are primarily just two types: alternative hypothesis and null hypothesis. 1. Alternative Hypothesis In the academic domain, it is very often denoted as H1. The significance of this kind is to identify the expected outcome of your research procedure. Additionally, it is further classified into two subcategories: e. Directional: A declaration that specifies how the anticipated results will be gathered is referred to as a directive. Instead than comparing different groups, it is typically employed when it is necessary to establish a link between different variables. For instance, athletes' performance on the field will increase if they attend physiotherapy treatments. f. No directional: A non-directional alternative hypothesis, as its name implies, offers no direction for the anticipated outcomes. For instance, athletes' performance on the field is influenced by their attendance at physiotherapy sessions. Now in the above two examples, carefully observe the two statements. The directional statement specifies that physiotherapy sessions will improve or boost performance. On the other hand, the non-directional statement helps establish a correlation between the two variables (physiotherapy sessions and performance). However, it does not emphasize whether the performance will be good or bad due to physiotherapy sessions. 129 CU IDOL SELF LEARNING MATERIAL (SLM)
2. Null Hypothesis: The symbol for a null hypothesis is H0. There is a null hypothesis rather than an alternate hypothesis. It is a claim that expresses the polar opposite of the findings or conclusions you anticipated from your investigation. To put it another way, a null hypothesis is used to prove that there is no connection between the variables listed in the hypothesis. The final example might be stated as follows to provide you with an understanding of how to formulate a null hypothesis: The physiotherapy sessions do not affect athletes' on-field performance. Both the null and alternative hypotheses are written to provide specific clarifications and examination of the research problem. So, to clarify confusion, the difference between a research problem statement and a hypothesis is that the former is just a question that can't be validated or tested. In contrast, the latter can be tested, validated, or denied. 3. A Simple Hypothesis It is a declaration that captures the relationship between the dependent and independent variables. If you follow the example, you will comprehend. Smoking is a major contributing factor to lung cancer. Consuming foods high in sugar can cause obesity. 4. Complex Hypothesis A complex hypothesis implies the relationship between multiple dependent or independent variables stated in the research problem. Follow through the below examples for better clarity on this: a. Individuals who eat more fruits tend to have higher immunity, lesser cholesterol, and high metabolism. b. Including short breaks during work hours can lead to higher concentration and boost productivity. 5. An Empirical Hypothesis Additionally known as the \"Working Hypothesis.\" When a hypothesis is confirmed through an experiment and observation, a claim of this kind is made. In this manner, the claim seems sufficiently reasonable and distinguishable from an educated guess. 130 CU IDOL SELF LEARNING MATERIAL (SLM)
Here are a few illustrations that will show you how to construct an empirical hypothesis: a. Women who take iron tablets face a lesser risk of anemia than those women who take vitamin B12. b. Canines learn faster if they are provided with food immediately after they obey a command. 6. Statistical Hypothesis A statistical hypothesis is a claim that offers an explanation after examining a sample of the population. It is a kind of logic-based study in which a certain population is studied and evidence is gathered using a defined sample size. Here are some fictitious statistical statements to help you understand how you can use statistical data in your research. a. 44% of the Indian population belong in the age group of 22-27 b. 47% of the rural population in India is involved in agro-based activities. 9.3 NULL HYPOTHESIS According to the null hypothesis, there is no correlation between the independent and dependent variables of a population. If the results support the hypothesis and there is a correlation between the two parameters, an experimental or sampling error may have occurred. The measured phenomena does, however, have a relationship if the null hypothesis is incorrect. The null hypothesis can be evaluated to determine whether or not there is a relationship between two measured phenomena, which makes it valuable. It can let the user know if the outcomes are the product of random chance or deliberate manipulation of a phenomenon. A hypothesis is tested to determine if it should be accepted or rejected with a given degree of confidence. There are two basic methods for drawing conclusions about a null hypothesis using statistics: Ronald Fisher's significance test and Jerzy Neyman and Egon Pearson's hypothesis test. According to Fisher's significance testing method, a null hypothesis is disproved if the measured data indicates that it was statistically improbable to have happened (the null hypothesis is false). As a result, the null hypothesis is disproved, and a new hypothesis is proposed. 131 CU IDOL SELF LEARNING MATERIAL (SLM)
The null hypothesis is accepted if the observed result is in accordance with its position. To draw a judgement regarding the observed data, however, Neyman and Pearson's hypothesis testing is contrasted with a competing theory. On the basis of the observed facts, the two hypotheses are distinguished. A theory with a null hypothesis is one that lacks adequate evidence to determine whether the observed data is true or not. A null hypothesis can read, for instance, \"Sunlight has no effect on the rate of plant growth.\" Plant growth in the presence of sunlight can be measured and contrasted with plant growth in the absence of sunlight to test the hypothesis. Further research to determine whether there is a relationship between the two variables can begin after the null hypothesis has been rejected. Rejecting a null hypothesis opens the door for future investigation but does not necessarily imply that the experiment did not yield the desired findings. A null hypothesis is written as H0 to distinguish it from other types of hypotheses, whereas an alternate hypothesis is written as HA or H1. A significance test is used to confirm the validity of a null hypothesis and assess if data manipulation or chance caused the observed results. By evaluating a random sample of the plants that are being grown with or without sunshine, researchers test their theory. The null hypothesis is disproved if the result shows a statistically significant change from the observed change. Null Hypothesis Example The annual return of ABC Limited bonds is assumed to be 7.5%. To test if the scenario is true or false, we take the null hypothesis to be “the mean annual return for ABC limited bond is not 7.5%.” To test the hypothesis, we first accept the null hypothesis. Any information that is against the stated null hypothesis is taken to be the alternative hypothesis for the purpose of testing the hypotheses. In such a case, the alternative hypothesis is “the mean annual return of ABC Limited is 7.5%.” We take samples of the annual returns of the bond for the last five years to calculate the sample mean for the previous five years. The result is then compared to the assumed annual return average of 7.5% to test the null hypothesis. The average annual returns for the five- year period are 7.5%; the null hypothesis is rejected. Consequently, the alternative hypothesis is accepted. 132 CU IDOL SELF LEARNING MATERIAL (SLM)
9.4 ALTERNATIVE HYPOTHESIS An alternative hypothesis is a theory that conflicts with the null hypothesis and takes a different position from the null. For instance, the alternative hypothesis would predict something to be untrue if the null hypothesis estimated it to be true. When attempting to refute the null hypothesis, the statement you test is frequently the alternative hypothesis. The alternative hypothesis will take the place of the null hypothesis if you can collect enough evidence to support it. A statement you attempt to refute is referred to as a null hypothesis, which presupposes that something is true. A null hypothesis can state, for instance, that the weather in a certain city has no bearing on crime rates. The alternative hypothesis contends that this is untrue and that weather has an influence on crime rates, either positively or negatively. This means you utilise the alternative to collect data to support the experiment since you aim to disprove the null hypothesis and the alternative assumes the null is incorrect. The alternative hypothesis only evaluates one direction in a one-tailed directional test. For instance, the test cannot determine whether the differences are higher than or less than zero simultaneously. The test is referred to as left-tailed if the researcher believes the difference is less. The test is referred to as right-tailed if the researcher contends that the difference is bigger than zero. The leaders of a corporation wish to evaluate their interviewing procedure. They think that only applicants with prior job experience are invited to the company's interviews for available positions. They formulate the supposition that candidates for employment who have at least four years of experience are more likely to be interviewed. An illustration of a null hypothesis is this. Additionally, the researcher is positing a one-sided hypothesis, i.e., that the number will be bigger than zero and that the data would indicate a trend in that direction. They then develop a competing theory. The converse is also true, according to a competing theory: \"Employee candidates with no work experience receive as many invitations to interviews as those with at least four years.\" Here, the researcher creates a test to find out how many years of experience interview candidates have. They determine the research's criteria and do their best to disprove the initial assertion. They assert that they cannot reject the null hypothesis if research does not establish the alternative hypothesis to be accurate. They can come to the conclusion that it can be rejected if there is sufficient evidence to do so. 133 CU IDOL SELF LEARNING MATERIAL (SLM)
The alternative hypothesis asserts that its parameters do not match the null hypothesis value in a two-tailed or non-directional test. The two-tailed directional test thereby asserts the presence of differences bigger and less than the null value. It's vital to notice that it just asserts that there is a difference between the null and alternative hypotheses; it gives no indication of which way the difference lies. High school pupils take a test in a sophisticated classroom. According to a school researcher, the additional instruction will result in classroom average grades that are higher than the state average. The null hypothesis they formulate reads, \"The average test grades in the advanced classroom are higher than the state average of 1,000 points.\" The researcher also develops a competing hypothesis, according to which test results are unrelated and the advanced learning programme has minimal impact on students' grades. Because the researcher is attempting to determine if the scores are greater or lower than the state average, this is an example of a two-sided hypothesis. After that, they try to confirm or disprove the initial null hypothesis. Example 1: It’s an accepted fact that ethanol boils at 173.1°F; you have a theory that ethanol actually has a different boiling point, of over 174°F. The accepted fact (“ethanol boils at 173.1°F”) is the null hypothesis; your theory (“ethanol boils at temperatures of 174°F”) is the alternate hypothesis. Example 2: A classroom full of students at a certain elementary school is performing at lower than average levels on standardized tests. The low test scores are thought to be due to poor teacher performance. However, you have a theory that the students are performing poorly because their classroom is not as well ventilated as the other classrooms in the school. The accepted theory (“low test scores are due to poor teacher performance”) is the null hypothesis; your theory (“low test scores are due to inadequate ventilation in the classroom”) is the alternative hypothesis. The Null Hypothesis And Alternative Hypothese Are Different A null hypothesis and an alternative hypothesis differ in the following ways: The alternative hypothesis is typically abbreviated as H1, while the null hypothesis is frequently abbreviated as H0.An alternative hypothesis makes a claim that suggests or recommends a prospective conclusion or an outcome that an investigator may expect, whereas a null hypothesis says the exact opposite of what an investigator predicts or expects. 134 CU IDOL SELF LEARNING MATERIAL (SLM)
The essential idea behind a null hypothesis is that there is no precise or real link between the variables. It is incompatible with a different hypothesis. 9.5 TYPE 1 AND TYPE 2 ERROR When a researcher mistakenly rejects a true null hypothesis, this is known as a type 1 error, also referred to as a false positive. This indicates that even though your results were accidental, you nevertheless declare them as noteworthy. Your alpha level (α), which is the p-value below which you reject the null hypothesis, is a measure of the likelihood of committing a type I error. When you reject the null hypothesis, you are willing to accept a 5% probability that you are mistaken, according to a p-value of 0.05. Use a lower number for p to lessen your chance of making a type I error. A p-value of 0.01, for instance, would indicate that there is a 1% probability of making a Type I error. However, if you use a lower number for alpha, you won't be as likely to spot a genuine difference, should one exist (thus risking a type II error). When a researcher fails to reject a null hypothesis that is actually wrong, this is referred to as a type II error or a false negative. Here, a researcher comes to the false conclusion that there is no substantial influence. The power of the statistical test (power = 1- β ) is connected to the likelihood of making a type II error, or Beta. By making sure your test has sufficient power, you can lower your likelihood of making a type II error. Making a type I error has the unintended result of wasting time, money, etc. by making adjustments or interventions that are not essential. When change is required, type II errors frequently result in the maintenance of the status quo (i.e., the same interventions). To achieve this, make sure your sample size is sufficient to identify a real difference in practice. A false positive conclusion in statistics is known as a Type I error, and a false negative conclusion is known as a Type II error. The possibility of making these mistakes is inherent in hypothesis testing because making a statistical choice always involves some level of uncertainty. 135 CU IDOL SELF LEARNING MATERIAL (SLM)
The significance level, or alpha (α), determines the likelihood of a Type I error, whereas beta (β) determines the likelihood of a Type II error. These dangers can be reduced by carefully designing the layout of your study. We’ll start off using a sample size of 100 and .4 to .6 boundary lines to make a 95% confidence interval for testing coins. Any coin whose proportion of heads lies outside the interval we’ll declare unfair. Only 5% of the time will a fair coin mislead us and lie outside the interval, leading us to erroneously declare it unfair. This is Type I Error. What about unfair coins that mislead us and lie inside the interval? That will lead us to erroneously declare them fair. This is Type II Error. Think of it as if I handed you a basket full of coins. Unknown numbers of fair coins and unfair coins are present in the basket. You must flip two arbitrary coins 100 times each to test them. You'll base your decision on the 95% confidence interval: You will evaluate the coin to be fair if the number of heads falls between the range of.4 and.6, and you will judge the coin to be unfair if the number of heads falls outside of that range. The four outcomes with fair or unfair coins that fall inside or outside the 95% confidence interval for the coin's fairness are highlighted in Figure. Graph No 9.1 Sample Proportion Heads Suppose you choose a coin, flip it 100 times, and the outcome is 55 heads. Since that is inside the range, you determine that the coin is fair. If the coin is indeed fair, the outcome is veridical (true) and helps you to the right conclusion. If the coin is unfair, the outcome is deceptive and causes you to commit a Type II Error. 136 CU IDOL SELF LEARNING MATERIAL (SLM)
Let's imagine you choose a different coin and flip it 100 times, getting 65 heads as a result. You consider the coin to be unfair because that is outside of the interval. If the coin is unfair,thoutcome is veridical (true) and helps you to the right conclusion.If in fact the coin is fair, then the result is misleading and leads you to make a Type I Error. Graph no 9.2 result Greek letters alpha (α) and beta (β) are frequently used to denote Type I and Type II errors, respectively. You actually decide how much you want to risk making a Type I error by rejecting the null hypothesis when it is actually true when you choose a degree of probability for a test. Because it shows the likelihood of making a Type I error, the area in the zone of rejection is frequently referred to as the alpha level. It is important to picture a second distribution for the genuine alternative next to the distribution for the null hypothesis in order to visualise a Type II, or, mistake. The area of the curve for the alternative (true) hypothesis lying to the left of the critical value represents the proportion of times you will have made a Type II error if the alternative hypothesis is in fact true but you fail to reject the null hypothesis for all values of the test statistic falling to the left of the critical value. Errors of Type I and Type II are negatively correlated; as one rises, the other falls. The researcher often predetermines the Type I, or α (alpha), error rate. Because it entails calculating the distribution of the alternative hypothesis, which is frequently unknown, it is more difficult to determine the Type II error rate for a particular test. Power is a related idea; it is the likelihood that a test will reject the null hypothesis even 137 CU IDOL SELF LEARNING MATERIAL (SLM)
Table No 9.1 Statistic Result is untrue. Figure 1 illustrates that power is equal to one minus the Type II error rate (β). Powerful output is desired. Power might be tricky to evaluate precisely, like, but growing the sample size always boosts power. 9.6 SUMMARY The null hypothesis and the alternative hypothesis are the two variants of the hypothesis that are typically used in research (called the experimental hypothesis when the method of investigation is an experiment). A non-directional alternative hypothesis, as its name implies, offers no direction for the anticipated outcomes. For instance, athletes' performance on the field is influenced by their attendance at physiotherapy sessions A hypothesis describes what outcomes are anticipated from an experiment. In scientific procedures, a hypothesis serves as the basis for further investigation. You won't find it more difficult to generate a hypothesis once you completely comprehend the idea behind it and its proper structure. To be an early-stage researcher for the first time, though, can be a demanding and unpleasant undertaking. The symbol for a null hypothesis is H0. There is a null hypothesis rather than an alternate hypothesis. It is a claim that expresses the polar opposite of the findings or conclusions you anticipated from your investigation. To put it another way, a null hypothesis is used to prove that there is no connection between the variables listed in the hypothesis. A statistical hypothesis is a claim that offers an explanation after examining a sample of the population. It is a kind of logic-based study in which a certain population is studied and evidence is gathered using a defined sample size. 9.7 KEYWORDS A hypothesis is an assumption that is made based on some evidence. This is the initial point of any investigation that translates the research questions into predictions. It includes components like variables, population and the relation between the 138 CU IDOL SELF LEARNING MATERIAL (SLM)
variables. A research hypothesis is a hypothesis that is used to test the relationship between two or more variables. Simple Hypothesis: It shows a relationship between one dependent variable and a single independent variable. For example – If you eat more vegetables, you will lose weight faster. Here, eating more vegetables is an independent variable, while losing weight is the dependent variable Complex HypothesisIt shows the relationship between two or more dependent variables and two or more independent variables. Eating more vegetables and fruits leads to weight loss, glowing skin, and reduces the risk of many diseases such as heart disease. Null HypothesisIt provides a statement which is contrary to the hypothesis. It’s a negative statement, and there is no relationship between independent and dependent variables. The symbol is denoted by “HO”. Directional HypothesisIt shows how a researcher is intellectual and committed to a particular outcome. The relationship between the variables can also predict its nature. For example- children aged four years eating proper food over a five-year period are having higher IQ levels than children not having a proper meal. This shows the effect and direction of the effect. 9.8 LEARNING ACTIVITY 1. Define Null Hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 2. State the various types of hypothesis ___________________________________________________________________________ ___________________________________________________________________________ 3. what is alternate hypothesis? ___________________________________________________________________________ ___________________________________________________________________________ 4. What is type 1 error? 139 CU IDOL SELF LEARNING MATERIAL (SLM)
___________________________________________________________________________ ___________________________________________________________________________ 5. What is type 2 error? ___________________________________________________________________________ ___________________________________________________________________________ 9.9 UNIT END QUESTIONS A. Descriptive Questions Short Questions: 1. What is directional hypothesis? 2. What is No directional hypothesis? 3. What is Type 1 error? 4. What is type 2 error? 5. What is null Hypothesis? Long Questions: 1. Explain various types of Hypothesis. 2. What is type 1 and type 2 error? 3. What is null hypothesis and alternate hypothesis? 4. What is significance level? B. Multiple Choice Questions 1. An _______ hypothesis is a theory that conflicts with the null hypothesis and takes a different position from the null a. alternative b. Null c. Type 1 error d. directional 2. The _____ hypothesis can be evaluated to determine whether or not there is a relationship between two measured phenomena, which makes it valuable. a. null 140 CU IDOL SELF LEARNING MATERIAL (SLM)
b. alternate c. stratified d. non directional 3. A false positive conclusion in statistics is known as a _______ error a. Type I b. B. type II c. No error d. 1 error 4. The _________, or alpha (α), determines the likelihood of a Type I error a. Alpha level b. significance level c. Null Hypothesis b. Alternate error Answers 1-a, 2-a, 3-a, 4-b 9.10 REFERENCES References book Shukla, Satishprakash, (2020) Research Methodology and Statistics. Ahmedabad: Rishit Publications. Shukla, Satishprakash, (2014) Research – An Introduction (Gujarati) Ahmedabad: KshitiPrakashan Kothari C.R. : Research Methodology, New Age International, 2011. Shajahan S. : Research Methods for Management, 2004. Thanulingom N : Research Methodology, Himalaya Publishing C. Rajendar Kumar : Research Methodology , APH Publishing Kumar Ranjit: Research Methodology: A Step by Step Guide for Beginners, Sage Publication, 2014 Website https://byjus.com/physics/hypothesis/ https://corporatefinanceinstitute.com/resources/knowledge/other/sampling-errors/ 141 CU IDOL SELF LEARNING MATERIAL (SLM)
https://typeset.io/resources/how-to-write-research-hypothesis-definition-types- examples-and-quick-tips/ 142 CU IDOL SELF LEARNING MATERIAL (SLM)
UNIT – 10LEVELS OF MEASUREMENT STRUCTURE 10.0Learning Objectives 10.1Introduction 10.2Levels of Management 10.3Characteristics of Good Hypothesis 10.3.1 Nominal Scale 10.3.2 Ordinal Scale 10.3.3 Interval scale 10.3.4 Ratio scale 10.4Summary 10.5Keywords 10.6Learning Activity 10.7Unit End Questions 10.8References 10.0 LEARNING OBJECTIVES After studying this unit, you will be able to: Describe various scale of measurement Identify features of various scale of measurement State the interval and ratio scale 10.1 INTRODUCTION It's crucial to first comprehend variables and what should be measured using them before performing statistical analysis on data. Statistics employs various levels of measurement, and the data generated by them can be broadly divided into qualitative and quantitative data. Let's first define what a variable is. Variables are things that can be measured and whose value varies among populations. Think about a sample of people who are employed, for instance. This group of people's characteristics can include things like industry, location, gender, age, skills, job type, paid time off, etc. Every employee highlight will have a different value for the factors. 143 CU IDOL SELF LEARNING MATERIAL (SLM)
For instance, figuring out the typical hourly wage of a worker in the US is next to impossible. In order for the sample audience to accurately represent the greater population, it is chosen at random. Next, the sample audience's average hourly rate is determined. You can get the average hourly wage of a bigger population using statistical testing. The sort of statistical test to be employed is determined by the level of measurement of a variable. The degree of measurement is defined as the mathematical properties of a variable, or, put another way, how a variable is measured. There are several characteristics of that variable. Let's assume that the terms \"republican,\" \"democrat,\" and \"independent\" are the only ones that matter in this particular election setting. We arbitrary assign the values 1, 2, and 3 to the three qualities in order to analyze the outcomes of this variable. The relationship between these three variables is described by the level of measurement. In this instance, we are merely substituting shorter integers for longer text phrases. We don't automatically think that larger numbers indicate \"more\" and lower ones indicate \"less\" of something. We don't take the value of 2 to suggest that something about Democrats is twice what about Republicans. Just because Republicans have value doesn't mean they are in first place or have the highest priority. First, knowing the level of measurement helps you decide how to interpret the data from that variable. When you know that a measure is nominal (like the one just described), then you know that the numerical values are just short codes for the longer names. Second, knowing the level of measurement helps you decide what statistical analysis is appropriate on the values that were assigned. If a measure is nominal, then you know that you would never average the data values or do a t-test on the data. The level at which you measure a variable determines how you can analyze your data. The different levels limit which descriptive statistics you can use to get an overall summary of your data, and which type of inferential statistics you can perform on your data to support or refute your hypothesis. In many cases, your variables can be measured at different levels, so you have to choose the level of measurement you will use before data collection begins. Level of measurement is a classification used in statistics that links the values given to variables with one another. In other words, the information contained in the values is described using the level of measurement. Stanley Smith, a psychologist, is credited with creating the nominal, ordinal, interval, and ratio levels of measurement. 144 CU IDOL SELF LEARNING MATERIAL (SLM)
10.2 LEVELS OF MEASUREMENT The framework that we seek to put everything into was created by us, but it wasn't created randomly; rather, it was created using measurements, which allowed us to incorporate the facts without changing their core characteristics. Measurement is the foundation of any research. The most important distinction between qualitative and quantitative research studies is in the forms of measurement employed to get information from the respondents, in addition to the ideologies and philosophical foundations of each mode of inquiry. In contrast to quantitative research, which often seeks answers on one of the measuring scales, qualitative research typically uses descriptive statements to do so (nominal, ordinal, interval or ratio). If one of the scales is not used to acquire a piece of information during data collection, it is converted into a variable during analysis by utilizing one of the scales. These scales allow for the use of either qualitative categories or a precise unit of measurement for measurement. Scales with a measurement unit (such as interval and ratio) are seen as being more sophisticated, objective, and accurate. However, because nominal and ordinal scales lack a true unit of measurement, they are seen as subjective and therefore less accurate. Other factors being equal, the confidence in other people's conclusions increases with the degree of refinement in the unit of measurement of a variable. The units of measurement used and the weight given to them are two key distinctions between the physical and social sciences. In contrast to the social sciences, where measurements can range from the highly subjective to the highly measurable, the physical sciences require measurements to be completely accurate and precise. The emphasis placed on accuracy in measurement differs significantly among the social sciences. An economist or an epidemiologist places more emphasis on \"objective\" measurement, whereas anthropologists typically utilize relatively \"subjective\" units of measurement. However, Bailey considers that ‘S S Stevens constructed a widely adopted classification of levels of measurement’ (1978: 52). As this book is written for the beginner and as Stevens’s classification is simpler, it is this that is used for discussion in this chapter. Stevens has classified the different types of measurement scale into four categories: • nominal or classificatory scale; 145 CU IDOL SELF LEARNING MATERIAL (SLM)
• ordinal or ranking scale; • interval scale; • ratio scale. 10.2.1 The nominal or classificatory scale A nominal scale makes it possible to group people, things, or answers based on a shared or common trait or characteristic. These individuals, things, or responses are broken up into a number of subgroups, each of which has a unique attribute. Depending on the degree of variance, a variable measured on a nominal scale may have one, two, or more subcategories. For instance, the variables \"water\" and \"taxi\" only contain one subgroup each, whereas the variable \"gender\" has both a male and a female subgroup. Labor, Liberal, Democrats, and Greens are the four primary divisions of political parties in Australia. People who identify as Labor Party members or supporters are categorized as \"Labor,\" while people who identify as Liberals are categorized as \"Liberals,\" and so on. The name chosen for a subcategory is hypothetical, however it is advisable to use something that describes the feature of the subclass for successful communication. Nominal scale, also known as the category variable scale, is a scale without a numerical value or order that is used to categorize variables into discrete groups. Of the four variable measuring scales, this one is the most straightforward. Since the alternatives have no numerical value, calculations on these variables are useless. The numbers corresponding to the variables on this scale are solely used as labels for categorization or division in situations where this scale is utilized for classification. Because these values have no quantitative significance, any calculations based on them will be useless. Using a nominal scale to classify assures that members of the same subgroup share a common trait or quality that serves as the classification's basis. There is no relationship between the subgroups, thus it doesn't matter in what order they are listed. For a question such as: Where do you live? 1- Suburbs 146 CU IDOL SELF LEARNING MATERIAL (SLM)
2- City 3- Town Nominal scale is often used in research surveys and questionnaires where only variable labels hold significance. For instance, a customer survey asking “Which brand of smartphones do you prefer?” Options : “Apple”- 1 , “Samsung”-2, “OnePlus”-3. In this survey question, only the names of the brands are significant for the researcher conducting consumer research or netnography. There is no need for any specific order for these brands. However, while capturing nominal data, researchers conduct analysis based on the associated labels. In the above example, when a survey respondent selects Apple as their preferred brand, the data entered and associated will be “1”. This helped in quantifying and answering the final question – How many respondents selected Apple, how many selected Samsung, and how many went for OnePlus – and which one is the highest. Nominal Scale Examples Gender Political preferences Place of residence 10.2.2 The ordinal or ranking scale An ordinal scale is a variable measurement scale that is used to show the order of variables rather than the differences between them. In general, these scales are used to represent non- mathematical concepts like frequency, pleasure, happiness, level of discomfort, etc. Since \"Ordinal\" and \"Order,\" which is what this scale is used for, sound similar to one another, it is easy to remember how to use it. The distance between variables cannot be measured since the ordinal scale lacks an origin of scale while maintaining descriptional properties and an inherent order. In addition to having a relative location of variables, the ordinal scale also contains tagging characteristics that are similar to those of the nominal scale, according to descriptive features. There is no origin for this scale, hence there is no \"real zero\" or definite beginning. Analysis and Ordinary Data 147 CU IDOL SELF LEARNING MATERIAL (SLM)
Ordinal scale data can be shown in tabular or graphical ways, allowing a researcher to easily analyze the data they have gathered. Additionally, techniques like the Kruskal-Wallis H test and the Mann-Whitney U test can be used to evaluate ordinal data. Typically, these techniques are used to compare two or more ordinal categories. Researchers can determine which variable from one group is larger or smaller than another variable from a randomly selected group using the Mann-Whitney U test. The Kruskal-Wallis H test, however, allows researchers to determine whether or not two or more ordinal groups have the same median. Ordinal Scale Examples The ordinal scale is most frequently used to describe workplace status, tournament team standings, product quality, and agreement or satisfaction levels. In market research, these scales are typically used to collect and assess comparative input about product satisfaction, shifting perceptions as a result of product updates, etc. For example, a semantic differential scale question such as: How satisfied are you with our services? Very Unsatisfied – 1 Unsatisfied – 2 Neutral – 3 Satisfied – 4 Very Satisfied – 5 Here, the order of variables is of prime importance and so is the labeling. Very unsatisfied will always be worse than unsatisfied and satisfied will be worse than very satisfied. This is where ordinal scale is a step above nominal scale – the order is relevant to the results and so is their naming. Analyzing results based on the order along with the name becomes a convenient process for the researcher. If they intend to obtain more information than what they would collect using a nominal scale, they can use the ordinal scale. 148 CU IDOL SELF LEARNING MATERIAL (SLM)
10.2.3 The interval scale An interval scale is a numerical scale in which both the order and the difference between the variables are known. Using the Interval scale, variables with recognizable, consistent, and calculable differences are categorized. It is simple to recall this scale's principal function as well; the word \"Interval\" stands for \"distance between two entities,\" which is what the interval scale aids in achieving. These scales are useful because they enable the statistical examination of the supplied data. The central tendency of this scale can be calculated using the mean, median, or mode. The absence of a true zero value or a predetermined starting point is the only disadvantage of this scale. The interval scale allows a calculation of the difference between variables in addition to having all the characteristics of the ordinal scale. The equidistant distance between objects is the scale's primary property. For instance, consider a Celsius/Fahrenheit temperature scale – 80 degrees is always higher than 50 degrees and the difference between these two temperatures is the same as the difference between 70 degrees and 40 degrees. Also, the value of 0 is arbitrary because negative values of temperature do exist – which makes the Celsius/Fahrenheit temperature scale a classic example of an interval scale. When a mandated difference between variables exists in a research study and cannot be met using a nominal or ordinal scale, interval scale is frequently utilized. The other two scales can only associate qualitative values with variables, however the interval scale quantifies the difference between two variables. In contrast to the previous two scales, the mean and median values of an ordinal scale can be examined. Since variables may be given a numerical value and calculations can be made using those values, interval scale is widely employed in statistics. Even if interval scales are amazing, they do not calculate the “true zero” value which is why the next scale comes into the picture. Interval Scale Examples 149 CU IDOL SELF LEARNING MATERIAL (SLM)
There are situations where attitude scales are considered to be interval scales. Apart from the temperature scale, time is also a very common example of an interval scale as the values are already established, constant, and measurable. Calendar years and time also fall under this category of measurement scales. Likert scale, Net Promoter Score, Semantic Differential Scale, Bipolar Matrix Table, etc. are the most-used interval scale examples. The following questions fall under the Interval Scale category: What is your family income? What is the temperature in your city? 10.2.4 The ratio scale All the characteristics of nominal, ordinal, and interval scales are present in a ratio scale, which also has a fixed beginning point of zero. Because the difference between the intervals is always measured from a zero point, it is an absolute scale. This means that calculations can be performed using the ratio scale. Examples of this scale in use include the measuring of income, age, height, and weight. A ratio scale is a type of variable measurement scale that, in addition to producing the order of the variables, also makes the difference between the variables and the value of true zero known. It is calculated under the assumptions that the variables have a zero option, that the difference between the two variables is the same, and that the alternatives are arranged in a particular sequence. Different inferential and descriptive analytic methods can be used on the variables if true zero is an option. The ratio scale can establish the value of absolute zero in addition to performing all of the functions that a nominal, ordinal, and interval scale can. Height and weight are the ideal ratio scales. A ratio scale is used in market research to figure out things like market share, annual sales, the cost of a new product, the number of customers, etc. Since researchers and statisticians can compute the central tendency using statistical procedures like mean, median, and mode, as well as methods like geometric mean, the coefficient of variation, or harmonic mean on this scale, ratio scale provides the most thorough information. 150 CU IDOL SELF LEARNING MATERIAL (SLM)
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