AIOU Solved Assignment 2 Code 8604 Spring 2020

AIOU Solved Assignments code B.ed 8604 Spring 2020 Assignment  2  Course: Research Methods in Education (8604) Spring 2020. AIOU past papers

Research Methods in Education (8604) B.ed 1.5 Years
Spring, 2020

AIOU Solved Assignment 2 Code 8604 Spring 2020

Q.1   Discuss in detail the validity and reliability of research tools for research. Develop a questionnaire for curriculum developer to explore the opinion about “existing curriculum for secondary level in public school as the tool for socio-economic development”. Mention the process to check the validity and reliability of this tool.                     (20)  

In many fields of science, accepted theories and models cannot, by their very nature, be quantified or observed (i.e. lacking empirical evidence). Many of these theories are also in dispute within the field.   The best example lies with quantum mechanics. Many facets of quantum mechanics are merely mathematical models explaining the behavior and interaction between subatomic particles. One of the major stabling blocks in quantum mechanics lies within one its’ fundamental theories: Quantum superposition (all particles exist in all states at the same time). There are many interpretations of this, the standard being: Copenhagen interpretation. This states, basically, that the act of measuring (or observing) the state of a particle collapses the superposition effect, altering it’s state to the value defined by the measurement. This shows that the superposition effect, while being one of the most widely accepted and fundamental principles of quantum mechanics, can never actually be observed.   Another good example for this is in the field of Paleontology. One of the fundamental pillars used for the reconstruction of fossils, especially the picturisation of what the animal looked and behaved like, is that we must use what we see today as our basis for rebuilding the past.   Example   Take the example of the Megalodon. From only a handful of teeth and vertebre, paleontologists can tell us that the Megalodon was, basically, a 20m long Great White with similar structure and behavioural patterns. The teeth of a megalodon are similar in shape to that of a Great White so it has been assumed that it’s morphology and behaviour are similar (although because the teeth are larger, it’s prey would be larger). While this may be accurate, it may also be completely wrong (there is strong support for the theory that megalodon and great whites are not related, the latter being a descendant of the broad-tooth Mako shark. The only real clue that we have towards the size and behaviour is that many bones of large whales have been found with tooth marks almost identical to that of the megalodon. However, there is no evidence, other than it’s similarity to the great white’s, that the teeth and vertebre even came from a shark and not some other animal which happened to have similar dentition and spinal structure.   In other words, the evidence and modeling that we have in these cases, while compelling, is insufficient to enable us to reliably prove the conclusions that we have made. Or, probably more accurately, the conclusions drawn cannot be reliably disproven with current methodology and technology.   Some limitations of the present scientific approach are imposed, as it were, from the ‘outside’. They are a result of materialist beliefs not science itself.   Determinism   it is fair to say that determinism is not something that only materialists adhere to. There is a long history of this belief that includes thinkers from very different perspectives. Materialism has only defined determinism in terms of the natural laws. This not only precludes the possibility of purposeful causes, but also of choice and of creativity. Ironically, modern science itself has come to the conclusion that determinism does not fully reflect reality, and yet many, especially human science disciplines, are reluctant to give it up (most psychology text-books, for example, still recognize nature and nurture as the only factors that affect human behaviour).   Reductionism – one of the most stubborn beliefs of modern science is that complex phenomena can always be reduced to simpler, more fundamental ones and the laws that govern them. Mind can be reduced to biology, biology to chemistry, chemistry to physics. This is the essence of reductionism, adopted in the 19th century. However, this belief appears to be a dead-end even on the most basic level. It is already recognised that, for example, ‘the macroscopic behaviour of a large ensemble of particles cannot be deduced from the properties of the individual particles themselves. Many eminent scientists are ready to admit the improbability of reductionism.   Insisting on material evidence – a position that would always insist on material evidence, and automatically dismiss an argument that is not based on observable data is somewhat naïve. Even hardcore science inevitably operates with phenomena or principles for which material evidence does not exist (e.g. time or causality) or is based on stipulations that cannot be empirically verified (such as the ones linked to the theory of relativity). Also, many scientific concepts (gravitation being one example) cannot be known directly but only through their effects.   Inertia of science   The inertia of science has been criticised by a number of scholars (Kuhn, Feyerabend and Lakatos being probably the best known). It transpires in a rigid, absolutistic demand to adhere to certain views and self-imposed methods and criteria. Physicist Max Planck allegedly said that a new scientific truth does not triumph by convincing its opponents, but rather because its opponents die, and a new generation grows up that is familiar with it. This stifles rather than advances human knowledge. As Chalmers points out, ‘we cannot legitimately defend or reject items of knowledge because they do or do not conform to some ready-made criterion of scientificity’ (1980, p.169). The best scientists have always been on the front lines, prepared to sacrifice their pre-assumptions for the sake of better understanding. However, there is another, inevitably larger group of scientists that prefer to maintain the status quo. Science writer Horgan comments that ‘the scientific culture was once much smaller and therefore more susceptible to rapid change. Now it has become a vast intellectual, social, and political bureaucracy, with inertia to match. Both of these groups, progressive and conservative, may be necessary, the former to prevent the solidification of science, and the latter to prevent chaos. The problem is that the conservative stream often supports and perpetuates particular ideological views in order to maintain a special status and social power. The suspicion is that some scientists are more interested in advancing their careers than knowledge. Chalmers claims that ‘[ideology of science] involves the use of the dubious concept of science and the equally dubious concept of truth that is often associated with it, usually in the defence of conservative positions’ .   Bias – those phenomena to which the established scientific method can be applied are studied in greater and greater detail, often without any reference to a larger picture; whereas those to which it cannot be are ignored or are declared illusionary. The Oxford Companion to the Mind, for example, has entries such as ‘Frankenstein’ but not ‘will’. The consequence of such an attitude is a distorted and impoverished picture of reality. Even if some phenomena or events cannot be explained, they need to be taken into account and acknowledged:   Objectivism has totally falsified our conception of truth, by exalting what we can know and prove, while covering up with ambiguous utterances all that we know and cannot prove, even though the latter knowledge underlies, and must ultimately set its seal to, all that we can prove. (Polanyi, 1958, p.286)   Limitations of science as a social practice   Besides the above ideological limitations there are other self-imposed limitations to present day science that are the result of the social milieu within which it operates.   Specialisation is such an instance. The best specialisation can provide is a fragmented picture on reality, which leaves out the possibility of an overall, synthetic view. This can lead to ‘not seeing the wood for the trees’, and can have highly undesirable consequences. James Burke, a scientist himself, concludes that ‘the reductionist approach, forcing people to be specialists, has got us into the mess we are in. The one who looks through a microscope all the time may not notice an elephant standing next to shim. Historian Zeldin proclaims:   Around the beginning of the eighteenth century… the ideal of encyclopaedic knowledge was replaced by specialisation. Withdrawal into a fortress of limited knowledge meant one could defend oneself on one’s home ground; it gave one self-confidence of a limited kind… Now that the silences produced by specialisation have become deafening, and now that information fills the air as never before, it is possible to reconsider the choice, to ask whether many people might not be better off if they began looking again for the road which leads beyond specialisation, if they tried seeing the universe as a whole.   Public and repeatable is still prevailing   The insistence on observable, public and repeatable is still prevailing, although there are certain phenomena (in cosmology and the realm of sub-atomic particles, as much as in studying life and mind) that cannot satisfy these requirements. Any attempts to fit them within these criteria severely impoverish their understanding. The very existence of atoms was derided as metaphysical nonsense until barely a century ago. Leading scientists argued that it made no sense to talk of entities that could never be observed, which drove one of the most talented scientists at that time, Boltzmann, to suicide. His struggles against the scientific orthodoxy illustrate the dangers of allowing such a dogmatism to seep into the quest for knowledge, especially in the fields of human and social science (the mind is neither observable, nor public, nor repeatable).   Authoritarianism – to secure their special status, priests used to perpetuate a belief that their vocation made them somehow closer to God, so the best way for ordinary people to relate and be informed about spiritual matters was through them. Scientists nowadays acquire a similar aura of authority. The impression is that they are experts above others (fostered not necessarily by scientists themselves, of which some, in all fairness, are trying to break out of such an image). It surfaces in frequently heard statements in the media such as ‘scientists claim that…’, without saying who these scientists are and what these claims are based on. This makes science not only vulnerable to manipulation, but also alienates it from ordinary people.   Scientific detachment was introduced to ensure a higher level of objectivity and is often justified (e.g. to enable independent verification). However, it is sometimes taken so far that it becomes an obstacle and, in fact, leads to bias through the back door.   Intrinsic limitations   The above ideological and historical limitations are contingent, and should not be taken as detrimental. After all, they can be overcome in the future. However, there are some limitations of science that can never be surpassed, which is why the scientific approach cannot be sufficient on its own and needs to be combined with other approaches. Dealing with complexity – scientific method is essentially analytic, which enables the simplification and generalization of some phenomena. Yet, reality is complex, and if that complexity is disregarded, some important qualities can be missed. One of the world’s most distinguished quantum physicists and a philosopher, Werner Heisenberg, warned: ‘…the scientific concepts are idealizations… But through this process of idealization and precise definition immediate connection with reality is lost’ (1958, p.200). More heuristic methods are better suited to deal with complex systems. Human beings could not operate in the world if they only relied on science and excluded the common sense that is capable of intuitively grasping this complexity. Psychologists, for example, are not yet nearly able to provide the profound insights about the human psyche that can be found in the works of narrative writers such as Shakespeare, Dickens or Tolstoy.   Incompleteness   There are certain phenomena or questions that are beyond the reach of science. For instance, one of the dogmas of the present scientific ideology is that all the processes in nature are governed by physical laws. However, science seems at loss to explain where these laws come from. It is not only a question of why there is this set of laws rather than any other, but more fundamentally, why there are laws at all, why the universe is orderly, rather than chaotic and disorderly. Physicist Paul Davies speculates that attaining full knowledge through science is unlikely, given the limits imposed by quantum indeterminacy, Gödel’s theorem, chaos theory and the like. Mystical experience might provide the only avenue to absolute truth, he concludes.   A lack of criteria for interpreting facts – Henri Poincaré, one of the greatest mathematicians and physicists in the 19th century, wrote: ‘Just as houses are made of stones, so is science made of facts; but a pile of stones is not a house and a collection of facts is not necessarily science’.  What sort of structure is created depends on the way scientists play with or interpret facts. Interpretations are important. Human understanding would be very limited if it was based only on descriptive statements. The laws do not have much explanatory power; they leave many questions unanswered. However, interpretations are not obvious, they are extrapolations that necessarily involve mental operations, not solely based on observations. So, many observable facts can give rise to a number of different interpretations, of which some may not be accurate even if the facts behind them are. A different set of criteria is needed for interpretations than for observations, but scientific method does not provide them. This is why it is easy to highjack scientific findings and present one’s interpretations as scientific truths.

AIOU Solved Assignment 2 Code 8604 Spring 2020 

Q.2   Discuss in detail the population and different sampling techniques used in educational research.                                                                                                           (20)  

Real Incest Brooke is a psychologist who is interested in studying how much stress college students face during finals. She works at a university, so she is planning to send out a survey around finals time and ask some students to rank on a scale of 1 to 5 how stressed out they are.   But which students should she survey? All of the students at the university? Only the students in the psychology department? Only freshmen? There are a lot of possibilities for Brooke’s sample. The sample of a study is simply the participants in a study. In Brooke’s case, her sample will be the students who fill out her survey.   Sampling is the process whereby a researcher chooses her sample. This might seem pretty straightforward: just get some people together, right? But how does Brooke do that? Should she just stand on a corner and start asking people to take her survey? Should she send out an email to every college student in the world? Where does she even begin?   Because sampling isn’t as straightforward as it initially seems, there is a set process to help researchers choose a good sample. Let’s look closer at the process and importance of sampling.   Process   So Brooke wants to choose a group of college students to take part in her study. To select her sample, she goes through the basic steps of sampling.  

  1. Identify the population of interest. A population is the group of people that you want to make assumptions about. For example, Brooke wants to know how much stress college students experience during finals. Her population is every college student in the world because that’s who she’s interested in. Of course, there’s no way that Brooke can feasibly study every college student in the world, so she moves on to the next step.


  1. Specify a sampling frame. A sampling frame is the group of people from which you will draw your sample. For example, Brooke might decide that her sampling frame is every student at the university where she works. Notice that a sampling frame is not as large as the population, but it’s still a pretty big group of people. Brooke still won’t be able to study every single student at her university, but that’s a good place from which to draw her sample.


  1. Specify a sampling method. There are basically two ways to choose a sample from a sampling frame: randomly or non-randomly. There are benefits to both. Basically, if your sampling frame is approximately the same demographic makeup as your population, you probably want to randomly select your sample, perhaps by flipping a coin or drawing names out of a hat.

  But what if your sampling frame does not really represent your population? For example, what if the school where Brooke works has a lot more men than women and a lot more whites than minority races? In the population of every college student in the world, there might be more of a balance, but Brooke’s sampling frame (her school) doesn’t really represent that well. In that case, she might want to non-randomly select her sample in order to get a demographic makeup that is closer to that of her population.  

  1. Determine the sample size. In general, larger samples are better, but they also require more time and effort to manage. If Brooke ends up having to go through 1,000 surveys, it will take her more time than if she only has to go through 10 surveys. But the results of her study will be stronger with 1,000 surveys, so she (like all researchers) has to make choices and find a balance between what will give her good data and what is practical.


  1. Implement the plan. Once you know your population, sampling frame, sampling method, and sample size, you can use all that information to choose your sample.

It is virtually impossible to study every individual in the target population. In most cases, the target population, such as students in JS1, is simply too large for the researcher to plan a quality research study. Collecting millions of questionnaires from every JS1 student would present the following challenges:

  1. Millions of naira would be spent just to print the questionnaires, let alone transportation costs to distribute the questionnaires to all JS1 students.
  2. Researchers would have difficulties finding all JS1 students, particularly in village areas.
  3. Unqualified research assistants would have to be enlisted to assist in data collection, reducing the quality of data received.
  4. Years would be spent distributing and collecting the questionnaires, let alone coding the questionnaire responses.
  5. Since it will take so long to collect data from the entire population, the data from the first group of students sampled will likely be outdated by the time the last group of students is sampled.

Does this therefore mean that the target population has to be restricted to such a small group – such as all JS1 students in Baptist Academy – so that the researcher can access the entire population? NO! “Paradoxically, the attempt to observe all cases [in a population] may actually describe a population less accurately than a carefully selected sample…The reason is that the planning and logistics of observation are more manageable with a sample” (Singleton & Straits, 2010, p. 151). Research methodologists have developed sampling procedures that should identify a sample that is representative of the population, meaning that the sample closely resembles the target population on all relevant characteristics.   Theory of Sampling The theory of sampling is as follows:

  1. Researchers want to gather information about a whole group of people (the population).
  2. Researchers can only observe a part of the population (the sample).
  3. The findings from the sample are generalized, or extended, back to the population.

Therefore, the key question in sampling is How representative is the sample of the target population? This question is the foundation of population validity, the degree to which the results of a study can be generalized from the sample to the target population. The analogy of a fruit market can be used when thinking about the population, the sample, and the sampling technique. The first step in sampling is to identify the unit of analysis. This was described in Chapter 11, Identify the Population. Let’s say that you are conducting research related to a fruit market. What will be studied in the fruit market? Is it a type of fruit or the fruit sellers themselves? Let’s say you identify citrus fruit as the unit of analysis, and your population is all citrus fruit within the Bauchi Road fruit market. There are too many pieces of citrus fruit for you to study in that market, so you must select only a sample of the citrus fruit. A common error in sampling is that the sample and population are not identical. For example, the sample may be too narrow. If the population is all citrus fruit within the Bauchi Road fruit market, then the sample cannot only consist of lemons because your sample would be missing oranges, grapefruit, and limes. Therefore, you must find a way of selecting a representative sample of citrus fruit from your population. To apply to an educational study, perhaps one may say that the population is all university students, but only university students in public schools are sampled. Another common error is to make the population too broad. Some may say that the population is all mangoes in the Bauchi Road fruit market, but they are really only interested in green mangoes. If only green mangoes are of interest, then the population should be green mangoes in the Bauchi Road fruit market. In educational research, sometimes researchers are only interested in a population with a certain characteristic, such as student who has not chosen a career (in the case of career counseling). Thus, the population and sample must be the same.                                 Preliminary Considerations in Selecting a Sample Before selecting a sampling procedure, first consider the following:

  • Select the unit of analysis. When selecting the sample, it is imperative that the sampling technique selects cases based on this unit of analysis. In other words, if the unit of analysis is students, then the sampling technique must focus solely on how the students were selected. It would be an error to describe the selection of schools as the sampling technique when the unit of analysis is students.
  • Determine how many units need to be sampled. This step is a tricky balancing act. On the one hand, larger samples tend to be more representative of the target population and provide stronger statistical power. On the other hand, larger samples can decrease the quality of the research study, particularly for experimental and quasi-experimental designs. In experimental designs, if many people participate in the treatment, then the quality of treatment that each individual receives might suffer, resulting in inaccurate conclusions. It is a truism that overpopulation in classrooms reduces the impact of instruction; if there are too many students in the class, then the teaching will not be as effective. Likewise, we should equally avoid the problem of overpopulation in experiments: too many participants in a treatment group will reduce the impact of the treatment. Therefore, smaller treatment groups are generally preferable. In general, descriptive designs require at least 100 participants, correlational designs require at least 30 participants, and experimental, quasi-experimental, and causal-comparative designs require at least 15 participants per group. The size of the sample in experiments depend on how effective the treatment is. If you have a very effective treatment, then only a few participants are necessary. However, if the treatment is weak, then a larger sample size is necessary to find a significant effect.

Sampling Procedures There are many sampling procedures that have been developed to ensure that a sample adequately represents the target population. A few of the most common are described below. Simple Random Sampling In simple random sampling, every individual in the target population has an equal chance of being part of the sample. This requires two steps:

  1. Obtain a complete list of the population.
  2. Randomly select individuals from that list for the sample.

Recall that the sampling procedure must reflect the unit of analysis. In a study where the unit of analysis is the student, the researcher must obtain a complete list of every student in the target population to achieve simple random sampling. This is rarely possible, so very few, if any, educational studies use simple random sampling. Another factor to consider is the word random. Random is a technical term in social science research that means that selection was made without aim, reason, or patterns. If any study uses the word random, it means that specific scientific procedures were used to ensure that the sample was selected purely by chance. Scientists have developed a few procedures that must be followed for a study to achieve random, such as the hat-and-draw method or a random number table. To be random, participants cannot be chosen because of their intelligence, gender, social class, convenience, or any other factor besides scientifically-agreed upon random procedures. Using the word random when the unit of analysis was not selected by the hat-and-draw method or a random number table is either irresponsible or flat-out untruthful.               Stratified Random Sampling In stratified random sampling, the researcher first divides the population into groups based on a relevant characteristic and then selects participants within those groups. In educational research, stratified random sampling is typically used when the researcher wants to ensure that specific subgroups of people are adequately represented within the sample. For example, a research study examining the effect of computerized instruction on maths achievement needs to adequately sample both male and female pupils. Stratified random sampling will be used to ensure adequate representation of both males and females. Stratified random sampling requires four steps:

  • Determine the strata that the population will be divided into. The strata are the characteristics that the population is divided into, perhaps gender, age, urban/rural, etc.
  • Determine the number of participants necessary for each stratum. Perhaps the researcher wants equal representation within the strata: half male, half female; 20 children age 5, 20 children age 6, and 20 age 7; etc. Other times (e.g., large survey research), the researcher might want to use proportionate random sampling. This requires that the researcher first knows the proportion of the group in the entire population and then match that proportion within the sample. For example, a researcher might find the most recent Nigerian census to determine that females represent 53% of the population in Nigeria, so the sample will then include 53% females.
  • Split the units of analysis into the respective strata. In other words, if the target population is students and the researcher wants to stratify based on gender, then the researcher will need two lists of the target population: one list of the male students and another list of the female students.
  • Randomly sample participants from within the group. Using either the hat-and-draw method or a random number table, randomly select the requisite number of males and do the same for the females.

            Purposive Sampling In purposive sampling, the researcher uses their expert judgment to select participants that are representative of the population. To do this, the researcher should consider factors that might influence the population: perhaps socio-economic status, intelligence, access to education, etc. Then the researcher purposefully selects a sample that adequately represents the target population on these variables. Multi-Stage Sampling More frequently, educational researchers use multi-stage sampling. In multi-stage sampling, the sample is selected in multiple steps, or stages. For example, in the first stage, geographical regions, such as local government areas, are selected. In the second stage, perhaps schools may be selected. In the third stage, the unit of analysis – perhaps teachers or students, are sampled. If the unit of analysis is not selected in the first step, then the sampling procedure is multi-stage sampling. In multi-stage sampling, other sampling techniques may be used at the different stages. For example, the first stage may use random sampling, the second stage may use purposive sampling, and the third stage may use stratified sampling. The steps in multi-stage sampling are as follows:

  • Organize the sampling process into stages where the unit of analysis is systematically grouped.
  • Select a sampling technique for each stage.
  • Systematically apply the sampling technique to each stage until the unit of analysis has been selected.

                Conclusion Recall that the key question in sampling is How representative is the sample of the target population? Therefore, the researcher has the burden of demonstrating in their report (primarily in the methods section) that their sample, regardless of how it was chosen, represents the target population. Simple random sampling or multi-stage sampling will typically answer this question the best. However, as long as the researcher makes a convincing argument in their methods section that their sample adequately represents the target population, the researcher really can use any available sampling procedure.

AIOU Solved Assignment 2 Code 8604 Spring 2020 

Q.3   Develop a research proposal on following topic: “Comparison of 8th grade students’ achievements in mathematics at elementary level in Rawalpindi and Islamabad”.    (20)  

Your introduction is very important, actually the most important part of your proposal.  If your introduction gets your audience’s attention, they will stay with you throughout your proposal.   An effective introduction discusses the meaningfulness of the study with presentation of problem or issue.  It also serves as an argument advocating the need of study for your chosen object and gives a clear insight into your intentions. Thus the introduction presents a background and statement of context for your investigation.   The rest of your proposal supports this section.  It doesn’t need to be overly long, a few paragraphs should be enough, but it is the most critical as it establishes the nature, context, and scope of your project.  Key parts of the Introduction often become a part of a research abstract that may be used when you present your completed investigation and conclusions to an audience.  Although these aspects of an introduction are described separately, some parts may, in reality, be combined together when the actual proposal is written.   Clear Statement of the Problem   The most important aspect of a research proposal is the clarity of the research problem. For a short statement, it certainly has a lot of power.  The statement of the problem is the focal point of your research. It should state what you will be studying, whether you will do it through experimental or non-experimental investigation, and what the purpose of your findings will be.  As a part of the Introduction, effective problem statements answer the question “Why does this research need to be conducted?”   It is just one sentence (with several paragraphs of elaboration).  In it, you are looking for something wrong, something that needs close attention, or something where existing methods no longer seem to be working.   Example of a problem statement:   “The frequency of job layoffs is creating fear, anxiety, and a loss of productivity in middle management workers.”   In your wording, be succinct and on target. Give a short summary of the research problem that you have identified.  A research proposal may not be considered acceptable or credible if you fail to clearly identify the problem. Your biggest difficulty might be narrowing the topic since the topic is still relatively unfamiliar to you.  Your Literature Review should be a helpful source.   Problem statement   While the problem statement itself is just one sentence, it is always accompanied in the larger Introduction by several paragraphs that help to elaborate and that may include other elements of the research proposal.  You might present persuasive arguments as to why the problem is important enough to study or include the opinions of others (politicians, futurists, other professionals). Explain how the problem relates to business, social or political trends by presenting a bit of evidence from your Literature Review that demonstrates the scope and depth of the problem. Try to give dramatic and concrete illustrations of the problem. After writing the Introduction, however, make sure you can still easily identify the single sentence that is the problem statement.   Definitions   Be sure that your proposal is understandable to a general reader who does not know much about your field of investigation.  This section gives the definition of important terms and concepts that are usually stated in the objectives, hypothesis, and research questions.  Define subject-specific and technical terms.  If you are using words that are different in meaning in the context of your experiment from traditionally accepted meanings, define the terms.  Be sure to refer to authoritative sources in your definitions.   Explain any operational definitions, the definitions that you have created just for your study.  An example of an operational definition is: “For the purpose of this research, improvement is operationally defined as posttest score minus pretest score”. The clearest way to arrange your definitions page is to arrange terms in alphabetical order, with definitions stated in complete sentences. The following is an example of a definition section from a proposal entitled “Self-directed learning readiness and life satisfaction among older adults.”   Definition of Key Terms   Life Satisfaction – a self reported assessment of one’s overall psychosocial well-being. It is a combination of (a) personality factors such as mood and self-concept, (b) more socially-related factors such as the nature of one’s social interactions, (c) perceived health, and (d) financial security.   Older Adult – for the proposed study, older adult is defined as any person who is at least 65 years of age.   Self-Directed Learning – a process in which individuals take the initiative, with or without the help of others, in diagnosing their learning needs, formulating learning goals, identifying human and material resources for learning, choosing and implementing appropriate learning strategies, and evaluating learning outcomes.   Literature Review   Your literature review is already completed and can be included here.  The literature review develops broad ideas of what is already known in a field, and what questions are still unanswered.  This process will assist you in furthering narrowing the problem for investigation, and will highlight any theories that may exist to support developing hypotheses.  You must show that you have looked through the literature and have found the latest updates in your field of study in order for a proposal to be convincing to an audience.  This process also helps you to be sure that your investigation is not just “reinventing the wheel.”  A discussion of the present understanding and/or state of knowledge concerning the problem or issue sets the context for your investigation.   Questions or Hypothesis   Questions and hypotheses are testable explanations that are proposed before the methodology of a project is conducted, but after the researcher has had an opportunity to develop background knowledge (much like the literature review that you just finished).  Although research questions and hypotheses are different in their sentence structure and purpose, both seek to predict relationships.  Deciding whether to use questions or hypothesis depends on facts such as the purpose of the study, the approach and design of the methodology, and the expected audience for the research proposal.   Role of the Researcher   Determine what your role will be in the collection of the research material.  In this section describe your major tasks in your research procedure. Explain whether you will be an unobtrusive observer, a participant observer, or a collaborator.  Evaluate how your own bias may affect the methodology, outcomes, and analysis of findings.   Many times this element of the research Proposal will be affected by Ethics.  In addition, this section is often interwoven in a narrative design explanation with other elements of the proposal.   Review the excerpt below from a research proposal. you can identify how the researcher has defined his or her role in the investigation from the narrative explanation that is provided.   Research Design and Procedures   Following these lines of thinking, a qualitative study of the social world of full-time adult undergraduates is proposed, using semi-structured interviews as the primary research approach. It is proposed to begin the interviewing process in the fall of 1996. They will begin with unstructured questions such as the following: “What has it been like to be a full-time student at Central College?” Often, with only an occasional question from me for clarification, it is anticipated that the adults will talk about a wide variety of topics throughout an extended interview.   Q.4   Write the characteristic of a research report and explain different parts of research report.         (20)  


  Mostly, research work is presented in a written form. The practical utility of research study depends heavily on the way it is presented to those who are expected to act on the basis of research findings. Research report is a written document containing key aspects of research project. Research report is a medium to communicate research work with relevant people. It is also a good source of preservation of research work for the future reference. Many times, research findings are not followed because of improper presentation. Preparation of research report is not an easy task. It is an art. It requires a good deal of knowledge, imagination, experience, and expertise. It demands a considerable time and money. InTRODUCTION In our experience, many students experience difficulties with the formatting of their research proposals. Although some of the editors listed by the University of Stellenbosch Business School (USB) can do technical editing for you, it will save you time and money if your document is in the correct format from the beginning. This template is designed to assist you in writing a research proposal in the correct technical format as required by the USB. This template should serve as a starting point for any student writing a research proposal. The headings and styles give an indication of the sections required in the research proposal.   The initial research proposal should be typed, using double-line spacing, and be between 2 000 and 3 000 words in length. Refer to the information supplied in Appendix A and Appendix B and style sheets used in this template.   You need to save this template under a personalised file name and start by providing a preliminary title on the cover page. This preliminary title should clearly convey the key words associated with the proposed research. It is the responsibility of the applicant, not of the University of Stellenbosch Business School (USB) or provisional promoter, to find a suitable topic. overview or background Give an overview of the subject area. By way of introduction, this reading section of the existing literature should take the form of an abstract of the general subject or study area and identify the discipline(s) within which it falls. From this analysis the problem or disorder you wish to research will emerge and constitutes the reason or condition which necessitates the research. You should also indicate here the way in which your background gives you competencies in the chosen area. research focus This is where you explain the research problem, question and aim. If you use subheadings, this is the way to format them. Research problem From the overview of the subject area follows the research problem, i.e. you have to identify the possible cause(s) of the disorder. This section states the problem that you are exploring. Research question The research question is specific, concise, and clear. The research question can be expanded upon by stating sub-questions. Note: The difference between the research problem and research question is that the problem is broader, while the research question represents the “one question that you will answer at the end of your dissertation”. Research aim Next, you have to describe the research aim as it relates to solving the uncertainty or burning question you are interested in. It should explicitly hint towards the contribution you want to make with the intended study. You will in a later section (Section 5) elaborate on the scientific contribution made. research methodology Outline the methodology to be used. In its most widely-used description, research methodology relates to the nature of the scientific method used.  You need to display an awareness of the available methodologies for data collection and show a clear understanding of the methodologies that would be most suitable for your research. It may be that qualitative methods are appropriate, e.g. case studies and group discussions. Alternatively, your research may involve quantitative aspects relating to statistics and finance. In many instances you will be combining methodologies. You are expected to outline the design you consider to be most appropriate, i.e. how the research would be conducted. Typically, reference is made here to the type of data you will need, the nature of data collection (questionnaire development, sampling, type of survey, etc.), processing and interpretation. qualitative research quantitative research You need to select the appropriate proposed methodology. Since most studies are multi-disciplinary, they employ a combination of qualitative and quantitative research methodologies, which is called a hybrid approach. data collection Describe the data collection methods you will use. DATA ANALYSIS Describe your proposed data analysis approach and techniques. Merit of the research and proposed contribution to science A convincing statement is required as to why your topic merits scientific research, i.e. how it will contribute to and enrich the academic knowledge and understanding of management theory and professional management practice. This contribution results from the systematic investigation of your research activities, which are conducted to discover new information, as well as to expand and verify existing knowledge.   This contribution does not simply imply the gathering of new data and a description thereof, i.e. the What? questions. There are many things we do not know and that we could find out. This is data-gathering. The contribution to be made by doctoral research goes beyond this and requires the So what? questions, i.e. explanations, relationships, generalisations and theories.   We refer you to a working paper by Dr John Morrison, entitled A contribution to scientific knowledge, which we highly recommend to PhD registrants. (Find the link to this downloadable document at Literature review In this section you should demonstrate that you are au fait with the debates and issues raised in related literature. You should furnish a description of recent academic and empirical research in your chosen area. References to key texts and recently published articles should be made to convince that you appreciate their integrative relevance to your research area. A PhD is original research and you should be able to demonstrate that your proposed area has not been studied before. As such, you need to identify how your own research might make a useful contribution to the particular management-related area. research protocol You need to include a preliminary time and work schedule outlining the main phases in your research project. This is referred to as the research protocol. references A full list of references to key texts and articles must be included. Appendix C has examples of how referencing should be done. The USB’s website has comprehensive details on the PhD programme. The brochure can be downloaded at   The most important thing regarding references is that you should start recording all details of your references from the first day you start your research. It is impossible to try and find details, such as page numbers and volume numbers, when you compile your final reference list months later. Rather keep more details than you think you will need.

  Q.5   Discuss questionnaire as a research tool covering the following concepts; its construction, different forms and process to check validity and reliability.            (20)          

    The main idea of statistical inference is to take a random sample from a population and then to use the information from the sample to make inferences about particular population characteristics such as the mean (measure of central tendency), the standard deviation (measure of spread) or the proportion of units in the population that have a certain characteristic. Sampling saves money, time, and effort. Additionally, a sample can, in some cases, provide as much information as a corresponding study that would attempt to investigate an entire population-careful collection of data from a sample will often provide better information than a less careful study that tries to look at everything. We must study the behavior of the mean of sample values from different specified populations. Because a sample examines only part of a population, the sample mean will not exactly equal the corresponding mean of the population. Thus, an important consideration for those planning and interpreting sampling results, is the degree to which sample estimates, such as the sample mean, will agree with the corresponding population characteristic. In practice, only one sample is usually taken (in some cases such as “survey data analysis” a small “pilot sample” is used to test the data-gathering mechanisms and to get preliminary information for planning the main sampling scheme). However, for purposes of understanding the degree to which sample means will agree with the corresponding population mean, it is useful to consider what would happen if 10, or 50, or 100 separate sampling studies, of the same type, were conducted. How consistent would the results be across these different studies? If we could see that the results from each of the samples would be nearly the same (and nearly correct!), then we would have confidence in the single sample that will actually be used. On the other hand, seeing that answers from the repeated samples were too variable for the needed accuracy would suggest that a different sampling plan (perhaps with a larger sample size) should be used. A sampling distribution is used to describe the distribution of outcomes that one would observe from replication of a particular sampling plan. Know that estimates computed from one sample will be different from estimates that would be computed from another sample. Understand that estimates are expected to differ from the population characteristics (parameters) that we are trying to estimate, but that the properties of sampling distributions allow us to quantify, probabilistically, how they will differ. Understand that different statistics have different sampling distributions with distribution shapes depending on (a) the specific statistic, (b) the sample size, and (c) the parent distribution. Understand the relationship between sample size and the distribution of sample estimates. Understand that the variability in a sampling distribution can be reduced by increasing the sample size. See that in large samples, many sampling distributions can be approximated with a normal distribution.

Variance and Standard Deviation

  Deviations about the mean of a population is the basis for most of the statistical tests we will learn. Since we are measuring how widely a set of scores is dispersed about the mean we are measuring variability. We can calculate the deviations about the mean, and express it as variance or standard deviation. It is very important to have a firm grasp of this concept because it will be a central concept throughout the course. Both variance and standard deviation measures variability within a distribution. Standard deviation is a number that indicates how much, on average, each of the values in the distribution deviates from the mean (or center) of the distribution. Keep in mind that variance measures the same thing as standard deviation (dispersion of scores in a distribution). Variance, however, is the average squared deviations about the mean. Thus, variance is the square of the standard deviation. In terms of quality of goods/services, It is important to know that higher variation means lower quality. Measuring the size of variation and its source is the statistician’s job, while fixing it is the job of the engineer or the manager. Quality products and services have low variation.

What Is a Confidence Interval?

  In practice, a confidence interval is used to express the uncertainty in a quantity being estimated. There is uncertainty because inferences are based on a random sample of finite size from a population or process of interest. To judge the statistical procedure we can ask what would happen if we were to repeat the same study, over and over, getting different data (and thus different confidence intervals) each time. Know that a confidence interval computed from one sample will be different from a confidence interval computed from another sample. Understand the relationship between sample size and width of confidence interval. Know that sometimes the computed confidence interval does not contain the true mean value (that is, it is incorrect) and understand how this coverage rate is related to confidence level.

Questionnaire Design and Surveys Management

  This part of the course is aimed at students who need to perform basic statistical analyses on data from sample surveys, especially those in the marketing science. Students are expected to have a basic knowledge of statistics such as descriptive statistics and the concept of hypothesis testing. When the sampling units are human beings, the main methods of collecting information are:

  1. face-to-face interviewing
  2. postal surveys
  3. telephone surveys
  4. direct observation.
  5. Internet

The main questions are: What is the purpose of the survey? What kinds of questions the survey would be developed to answer? What sorts of actions is the company considering based on the results of the survey?

3 Comments on “AIOU Solved Assignment 2 Code 8604 Spring 2020”

  1. Develop a research proposal on analysis of reforms in curriculum for secondary level in Pakistan . Mention all necessary steps properly

Leave a Reply

Your email address will not be published. Required fields are marked *