Term: an idea expressed in words either spoken or written
Classification of Terms:
Singular : one that stands for only one definite object
Examples: 1) Table 2) Socrates 3) Tree
Collective : one that is applicable to each and every member of a class taken as a whole but not to an individual taken singly.
Examples:
1) orchestra 2) platoon
Particular : one that refers to an indefinite number of individuals or groups. Some signifiers of a particular term: some, a number of, several, almostall, practically all, at least one, a few of, not all, and the like.
Examples:
1) some Sillimanians 2) almost all students 3) several politicians
Universal : one that is applicable to each and every member of a class. Some signifiers of a universal term: No, All, Each, Every
Examples:
1) All Sillimanians 2) Every politician
Judgment: the mental act of affirming of denying something.
Proposition: judgment expressed in words either spoken or written.
Example:
1) President Noynoy Aquino is a good president. 2) President Noynoy Aguino isnot a good president.
Kinds of Propositions used in Logic
Categorical : a proposition that expresses an unconditional judgment.
Example: 1) The Japanese people are hard-working.
Hypothetical : a proposition that expresses a conditional judgment
Example: 1) If it rains today, then the road is wet.
Elements of a Categorical Proposition
Subject (S)
Copula (C)
Predicate (P)
Quantity of a Categorical Proposition
Particular: one that contains a particular subject term.
Example: 1) Some Sillimanians are foreigners.
Universal : one that contains a universal subject term.
Example: 1) All Filipinos are Asian.
Note: It is the quantity of the subject that determines the quantity of the proposition. Thus, if the subject is particular, then the proposition is particular; if the subject is universal, then the proposition is universal.
Note: If the subject of the proposition does not contain a signifier, the quantity of the proposition must be based on what the proposition denotes.
Quality of a Categorical Proposition
Affirmative : if the copula of the proposition does not contain a negation sign “not”
Example: 1) Some Sillimanians are brilliant.
Negative : if the copula of the proposition contains a negation sign “not”
Example: 1) Some Sillimanians arenot brilliant.
Four Basic Types of Categorical Propositions
Universal Affirmative (A) : All men are mortal.
Universal Negative (E) : No men are mortal.
Particular Affirmative (I) : Some men are mortal.
Particular Negative (O) : Some men are not mortal.
Translating Categorical Propositions into their Standard Form:
Standard Forms:
A proposition : All + subject + copula + predicate
E proposition : No + subject + copula + predicate
I proposition : Some + subject + copula + predicate
O proposition : Some + subject + copula + not + predicate
Examples:
A: Every priest is religious.
Standard form: All priests are religious.
E: Every priest is not religious.
Standard form: No priest is religious.
I:Almost all politicians are corrupt.
Standard form: Some politicians are corrupt.
O: Several politicians are not corrupt.
Standard form: Some politicians are not corrupt.
Nuns are girls.
Standard from: All nuns are girls.
Cheaters are not trustworthy.
Standard from: No cheaters are trustworthy.
Fruits are delicious.
Standard form: Some fruits are delicious.
Flowers are not fragrant.
Standard form: Some flowers are not fragrant.
Square of Opposition
Contrary: A E; differ only in quality
Rules: If one of the contraries is true, the other is false.
If one is false, the other is doubtful.
Examples:
1) A:
E:
2) E:
A:
Sub-contrary: I O; differ only in quality
Rules: If one of the sub-contraries is true, the other is doubtful.
If one is false, the other is true.
Examples:
1) I:
O:
2) O:
I:
Sub-alternation: A I and E O; differ only in quantity
Rules: If the universal is true, the particular is true.
If the universal is false, the particular is doubtful.
If the particular is true, the universal is doubtful.
If the particular is false, the universal is false.
Examples:
1) A:
I:
2) E:
O:
3) I:
A:
4) O:
E:
Contradiction: A O and E I; differ both in quality and quantity
Rules: One member of each part is a denial of the other
Examples:
1) A:
O:
2) E:
I:
3) O:
A:
4) I:
E:
Argument and Syllogism
Argument: consists of one or more propositions offered as evidence for another proposition
Syllogism: an argument which consists of three propositions which are so related so that when the first two propositions are posited as true, the third proposition must also be true.
Example: All lawyers are professionals.
Some criminals are professionals.
Therefore, some criminals are lawyers.
Elements of a Syllogism:
Major premise: the proposition that contains the major term
Minor premise: the proposition that contains the minor term
Conclusion: the third proposition whose meaning and truth are implied in the premise
Terms used in Syllogisms:
Major term (T): the predicate of the conclusion
Minor term (t): the subject of the conclusion
Middle term (M): the remaining term in the syllogism which does not appear in the conclusion
8 Rules of Syllogism: refer to the rules used in determining the validity of an argument
1) There must only be three terms in the syllogism: the major, minor, and middle terms.
2) The major and/or the minor term should only be universal in the conclusion if they are universal in the premises.
3) The middle term must be universal at least once.
4) If both of the premises are affirmative, the conclusion must also be affirmative.
5) If one premise is affirmative and the other negative, the conclusion must be negative.
6) The argument (syllogism) is invalid if both of the premises are negative.
7) One premise at least must be universal.
8) If one premise is particular, the conclusion must also be particular.
Gene Glass coined the term meta-analysis to describe an empirically-based research method, which synthesizes research findings from numerous empirical studies. In short, a meta-analysis is a synthesis of results of many researchers about the field or topic of interest.
Meta-analysis had its beginning in the social science literature, but its applicability extends to behavioral and physical sciences research and to any discipline where individual study findings are too meager to test a theory. Meta-analysis can address policy issues. It has also been a popular research methodology.
Meta-analysis is related to the review of related literature presented in research reports. What makes it different from an ordinary literature review is that it is more rigorous and exhaustive and requires the original empirical data or summaries, such as means, standard deviations, and correlation co-efficient.
While a literature review simply reports the results of a study as significant or not, meta-analysis requires statistical analysis of original data from the studies being integrated. The real strength of meta-analysis lies in its ability to relate conditions that vary across studies to outcomes. For example, Gene Glass and Mary Smith made a meta-analysis of 375 psychotherapy outcome studies and calculated 833 effects. They found a mean effect size of 68 which indicates that the average treated group was two-thirds of a standard deviation better than its control group. Furthermore, 88% of the effects were positive, showing that most treatment groups exceeded their respective control groups on all kinds of outcomes.
Quantitative Methods and Meta-analysis
Quantitative meta-analysis employs quantitative methodology similar to that used in the primary researches that are being integrated. Statistical significance and estimation of effect size provide summaries of study in quantitative integrated reviews. As pointed by R. Rosenthal, the general relationship between tests of significance and effect size is given by the relation: Test statistics is a product of size of effect and sample size.
Effect is determined by dividing the control and experimental group difference by the standard deviation of the control group (the standard deviation being presumed to have been unaffected by treatment). The result is similar to a Z score. This results in standardized measures of effect for comparability of results across studies. The information from each study is presented as the number of standard deviations by which the experimental group exceeds the control group. Estimation of effects is difficult if standard deviations and means are not available. One course of action is to write the authors and request for these data. The other alternative is to estimate effects from other statistics presented. A method of estimating effects, given the t value and the sample sizes of the control and the experimental groups (assuming that the variance of the control group is unaffected by the treatment) is given by Rosenthal and Rubin:
Effects may also be computed from reported correlation coefficient, but there is a need for transformations to produce comparable correlation statistics.
Other standard quantitative techniques used in meta-analysis include: traditional vote counting, methods for testing the statistical significance of combined results and statistical methods based on vote counts, omnibus combined significance tests, Rosenthal’s fail-safe number, and the possibility of combining raw data, and testing variation among effect sizes, analogues to the ANOVA and regression analysis for effect sizes, and the use of conventional statistical methods like ANOVA and regression analysis with effect sizes or correlations. Estimators of effect size may be adjusted for sources of bias, and correlations may be transformed to standard mean differences.
R. Rosenthal provides dear explanations of how to conduct tests of differences among research results. These include methods for research results represented as effects of magnitude, as well as those represented as p-value or significance level (that is, terms of omnibus procedures for testing differences among the results of three or more studies, as well as procedures for testing specific contrasts among research results), procedures for combining estimates, and standard errors for optimally weighted estimates.
It must be noted that research integration does not have to be solely quantitative (that is, the use of quantitative such as tests of combined significance) or qualitative (that is, the use of purely narrative procedures) because it might be necessary to combine quantitative and qualitative information such as narrative information in quantitative studies, case studies, expert judgment, and narrative research reviews.
H. Cooper delineates five stages in doing a meta-analysis, namely, 1) problem formulation (that is, deciding about what questions or hypotheses to address and what evidence needs to be included in the review), 2) data collection (that is, specification of procedures to be used in finding relevant evidence), 3) data evaluation (that is, deciding about which of the retrieved data should be included in the review), 4) analysis and interpretation (that is, selection of procedures for making inferences about the literature as a whole, and 5) public presentation (that is, deciding what information should be included in the report of the integrated review). On the other hand, R. Light and D. Pillemer give the following strategy in doing a meta-analysis: 1) formulation of the precise question, 2) exploration of available information, 3) selection of studies, 4) determination of the generality of conclusions, and 5) determination of the relationships between study characteristics and study outcomes.
H. Cooper suggests the following basic structure in writing the research report of a meta-analysis: 1) introduction, 2) methods, 3) results, and 4) discussion. These are actually the basic sections of primary research reports.
Validity, Reliability, and other Issues
Threat to validity may arise from nonrepresentative sampling, subjective decisions that can lead to procedural variations that can affect the outcomes of the research review, and the “file drawer” problem in combined significance testing. The file drawer problem has something to do with the effects of selective sampling in doing an integrative research.
Studies that report larger effects or more statistically significant results are more likely to get published. If these studies are sampled in an integrative review, the effect of this selective sampling will seriously distort the conclusions of the integrated review. Mary Smith, for example, reported that published journal results in a meta-analytic study of sex bias in counseling differed from dissertations, with journal results showing bias (average effect of .22) and dissertations showing the opposite (-.24). R. Rosenthal also mentioned about these drawers being filled with studies of no significant difference. He provides a procedure for determining the number of null results that would be necessary to overturn the conclusion, based on a significant finding from a combined-significance test. If only a few unretrieved null results could reduce the combined significance test result to insignificance, then the file drawer threat must be seriously entertained as a rival hypothesis. If the number of null results required is implausibly large, the finding is robust against the file drawer threat.
Another problem confronting meta-analysts is the “apples and oranges” problem. This refers to the inadvertent comparison of studies that are not comparable. Gene Glass suggests inclusion of all research bearing on the topic of interest, carefully categorizing it so that comparisons among various categories will yield important differences in quality should they exist.
Experts differ in their opinion regarding what to include in a meta-analytic study. R. Light and M. Smith suggest stiff criteria for inclusion of research in meta-analysis. Other scholars, such as Gene Glass, insist on including all relevant literature so that statistical analysis can assist in decisions about the use of various classes of studies. V. Wilson and Putnam found a large and consistent difference between randomized and nonrandomized studies of pretest sensitization, which lead them to ignore nonrandomized studies in further meta-analyses. The experimental and logical evidence for pretest effect was lacking in the latter studies. On the other hand, M. Smith and G. Glass found no differences between randomized and nonrandomized psychotherapy outcome studies; hence, they aggregated the two in their latter syntheses.
Criticisms of Meta-analysis
R. Rosenthal gives six classes of criticisms of meta-analysis: those that concern sampling bias, the loss of information inherent in meta-analysis, heterogeneity of method or of study quality, problems of dependence between and within studies, the purported exaggeration of significance in meta-analysis, and the problem of determining the practical importance of effect size.
This lecture will briefly discuss the meaning, nature, and dynamics of a research gap. In particular, it will address the following questions:
1) What is a research gap?
2) What is the importance of identifying the research gap?
3) How to identify a research gap?
In addressing these three important questions, this lecture will give more weight on the third question. This is because many fledgling scholars and master’s and doctoral students struggled in identifying the gap in their research, thesis, or dissertation. Hence, it is the goal of this lecture to spare them the unnecessary burden of circling the mountain several times before getting to the top.
So, what is a research gap?
Understood more broadly, a research gap is the problem that researchers would want to see addressed in the research. As the name suggests, it is the gap that researchers fill with their proposed research project.
Hence, a research gap is “what is missing” or “what is not addressed” in the current state of knowledge. Put simply, a research gap is the question or problem that has not been answered in your area of specialization. For this reason, the research gap establishes “the need” or the “importance, urgency, and necessity” of your proposed research project, thesis, or dissertation.
This explains why all types of research always begin with a research gap. Indeed, no research activity is possible without the research gap.
Please note that this is what experienced reviewers or thesis/dissertation panel members are looking for during thesis or dissertation proposal defense. Thus, if your proposed thesis or dissertation does not have or does not clearly articulate the research gap, then chances are your thesis or dissertation proposal will be rejected and you have to do your research again from scratch.
This is the problem with many master’s and doctoral students when they write their thesis or dissertation. In most cases, because they are inexperienced researchers and, sometimes, they do not consult their thesis/dissertation adviser regularly, they simply start with a research aim and thought that it’s already the research gap. But the research gap is not the same with the research aim. And in some cases, master’s and doctoral students just copied or patterned their thesis or dissertation on previous researches.
Let us consider the example below.
Supposed the working title of the thesis/dissertation proposal is “Imposed Career Study among University Students in Hong Kong”. With this title, we can have the following research aim:
“The proposed research aims to determine the lived experiences of those students who were just forced to take a certain career course according to the wishes of their parents or significant others and how it affects the psycho-emotional and social wellbeing of these students.”
Again, many master’s and doctoral students thought that the aim is already the problem or the research gap of the proposed research project. But as already mentioned, it is not.
So, what could possibly be the research gap of the above proposed research project?
Based on the above research aim, we can have, for example, the idea:
“The researcher may have learned from experience or through literature review that there are university students in Hong Kong who were just forced to take certain career course according to the wishes of their parents or significant others and that these students were devastated and became rebellious in schools. For this reason, these students may become social delinquents in the future. Now, based on the researcher’s initial review of related literature, it was found out that no study has been conducted on the topic.”
As we can see, the problem is that there are university students in Hong Kong who were just forced to take a certain career course according to the wishes of their parents or significant others. As a result of being just forced to take a certain career course, these students have become devastated and rebellious, which in turn will make them as social delinquents in the future. Also, there has been no study conducted on this topic in Hong Kong. This is exactly what we meant by a “research gap”. This is “what is missing” or “what has not been addressed” in the current state of knowledge in this field. And with this research gap, we can now formulate the research aims, which reads:
“The proposed research aims to determine the lived experiences of those students who were just forced to take a certain career course according to the wishes of their parents or significant others and how it affects the psycho-emotional and social wellbeing of these students.”
If one may ask why the need for this study, then the researcher may add:
“The researcher argues that there is a need to determine the lived experiences of these students so that we can create a career decision-making program as an alternative in addressing the problem.”
As we can see, identifying the research gap and articulating it in the “background” or “rationale” of the study is important not only because it will spare the researcher the unnecessary toil of making major revision, but also because it will make the research publishable. For sure, if the researcher clearly identifies the research gap and articulates it in the background of the study, the reviewers or thesis defense panel members will be able to conclude right away that the proposed research project is unique and original because it is not a duplication of what have been done in the past. This will also send a message to the reviewers or thesis defense panel members that the researcher has deep knowledge of the topic under investigation. As is well known, finding original and innovative topics in the chosen field as well as identifying and articulating the research gap is never an easy feat.
Now that we have briefly discussed the nature and meaning of a research gap and its importance, the next question is how do we identify the research gap?
For experienced researchers, because they already have broad and deep knowledge on their chosen field of specialization, they can easily identify a research gap. However, for fledgling scholars as well as master’s and doctoral students, as already mentioned, identifying a research gap is never an easy feat. But the application of some proven techniques will somehow help ease the process.
Let me briefly discuss the three important techniques in identifying a research gap.
Of course, there are a number of techniques on how to identify a research gap, but the three points introduced below are the most effective ones.
First, when thinking of a topic in your field of specialization, it would greatly help if you start with something that you are passionate about, something that would seem like second skin to you.
For some obvious reason, being passionate at something makes you push yourself harder, and despite working long hours on it, you will still manage to smile. In fact, if you love what you are doing, then long and hard labor is turned into “play”. Hence, despite the hardships, you keep doing your research because you enjoyed it.
Of course, starting with something that you are passionate about in relation to identifying a research gap involves “choosing a particular topic” in your discipline or field of specialization. For instance, if your discipline is “education”, then you might be passionate about doing research on “teachers’ burnout level”, “philosophy of education”, “critical pedagogy”, or “lived experiences of teachers handling subjects not in line with their field of specialization”.
If your discipline is psychology, then you might be passionate about doing research on “social cognition”, “social control”, “racism”, “verbal communication”, or even “attraction, romance, and love”.
Second, once you have chosen a topic that you are passionate about, the next step is to “determine the mega trends and recent debates” in your discipline or field of specialization. This is important because once you know the mega trends or recent debates in your discipline or field of specialization, you can easily identify what have and have not been done in your discipline.
Determining the mega trends and recent debates in your discipline is also important because it will ensure that your research is timely and necessary. You have to remember that you do not do research for the sake of doing research, of completing a master’s or doctoral degree. You do research because there is a problem that needs to be addressed. Hence, a particular research is timely if the topic is one of the mega trends and recent debates in the field and it is necessary if it attempts to address a serious problem that requires urgent consideration.
Of course, determining the mega trends and recent debates in your discipline implies doing a literature review. This leads us to the third and last point.
Needless to say, you need to review recent literature in your chosen discipline or field of specialization so you may know what scholars have done so far. In this way, you will be able to identify possible gaps that you can fill in. For example, if your discipline is anthropology and you are passionate about doing research on the indigenous peoples in Southeast Asia, then you need to review literature on indigenous peoples in Southeast Asia in the last, say, 3-5 years.
Now, suppose several famous scholars on indigenous peoples in Southeast Asia have published on “the marginalization” of the Dayak indigenous people in Borneo, then this is precisely one of the mega trends and recent debates in this field of specialization.
Suppose you are interested in joining the discussion or debate on this topic, then you need to identify what have not been done by those scholars. It could be a problem that remains unsolved or a new insight that may help shed light on the issue being debated upon.
How do you do this?
Suppose there are 5 famous scholars working on the topic “the marginalization” of the indigenous peoples in Southeast Asia, particularly the Dayak indigenous people in Borneo. What you need to do now is review these pieces of literature and identify their concepts and arguments. For instance, you may say:
Scholar 1, in her work titled “Modernism and the Dayak People of Borneo”, says that the Dayak indigenous peoples in Borneo have been pushed further to the periphery by the forces of modernity, such as consumerism.
Scholar 2, in his work titled “Militarism in Borneo”, argues that one of the causes of the marginalization of the Dayak people in Borneo is the imposition of militarization in the island.
Scholar 3, in her work titled “The Resiliency of the Dayak People”, says that despite the constant presence of social forces that marginalized the Dayak people, the researcher found out that the Dayak people are very resilient. In fact, they have overcome every challenge that they faced and easily returned to their normal life.
Scholar 4, in his work titled “Different Faces of Marginalization in Borneo”, says that the Dayak people have been marginalized by different forces of globalization, such as the logging and mining companies.
Lastly, scholar 5, in her work titled “Rights, Recognition, and the Dayak People”, narrates not only how the Dayak people have been marginalized by the forces of globalization but also the basic and inalienable rights of the Dayak people.
Now, after reviewing these important pieces of literature about the marginalization of the Dayak people, you realized that no scholar on the Dayak people, so far as you know, has done research on “the way in which the Dayak people resisted any forms of marginalization”.
As you can see, this issue is one of the important topics on the debate about the marginalization of the Dayak people in Borneo, yet no scholar has brought this issue on the table. Hence, this could be a possible “gap” in this area of specialization that you can fill in with your research on the way in which the Dayak people resisted any forms of marginalization.
With this “research gap”, you may work, for example, on “the Dayak people’s struggle for recognition of their rights to ancestral domain”. Your working title may read:
“Self-Determination and the Dayak People’s Struggle for Recognition”
And your research’s main goal reads:
“This proposed thesis aims to explore how the Dayak people in Borneo resisted the forces of globalization that marginalized them.”
So, that’s what a research gap is and how to identify it.
Please note, however, that what I shared above are just some of the techniques on how to identify a research gap. There are other techniques that might help you in identifying a research gap or you may want to develop your own. What is important at this point is that through the discussion above you have now a basic understanding of what a research gap is and how to identify it.
And lastly, please note that the principles that we applied in the above discussion on how to identify a research gap can be applied to all disciplines, be they social sciences, humanities, natural sciences, education, engineering, mathematics, or psychology.
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Background of the Study in Research: Definition and the Core Elements it Contains
Before we embark on a detailed discussion on how to write the background of the study of your proposed research or thesis, it is important to first discuss its meaning and the core elements that it should contain. This is obviously because understanding the nature of the background of the study in research and knowing exactly what to include in it allow us to have both greater control and clear direction of the writing process.
So, what really is the background of the study and what are the core elements that it should contain?
The background of the study, which usually forms the first section of the introduction to a research paper or thesis, provides the overview of the study. In other words, it is that section of the research paper or thesis that establishes the context of the study. Its main function is to explain why the proposed research is important and essential to understanding the main aspects of the study.
The background of the study, therefore, is the section of the research paper or thesis that identifies the problem or gap of the study that needs to addressed and justifies the need for conducting the study. It also articulates the main goal of the study and the thesis statement, that is, the main claim or argument of the paper.
Given this brief understanding of the background of the study, we can anticipate what readers or thesis committee members expect from it. As we can see, the background of the study should contain the following major points:
1) brief discussion on what is known about the topic under investigation; 2) An articulation of the research gap or problem that needs to be addressed; 3) What the researcher would like to do or aim to achieve in the study (research goal); 4) The thesis statement, that is, the main argument or contention of the paper (which also serves as the reason why the researcher would want to pursue the study); 5) The major significance or contribution of the study to a particular discipline; and 6) Depending on the nature of the study, an articulation of the hypothesis of the study.
Thus, when writing the background of the study, you should plan and structure it based on the major points just mentioned. With this, you will have a clear picture of the flow of the tasks that need to be completed in writing this section of your research or thesis proposal.
Now, how do you go about writing the background of the study in your proposed research or thesis?
The next lessons will address this question.
How to Write the Opening Paragraphs of the Background of the Study?
To begin with, let us assume that you already have conducted a preliminary research on your chosen topic, that is, you already have read a lot of literature and gathered relevant information for writing the background of your study. Let us also assume that you already have identified the gap of your proposed research and have already developed the research questions and thesis statement. If you have not yet identified the gap in your proposed research, you might as well go back to our lesson on how to identify a research gap.
So, we will just put together everything that you have researched into a background of the study (assuming, again, that you already have the necessary information). But in this lesson, let’s just focus on writing the opening paragraphs.
It is important to note at this point that there are different styles of writing the background of the study. Hence, what I will be sharing with you here is not just “the” only way of writing the background of the study. As a matter of fact, there is no “one-size-fits-all” style of writing this part of the research or thesis. At the end of the day, you are free to develop your own. However, whatever style it would be, it always starts with a plan which structures the writing process into stages or steps. The steps that I will share with below are just some of the most effective ways of writing the background of the study in research.
So, let’s begin.
It is always a good idea to begin the background of your study by giving an overview of your research topic. This may include providing a definition of the key concepts of your research or highlighting the main developments of the research topic.
Let us suppose that the topic of your study is the “lived experiences of students with mathematical anxiety”.
Here, you may start the background of your study with a discussion on the meaning, nature, and dynamics of the term “mathematical anxiety”. The reason for this is too obvious: “mathematical anxiety” is a highly technical term that is specific to mathematics. Hence, this term is not readily understandable to non-specialists in this field.
So, you may write the opening paragraph of your background of the study with this:
“Mathematical anxiety refers to the individual’s unpleasant emotional mood responses when confronted with a mathematical situation.”
Since you do not invent the definition of the term “mathematical anxiety”, then you need to provide a citation to the source of the material from which you are quoting. For example, you may now say:
“Mathematical anxiety refers to the individual’s unpleasant emotional mood responses when confronted with a mathematical situation (Eliot, 2020).”
And then you may proceed with the discussion on the nature and dynamics of the term “mathematical anxiety”. You may say:
“Lou (2019) specifically identifies some of the manifestations of this type of anxiety, which include, but not limited to, depression, helplessness, nervousness and fearfulness in doing mathematical and numerical tasks.”
After explaining to your readers the meaning, nature, and dynamics (as well as some historical development if you wish to) of the term “mathematical anxiety”, you may now proceed to showing the problem or gap of the study. As you may already know, the research gap is the problem that needs to be addressed in the study. This is important because no research activity is possible without the research gap.
Let us suppose that your research problem or gap is: “Mathematical anxiety can negatively affect not just the academic achievement of the students but also their future career plans and total well-being. Also, there are no known studies that deal with the mathematical anxiety of junior high school students in New Zealand.” With this, you may say:
“If left unchecked, as Shapiro (2019) claims, this problem will expand and create a total avoidance pattern on the part of the students, which can be expressed most visibly in the form of cutting classes and habitual absenteeism. As we can see, this will negatively affect the performance of students in mathematics. In fact, the study conducted by Luttenberger and Wimmer (2018) revealed that the outcomes of mathematical anxiety do not only negatively affect the students’ performance in math-related situations but also their future career as professionals. Without a doubt, therefore, mathematical anxiety is a recurring problem for many individuals which will negatively affect the academic success and future career of the student.”
Now that you already have both explained the meaning, nature, and dynamics of the term “mathematical anxiety” and articulated the gap of your proposed research, you may now state the main goal of your study. You may say:
“Hence, it is precisely in this context that the researcher aims to determine the lived experiences of those students with mathematical anxiety. In particular, this proposed thesis aims to determine the lived experiences of the junior high school students in New Zealand and identify the factors that caused them to become disinterested in mathematics.”
Please note that you should not end the first paragraph of your background of the study with the articulation of the research goal. You also need to articulate the “thesis statement”, which usually comes after the research goal. As is well known, the thesis statement is the statement of your argument or contention in the study. It is more of a personal argument or claim of the researcher, which specifically highlights the possible contribution of the study. For example, you may say:
“The researcher argues that there is a need to determine the lived experiences of these students with mathematical anxiety because knowing and understanding the difficulties and challenges that they have encountered will put the researcher in the best position to offer some alternatives to the problem. Indeed, it is only when we have performed some kind of a ‘diagnosis’ that we can offer practicable solutions to the problem. And in the case of the junior high school students in New Zealand who are having mathematical anxiety, determining their lived experiences as well as identifying the factors that caused them to become disinterested in mathematics are the very first steps in addressing the problem.”
If we combine the bits and pieces that we have written above, we can now come up with the opening paragraphs of your background of the study, which reads:
As we can see, we can find in the first paragraph 5 essential elements that must be articulated in the background of the study, namely:
1) A brief discussion on what is known about the topic under investigation; 2) An articulation of the research gap or problem that needs to be addressed; 3) What the researcher would like to do or aim to achieve in the study (research goal); 4) The thesis statement, that is, the main argument or claim of the paper; and 5) The major significance or contribution of the study to a particular discipline. So, that’s how you write the opening paragraphs of your background of the study. The next lesson will talk about writing the body of the background of the study.
How to Write the Body of the Background of the Study?
If we liken the background of the study to a sitting cat, then the opening paragraphs that we have completed in the previous lesson would just represent the head of the cat.
This means we still have to write the body (body of the cat) and the conclusion (tail). But how do we write the body of the background of the study? What should be its content?
Truly, this is one of the most difficult challenges that fledgling scholars faced. Because they are inexperienced researchers and didn’t know what to do next, they just wrote whatever they wished to write. Fortunately, this is relatively easy if they know the technique.
One of the best ways to write the body of the background of the study is to attack it from the vantage point of the research gap. If you recall, when we articulated the research gap in the opening paragraphs, we made a bold claim there, that is, there are junior high school students in New Zealand who are experiencing mathematical anxiety. Now, you have to remember that a “statement” remains an assumption until you can provide concrete proofs to it. This is what we call the “epistemological” aspect of research. As we may already know, epistemology is a specific branch of philosophy that deals with the validity of knowledge. And to validate knowledge is to provide concrete proofs to our statements. Hence, the reason why we need to provide proofs to our claim that there are indeed junior high school students in New Zealand who are experiencing mathematical anxiety is the obvious fact that if there are none, then we cannot proceed with our study. We have no one to interview with in the first. In short, we don’t have respondents.
The body of the background of the study, therefore, should be a presentation and articulation of the proofs to our claim that indeed there are junior high school students in New Zealand who are experiencing mathematical anxiety. Please note, however, that this idea is true only if you follow the style of writing the background of the study that I introduced in this course.
So, how do we do this?
One of the best ways to do this is to look for literature on mathematical anxiety among junior high school students in New Zealand and cite them here. However, if there are not enough literature on this topic in New Zealand, then we need to conduct initial interviews with these students or make actual classroom observations and record instances of mathematical anxiety among these students. But it is always a good idea if we combine literature review with interviews and actual observations.
Assuming you already have the data, then you may now proceed with the writing of the body of your background of the study. For example, you may say:
“According to records and based on the researcher’s firsthand experience with students in some junior high schools in New Zealand, indeed, there are students who lost interest in mathematics. For one, while checking the daily attendance and monitoring of the students, it was observed that some of them are not always attending classes in mathematics but are regularly attending the rest of the required subjects.”
After this sentence, you may insert some literature that will support this position. For example, you may say:
“As a matter of fact, this phenomenon is also observed in the work of Estonanto. In his study titled ‘Impact of Math Anxiety on Academic Performance in Pre-Calculus of Senior High School’, Estonanto (2019) found out that, inter alia, students with mathematical anxiety have the tendency to intentionally prioritize other subjects and commit habitual tardiness and absences.”
Then you may proceed saying:
“With this initial knowledge in mind, the researcher conducted initial interviews with some of these students. The researcher learned that one student did not regularly attend his math subject because he believed that he is not good in math and no matter how he listens to the topic he will not learn.”
Then you may say:
“Another student also mentioned that she was influenced by her friends’ perception that mathematics is hard; hence, she avoids the subject. Indeed, these are concrete proofs that there are some junior high school students in New Zealand who have mathematical anxiety. As already hinted, “disinterest” or the loss of interest in mathematics is one of the manifestations of a mathematical anxiety.”
If we combine what we have just written above, then we can have the first two paragraphs of the body of our background of the study. It reads:
“According to records and based on the researcher’s firsthand experience with students in some junior high schools in New Zealand, indeed there are students who lost interest in mathematics. For one, while checking the daily attendance and monitoring of the students, it was observed that some of them are not always attending classes in mathematics but are regularly attending the rest of the required subjects. As a matter of fact, this phenomenon is also observed in the work of Estonanto. In his study titled ‘Impact of Math Anxiety on Academic Performance in Pre-Calculus of Senior High School’, Estonanto (2019) found out that, inter alia, students with mathematical anxiety have the tendency to intentionally prioritize other subjects and commit habitual tardiness and absences.
With this initial knowledge in mind, the researcher conducted initial interviews with some of these students. The researcher learned that one student did not regularly attend his math subject because he believed that he is not good in math and no matter how he listens to the topic he will not learn. Another student also mentioned that she was influenced by her friends’ perception that mathematics is hard; hence, she avoids the subject. Indeed, these are concrete proofs that there are some junior high school students in New Zealand who have mathematical anxiety. As already hinted, “disinterest” or the loss of interest in mathematics is one of the manifestations of a mathematical anxiety.”
And then you need validate this observation by conducting another round of interview and observation in other schools. So, you may continue writing the body of the background of the study with this:
“To validate the information gathered from the initial interviews and observations, the researcher conducted another round of interview and observation with other junior high school students in New Zealand.”
“On the one hand, the researcher found out that during mathematics time some students felt uneasy; in fact, they showed a feeling of being tensed or anxious while working with numbers and mathematical problems. Some were even afraid to seat in front, while some students at the back were secretly playing with their mobile phones. These students also show remarkable apprehension during board works like trembling hands, nervous laughter, and the like.”
Then provide some literature that will support your position. You may say:
“As Finlayson (2017) corroborates, emotional symptoms of mathematical anxiety involve feeling of helplessness, lack of confidence, and being nervous for being put on the spot. It must be noted that these occasionally extreme emotional reactions are not triggered by provocative procedures. As a matter of fact, there are no personally sensitive questions or intentional manipulations of stress. The teacher simply asked a very simple question, like identifying the parts of a circle. Certainly, this observation also conforms with the study of Ashcraft (2016) when he mentions that students with mathematical anxiety show a negative attitude towards math and hold self-perceptions about their mathematical abilities.”
And then you proceed:
“On the other hand, when the class had their other subjects, the students show a feeling of excitement. They even hurried to seat in front and attentively participating in the class discussion without hesitation and without the feeling of being tensed or anxious. For sure, this is another concrete proof that there are junior high school students in New Zealand who have mathematical anxiety.”
To further prove the point that there indeed junior high school students in New Zealand who have mathematical anxiety, you may solicit observations from other math teachers. For instance, you may say:
“The researcher further verified if the problem is also happening in other sections and whether other mathematics teachers experienced the same observation that the researcher had. This validation or verification is important in establishing credibility of the claim (Buchbinder, 2016) and ensuring reliability and validity of the assertion (Morse et al., 2002). In this regard, the researcher attempted to open up the issue of math anxiety during the Departmentalized Learning Action Cell (LAC), a group discussion of educators per quarter, with the objective of ‘Teaching Strategies to Develop Critical Thinking of the Students’. During the session, one teacher corroborates the researcher’s observation that there are indeed junior high school students in New Zealand who have mathematical anxiety. The teacher pointed out that truly there were students who showed no extra effort in mathematics class in addition to the fact that some students really avoided the subject. In addition, another math teacher expressed her frustrations about these students who have mathematical anxiety. She quipped: “How can a teacher develop the critical thinking skills or ability of the students if in the first place these students show avoidance and disinterest in the subject?’.”
Again, if we combine what we have just written above, then we can now have the remaining parts of the body of the background of the study. It reads:
So, that’s how we write the body of the background of the study in research. Of course, you may add any relevant points which you think might amplify your content. What is important at this point is that you now have a clear idea of how to write the body of the background of the study.
How to Write the Concluding Part of the Background of the Study?
Since we have already completed the body of our background of the study in the previous lesson, we may now write the concluding paragraph (the tail of the cat). This is important because one of the rules of thumb in writing is that we always put a close to what we have started.
It is important to note that the conclusion of the background of the study is just a rehashing of the research gap and main goal of the study stated in the introductory paragraph, but framed differently. The purpose of this is just to emphasize, after presenting the justifications, what the study aims to attain and why it wants to do it. The conclusion, therefore, will look just like this:
“Given the above discussion, it is evident that there are indeed junior high school students in New Zealand who are experiencing mathematical anxiety. And as we can see, mathematical anxiety can negatively affect not just the academic achievement of the students but also their future career plans and total well-being. Again, it is for this reason that the researcher attempts to determine the lived experiences of those junior high school students in New Zealand who are experiencing a mathematical anxiety.”
If we combine all that we have written from the very beginning, the entire background of the study would now read:
If we analyze the background of the study that we have just completed, we can observe that in addition to the important elements that it should contain, it has also addressed other important elements that readers or thesis committee members expect from it.
On the one hand, it provides the researcher with a clear direction in the conduct of the study. As we can see, the background of the study that we have just completed enables us to move in the right direction with a strong focus as it has set clear goals and the reasons why we want to do it. Indeed, we now exactly know what to do next and how to write the rest of the research paper or thesis.
On the other hand, most researchers start their research with scattered ideas and usually get stuck with how to proceed further. But with a well-written background of the study, just as the one above, we have decluttered and organized our thoughts. We have also become aware of what have and have not been done in our area of study, as well as what we can significantly contribute in the already existing body of knowledge in this area of study.
Please note, however, as I already mentioned previously, that the model that I have just presented is only one of the many models available in textbooks and other sources. You are, of course, free to choose your own style of writing the background of the study. You may also consult your thesis supervisor for some guidance on how to attack the writing of your background of the study.
Lastly, and as you may already know, universities around the world have their own thesis formats. Hence, you should follow your university’s rules on the format and style in writing your research or thesis. What is important is that with the lessons that you learned in this course, you can now easily write the introductory part of your thesis, such as the background of the study.
In scientific research, variables are used to describe and measure different phenomena. These variables can be broadly categorized as either dependent or independent variables. Understanding the difference between these two types of variables is crucial in designing and conducting research studies.
Dependent variables (DV) are the variables that are observed and measured in a study. The value of the dependent variable is thought to depend on, or be influenced by, changes in the independent variable(s). The dependent variable is also referred to as the outcome variable or the response variable.
For example, in a study examining the effects of a new medication on blood pressure, the dependent variable would be the blood pressure of the participants. If the medication is effective, the dependent variable (blood pressure) should decrease in those who received the medication compared to those who received a placebo or no treatment.
Independent variables (IV) are the variables that are manipulated or controlled by the researcher. The independent variable is thought to cause changes in the dependent variable. The independent variable is also referred to as the predictor variable or the explanatory variable.
For example, in a study examining the effects of a new medication on blood pressure, the independent variable would be the medication itself. The researcher can manipulate the independent variable by administering the medication to the treatment group while giving a placebo to the control group.
It’s important to note that the relationship between the independent and dependent variables is often not as straightforward as in the above example. In many cases, there may be multiple independent variables or multiple dependent variables that are influenced by various independent variables. This complexity can make it challenging to design and interpret research studies.
The relationship between the independent and dependent variables is often depicted in a graph or chart called a scatterplot. The scatterplot can help researchers visualize the relationship between the two variables and identify any patterns or trends in the data.
One way to remember the difference between independent and dependent variables is to use the acronym “DRY MIX”. In this acronym, DRY stands for “dependent variable, response variable, or Y-axis” and MIX stands for “manipulated variable, independent variable, or X-axis”.
In summary, the independent variable is the variable that is manipulated or controlled by the researcher, while the dependent variable is the variable that is observed and measured in the study. The relationship between the independent and dependent variables is often complex, and it can be challenging to design and interpret research studies that investigate this relationship.
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In research, variables are crucial components that help to define and measure the concepts and phenomena under investigation. Variables are defined as any characteristic or attribute that can vary or change in some way. They can be measured, manipulated, or controlled to investigate the relationship between different factors and their impact on the research outcomes. In this essay, I will discuss the importance of variables in research, highlighting their role in defining research questions, designing studies, analyzing data, and drawing conclusions.
Defining Research Questions
Variables play a critical role in defining research questions. Research questions are formulated based on the variables that are under investigation. These questions guide the entire research process, including the selection of research methods, data collection procedures, and data analysis techniques. Variables help researchers to identify the key concepts and phenomena that they wish to investigate, and to formulate research questions that are specific, measurable, and relevant to the research objectives.
For example, in a study on the relationship between exercise and stress, the variables would be exercise and stress. The research question might be: “What is the relationship between the frequency of exercise and the level of perceived stress among young adults?”
Designing Studies
Variables also play a crucial role in the design of research studies. The selection of variables determines the type of research design that will be used, as well as the methods and procedures for collecting and analyzing data. Variables can be independent, dependent, or moderator variables, depending on their role in the research design.
Independent variables are the variables that are manipulated or controlled by the researcher. They are used to determine the effect of a particular factor on the dependent variable. Dependent variables are the variables that are measured or observed to determine the impact of the independent variable. Moderator variables are the variables that influence the relationship between the independent and dependent variables.
For example, in a study on the effect of caffeine on athletic performance, the independent variable would be caffeine, and the dependent variable would be athletic performance. The moderator variables could include factors such as age, gender, and fitness level.
Analyzing Data
Variables are also essential in the analysis of research data. Statistical methods are used to analyze the data and determine the relationships between the variables. The type of statistical analysis that is used depends on the nature of the variables, their level of measurement, and the research design.
For example, if the variables are categorical or nominal, chi-square tests or contingency tables can be used to determine the relationships between them. If the variables are continuous, correlation analysis or regression analysis can be used to determine the strength and direction of the relationship between them.
Drawing Conclusions
Finally, variables are crucial in drawing conclusions from research studies. The results of the study are based on the relationship between the variables and the conclusions drawn depend on the validity and reliability of the research methods and the accuracy of the statistical analysis. Variables help to establish the cause-and-effect relationships between different factors and to make predictions about the outcomes of future events.
For example, in a study on the effect of smoking on lung cancer, the independent variable would be smoking, and the dependent variable would be lung cancer. The conclusion would be that smoking is a risk factor for lung cancer, based on the strength and direction of the relationship between the variables.
Conclusion
In conclusion, variables play a crucial role in research across different fields and disciplines. They help to define research questions, design studies, analyze data, and draw conclusions. By understanding the importance of variables in research, researchers can design studies that are relevant, accurate, and reliable, and can provide valuable insights into the phenomena under investigation. Therefore, it is essential to consider variables carefully when designing, conducting, and interpreting research studies.
First of all, research is necessary and valuable in society because, among other things, 1) it is an important tool for building knowledge and facilitating learning; 2) it serves as a means in understanding social and political issues and in increasing public awareness; 3) it helps people succeed in business; 4) it enables us to disprove lies and support truths; and 5) it serves as a means to find, gauge, and seize opportunities, as well as helps in finding solutions to social and health problems (in fact, the discovery of COVID-19 vaccines is a product of research).
Now, quantitative research, as a type of research that explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics, is very important because it relies on hard facts and numerical data to gain as objective a picture of people’s opinion as possible or an objective understanding of reality. Hence, quantitative research enables us to map out and understand the world in which we live.
In addition, quantitative research is important because it enables us to conduct research on a large scale; it can reveal insights about broader groups of people or the population as a whole; it enables researchers to compare different groups to understand similarities and differences; and it helps businesses understand the size of a new opportunity. As we can see, quantitative research is important across fields and disciplines.
Let me now briefly discuss the importance of quantitative research across fields and disciplines. But for brevity’ sake, the discussion that follows will only focus on the importance of quantitative research in psychology, economics, education, environmental science and sustainability, and business.
First, on the importance of quantitative research in psychology.
We know for a fact that one of the major goals of psychology is to understand all the elements that propel human (as well as animal) behavior. Here, one of the most frequent tasks of psychologists is to represent a series of observations or measurements by a concise and suitable formula. Such a formula may either express a physical hypothesis, or on the other hand be merely empirical, that is, it may enable researchers in the field of psychology to represent by a few well selected constants a wide range of experimental or observational data. In the latter case it serves not only for purposes of interpolation, but frequently suggests new physical concepts or statistical constants. Indeed, quantitative research is very important for this purpose.
It is also important to note that in psychology research, researchers would normally discern cause-effect relationships, such as the study that determines the effect of drugs on teenagers. But cause-effect relationships cannot be elucidated without hard statistical data gathered through observations and empirical research. Hence, again, quantitative research is very important in the field of psychology because it allows researchers to accumulate facts and eventually create theories that allow researchers in psychology to understand human condition and perhaps diminish suffering and allow human race to flourish.
Second, on the importance of quantitative research in economics.
In general perspective, the economists have long used quantitative methods to provide us with theories and explanations on why certain things happen in the market. Through quantitative research too, economists were able to explain why a given economic system behaves the way it does. It is also important to note that the application of quantitative methods, models and the corresponding algorithms helps to make more accurate and efficient research of complex economic phenomena and issues, as well as their interdependence with the aim of making decisions and forecasting future trends of economic aspects and processes.
Third, on the importance of quantitative research in education.
Again, quantitative research deals with the collection of numerical data for some type of analysis. Whether a teacher is trying to assess the average scores on a classroom test, determine a teaching standard that was most commonly missed on the classroom assessment, or if a principal wants to assess the ways the attendance rates correlate with students’ performance on government assessments, quantitative research is more useful and appropriate.
In many cases too, school districts use quantitative data to evaluate teacher effectiveness from a number of measures, including stakeholder perception surveys, students’ performance and growth on standardized government assessments, and percentages on their levels of professionalism. Quantitative research is also good for informing instructional decisions, measuring the effectiveness of the school climate based on survey data issued to teachers and school personnel, and discovering students’ learning preferences.
Fourth, on the importance of quantitative research in Environmental Science and Sustainability.
Addressing environmental problems requires solid evidence to persuade decision makers of the necessity of change. This makes quantitative literacy essential for sustainability professionals to interpret scientific data and implement management procedures. Indeed, with our world facing increasingly complex environmental issues, quantitative techniques reduce the numerous uncertainties by providing a reliable representation of reality, enabling policy makers to proceed toward potential solutions with greater confidence. For this purpose, a wide range of statistical tools and approaches are now available for sustainability scientists to measure environmental indicators and inform responsible policymaking. As we can see, quantitative research is very important in environmental science and sustainability.
But how does quantitative research provide the context for environmental science and sustainability?
Environmental science brings a transdisciplinary systems approach to analyzing sustainability concerns. As the intrinsic concept of sustainability can be interpreted according to diverse values and definitions, quantitative methods based on rigorous scientific research are crucial for establishing an evidence-based consensus on pertinent issues that provide a foundation for meaningful policy implementation.
And fifth, on the importance of quantitative research in business.
As is well known, market research plays a key role in determining the factors that lead to business success. Whether one wants to estimate the size of a potential market or understand the competition for a particular product, it is very important to apply methods that will yield measurable results in conducting a market research assignment. Quantitative research can make this happen by employing data capture methods and statistical analysis. Quantitative market research is used for estimating consumer attitudes and behaviors, market sizing, segmentation and identifying drivers for brand recall and product purchase decisions.
Indeed, quantitative data open a lot of doors for businesses. Regression analysis, simulations, and hypothesis testing are examples of tools that might reveal trends that business leaders might not have noticed otherwise. Business leaders can use this data to identify areas where their company could improve its performance.
At the outset, it must be noted that when we talk about the “strengths” of quantitative research, we do not necessarily mean that it is better than qualitative research; nor we say that it is inferior to qualitative research if we talk about its weaknesses. Hence, these strengths and weaknesses depend only on a specific purpose they serve, such as in terms of the problems or gaps that it aims to address or in terms of the time needed to complete the research. This means, therefore, that quantitative research is better than qualitative research only in some respects, and vice versa.
So, what are some of the major strengths of quantitative research?
First, in terms of objectivity and accuracy. If the issue is about objectivity and accuracy, then quantitative research is strong and more preferrable because, as we may already know, quantitative research explains phenomena according to numerical data which are analyzed by means of mathematically based methods, especially statistics. In this way, biases are reduced to the minimum and analysis and interpretations are more objective and accurate. In fact, another important point to remember in quantitative research is that it is informed by objectivist epistemology. This means that quantitative research seeks to develop explanatory universal laws, for example, in social behaviors, by statistically measuring what it assumes to be a static reality. In relative vein, a quantitative approach endorses the view that psychological and social phenomena have an objective reality that is independent of the subject, that is, the knower or the researcher and the known or subjects are viewed as relatively separate and independent. Hence, in quantitative research, reality should be studied objectively by the researchers who should put a distance between themselves and what is being studied. In other words, in quantitative research, the researcher lets the “object” speaks for itself by objectively describing rather than giving opinions about it. This explains why quantitative researchers are supposed to play a neutral role in the research process. Hence, the meaning participants ascribe to the phenomenon studied is largely ignored in quantitative studies.
Second, in terms of sample size. It must be noted that a broader study can be made with quantitative approach, which involves more subjects and enabling more generalizations of results. In fact, scholars and researchers argue that one major advantage of quantitative research is that it allows researchers to measure the responses of a large number of participants to a limited set of questions. Also, quantitative methods and procedures allow the researchers to obtain a broad and generalizable set of findings from huge sample size and present them succinctly and parsimoniously.
Third, in terms of efficiency in data gathering. In terms of data gathering, quantitative research allows researchers to use a pre-constructed standardized instrument or pre-determined response categories into which the participants’ varying perspectives and experiences are expected to fit. Hence, data gathering in quantitative research is faster and easier. In fact, data gathering in quantitative research can be automated via digital or mobile surveys which, for example, allows thousands of interviews to take place at the same time across multiple countries. As we can see, data gathering in quantitative research is efficient and requires less effort.
And fourth, in terms of cost efficiency. Since data gathering in quantitative research is efficient and requires less effort, then obviously, the cost of someone conducting quantitative research is typically far less than in qualitative research.
So much for the major strengths of quantitative research. Let me now discuss very briefly its major weaknesses.
First is that results in quantitative research are less detailed. Since results are based on numerical responses, then there is a big possibility that most results will not offer much insight into thoughts and behaviors of the respondents or participants. In this way too, results may lack proper context.
Second, because quantitative research puts too much emphasis on objectivity and accuracy, it does not consider meaning behind phenomena. Needles to say, in every phenomenon, there are always important points that cannot be fully captured by statistics or mathematical measurements. Indeed, not all phenomena can be explained by numbers alone.
Third is on the issue of artificiality. Quantitative research can be carried out in an unnatural environment so that controls can be applied. This means that results in quantitative research may differ from “real world” findings.
Fourth is that in quantitative research, there is a possibility of an improper representation of the target population. Improper representation of the target population might hinder the researcher from achieving its desired aims and objectives. Despite the application of an appropriate sampling plan, still representation of the subjects is dependent on the probability distribution of observed data. As we can see, this may lead to miscalculation of probability distribution and falsity in proposition.
Fifth, quantitative research is limiting. Quantitative research employs pre-set answers which might ask how people really behave or think, urging them to select an answer that may not reflect their true feelings. Also, quantitative research method involves structured questionnaire with close-ended questions which leads to limited outcomes outlined in the research proposal. In this way, the results, expressed in a generalized form, cannot always represent the actual occurrence or phenomenon.
And sixth is the difficulty in data analysis. Quantitative studies require extensive statistical analysis, which can be difficult to perform for researchers from non-statistical backgrounds. Statistical analysis is based on scientific discipline and, hence, difficult for non-mathematicians to perform. Also, quantitative research is a lot more complex for social sciences, education, sociology, and psychology. Effective response should depend on the research problem rather than just a simple yes or no response. For example, to understand the level of motivation perceived by Grade 12 students from the teaching approach taken by their class teachers, mere “yes” and “no” might lead to ambiguity in data collection and, hence, improper results. Instead, a detailed interview or focus group technique might develop in-depth views and perspectives of both the teachers and children.
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Quantitative research is a powerful tool for studying human behavior, attitudes, and opinions. It involves the collection and analysis of numerical data, and can be used to test hypotheses and answer specific research questions. There are several situations in which quantitative research may be an appropriate research method, including:
1. When the research question requires objective measurement:
Quantitative research is particularly useful when the research question requires objective measurement. For example, if a researcher wants to study the effectiveness of a new drug, they might use a randomized controlled trial to objectively measure the drug’s effects. Similarly, if a researcher wants to study the relationship between two variables, such as the relationship between socioeconomic status and academic achievement, they might use a correlational study to objectively measure the strength and direction of that relationship.
2. When the research question requires statistical analysis:
Quantitative research is also useful when the research question requires statistical analysis. Statistical analysis can help researchers determine whether the results they obtain are statistically significant, meaning that they are unlikely to have occurred by chance. This is particularly important in fields such as medicine and psychology, where statistical analysis is often used to determine the effectiveness of treatments or interventions.
3. When the research question requires a large sample size:
Quantitative research is often used when the research question requires a large sample size. This is because quantitative research methods, such as surveys and questionnaires, can be used to collect data from a large number of participants quickly and efficiently. For example, if a researcher wants to study the prevalence of a particular behavior, they might use a survey to collect data from a large sample of people.
4. When the research question requires generalization:
Quantitative research is also useful when the research question requires generalization. Generalization refers to the ability to make inferences about a larger population based on the results obtained from a smaller sample. For example, if a researcher wants to study the prevalence of depression in a particular population, they might use a survey to collect data from a sample of that population. The results obtained from the sample could then be generalized to the larger population.
5. When the research question requires control over variables:
Quantitative research is also useful when the research question requires control over variables. In experimental research, for example, the researcher can manipulate the independent variable and control for extraneous variables, allowing them to determine whether there is a cause-and-effect relationship between the independent variable and the dependent variable. This type of control is not possible in other research methods, such as observational studies.
In conclusion, quantitative research is a powerful tool for studying human behavior, attitudes, and opinions. It can be used in a wide range of research contexts, including when the research question requires objective measurement, statistical analysis, a large sample size, generalization, or control over variables. By carefully designing and conducting quantitative research studies, researchers can gain valuable insights into the complex and multifaceted nature of human behavior.
Quantitative research is a type of research method that involves the collection and analysis of numerical data. It is widely used in social sciences such as psychology, sociology, and education to study human behavior, attitudes, and opinions. Quantitative research can be broadly divided into four categories, including descriptive, correlational, quasi-experimental, and experimental research.
Descriptive research
Descriptive research is a type of quantitative research that involves the collection of data to describe a particular phenomenon or situation. This type of research does not involve any manipulation of variables, but rather focuses on describing the characteristics of a particular population or situation. Descriptive research can be conducted using a variety of methods, including surveys, questionnaires, and observations. Some examples of descriptive research include market research, demographic surveys, and epidemiological studies.
Correlational research
Correlational research is a type of quantitative research that examines the relationship between two or more variables. The goal of correlational research is to determine whether there is a relationship between two or more variables, and if so, to describe the nature of that relationship. Correlational research can be conducted using a variety of methods, including surveys and questionnaires, and can be used to study a wide range of topics, including education, health, and social behavior. Some examples of correlational research include studies examining the relationship between academic achievement and socioeconomic status, or the relationship between stress and health.
Quasi-experimental research
Quasi-experimental research is a type of quantitative research that involves the manipulation of an independent variable, but does not include random assignment of participants to groups. In quasi-experimental research, the researcher selects participants who are already in a particular group or who have already experienced a particular event. This type of research is often used when it is not possible or ethical to randomly assign participants to groups. Quasi-experimental research can be used to study a wide range of topics, including education, health, and social behavior. Some examples of quasi-experimental research include studies examining the effectiveness of a new teaching method or the impact of a community intervention program.
Experimental research
Experimental research is a type of quantitative research that involves the manipulation of an independent variable and the random assignment of participants to groups. The goal of experimental research is to determine whether there is a cause-and-effect relationship between the independent variable and the dependent variable. Experimental research can be used to study a wide range of topics, including education, health, and social behavior. Some examples of experimental research include studies examining the effectiveness of a new drug or the impact of a social intervention program.
In conclusion, quantitative research is a powerful tool for studying human behavior, attitudes, and opinions. The four main types of quantitative research include descriptive, correlational, quasi-experimental, and experimental research. Each of these types of research has its own strengths and weaknesses, and researchers should carefully consider the appropriate method to use for their specific research question. By carefully designing and conducting quantitative research studies, researchers can gain valuable insights into the complex and multifaceted nature of human behavior.
