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14 Important Parts of Academic Report
You may need to submit multiple academic reports during your degree. Our Tutors here at Assignmentgiant.com are ready to guide you through the general features of an academic report. There are probably just a few of these elements that will be necessary for your course, and you also have other criteria that aren’t covered here. The criteria for reports might differ from one department to another, therefore if you are unclear of what you need to include in your report, you can consult your course handbook or contact your subject tutor or lecturer.
Key parts of an academic report
An academic report is different from an essay in many ways. There is no one best way to organize a report; rather, the format should be tailored to the report’s intended use. However, in most cases, academic papers include some of the components that are listed below.
Key parts of academic report includes
1. Title page
On this page, the most important details of your academic report are listed.
2. Declaration Statement
This is a document that has to be signed, and it needs to be included with any written report or essay that you submit in order to prove that the project is all your own work. These forms are available for pick-up at the office of your faculty department.
3. Abstract or Executive Summary
An abstract is a concise summary of the whole academic report, often no more than 150 words long. It should be written after everything else. In contrast to a conclusion, an abstract is required to provide a concise summary of all of the phases of the report, not simply the findings or conclusions. When writing an abstract, one of your goals should be to provide prospective readers with just enough information to let them decide whether or not they need to read the whole report.
If you’ve never written an abstract before, one strategy you may try is to write one or two phrases that summarize each component of your academic report. This is very helpful for those who are just starting out. Take a look at the abstracts or executive summaries included in the reports that may be found in the Library or on the internet to get a sense of the writing style that is employed.
There is a separate page dedicated to expressing gratitude to those individuals who have helped in any way with the assignment. In most cases, only the more substantial academic report will require an acknowledgments page.
5. Table of Contents
This should specify clearly all of the parts and subsections that make up your report, as well as the page numbers that correspond to the beginning of each of those sections. Using heads that are numbered in ascending order is a typical practice for structuring reports; however, this method is not required.
Following the Table of Contents is a separate list of any tables, charts, or diagrams that you have included in the academic report. This list follows after the conclusion of the report. The names of the tables should be “Table 1 [with the title],” “Table 2,” and so on in a sequential fashion. The names of charts and diagrams should be written out as “Figure 1 [with the title],” “Figure 2,” and so on. The page number of each table and chart should be included on this distinct list.
You should explain why the report is required and/or valuable, as well as outline the goal (aim) of the report in the opening section of the paper. Depending on what you want to accomplish with the report, you can decide to subdivide the overarching goal into several smaller goals. You should also clarify important phrases (words) that you use in the academic report so that the reader understands exactly what you mean when you use those terms. This will ensure that the report is as clear as possible.
Only in papers on primary (your own) research, such as an experiment, survey, or observation, will the following four parts typically be included. If the only source of information for your report is reading, you will most likely replace these four parts with a number of subject headers that you have selected yourself.
7. Literature review
In this area, you will discuss prior and present ways of thinking about the issue, as well as research that has been done on it. To put it another way, your academic report will consist of a summary of what other people have written about the subject. Due to the fact that you will be reporting the work of others, the literature review that you write will most likely include several in-text citations to the books and papers that you have read. It is typical practice to conclude the literature evaluation with one or more hypotheses for your own study in research that is of a more scientific nature. In many types of reports, the literature review is included into the introduction and may have a more straightforward name, such as “Background.”
8. Method(s) or Methodology or Research design
In reality, the definitions of these three terms—”method,” “methodology,” and “research design”—differ just a little bit from one another. For additional detail on this topic, check a work on research techniques. In contrast, you should explain to the reader in this part how you obtained the information that was utilized in the academic report (i.e. your methods). You may, for instance, explain an activity that you participated in or an event that you witnessed in a step-by-step fashion. In most cases, it is necessary for this description to be fairly specific. In most cases, you will also need to justify the techniques you used to acquire the data and explain why you chose to do so in the manner that you did. This justification could need a lot of specificity.
You could find it helpful to include some in-text references to published works on research methodologies to assist you in explaining the methodology you chose.
9. Results or Findings
It is at this point that you will deliver the findings of your investigation, often known as “what you found out.” Those results should not be the subject of any debate or examination. Tables and charts are frequently included in this section.
In the event that you have developed one or more hypotheses for your report, you need to specify in this part whether you can accept or reject those hypotheses.
10. Discussion of results or Analysis or Interpretation
Because it demonstrates what you think about the results, this section of the academic report is frequently considered to be the most essential element. It is expected that you will remark on your results during the conversation. This may also involve:
- Describing any patterns found in the results and offering possible explanations for them, maybe mentioning any anomalies (results that don’t ‘fit in with’ the rest of the findings).
- Providing an explanation for what you found (while referring to theory).
- Offering commentary on the extent to which your findings accord or differ with the existing body of research.
- Taking into consideration the correctness and dependability of your findings.
- Taking into consideration the repercussions that your findings may have, such as what they may signify for your clinical work.
- Engaging in a discussion on the potential future applications of more research in this field.
You should summarize the most important results from your academic report in the conclusion section. (Only imagine that you have to condense what you learned into just five or six phrases.) There should be no newly discovered information supplied. It is often beneficial to review the goal(s) and objectives stated in the introduction, and it is also possible to offer some commentary on the degree to which those goals and objectives have been accomplished.
12. Recommendations (Depends on Nature of the Report)
Recommendations are included in just few of the reports. But if your academic report is on a work-related topic or case study, and especially if the issue includes problem-solving or improving practice, it may very well be acceptable to give suggestions. This is especially true if the issue concerns improving practice. The study includes several recommendations for possible follow-up actions on the matter. In most cases, they will be recommendations that are the result of your study and that you believe would make a situation better.
13. References or Reference list or Bibliography
This is a list of the books and articles that you read for the report and that you utilized in the academic report. It is written in a very specific way. When you create a bibliography, you list all of the sources that you utilized, but when you create a reference list, you just mention the sources that you actually cited in your text.
Appendices are supplemental sections that are often added at the very end of an academic report. They are used to contain additional information. This may include copies of observation forms or notes, tables of data, or excerpts (not photocopies) from huge papers that you have referred to. They might also be any other important information that you have discussed in your academic report and to which you would like your reader to be able to refer. You should include this information in your references section. Place each source in its own individual appendix; label them Appendix A [or 1], Appendix B [or 2], and so on.
When Should I use T test?
Who can help me with my t test? When should I use t test? These are some of the questions that are asked by researchers and statistical analysts. Here at Assignmentgiant.com we shall answer the question, but first let’s begin by understanding the concept of t-test. A t test is a statistical test that is used when comparing the means of two groups. It is used when hypothesis testing to establish if a process or treatment actually has an impact on the population of interest. Through it, you can determine if two groups are different from one another.
Application of t test: example
You want to understand the difference in knee cost surgeries for the same person in two different public hospitals. You will test the difference between these two hospitals using a t test and null and alternative hypotheses.
- Null hypothesis (H0) is that the true difference between the hospital costs is zero
- Alternative hypothesis (Ha) is that the true difference between the hospital costs is different from zero.
When Should I use a T test?
A t-test can only be used when comparing the means of two groups (that is pairwise comparison). If you want to compare more than two groups or you want to carry on multiple pairwise comparison then use ANOVA test or the post-hoc test.
The t-test is a parametric test of difference. This is because it makes the same assumptions regarding your data just as the other parametric tests. The assumptions includes:
- The data are independent.
- The data are normally distributed or approximately distributed
- The data have same degree of variance within each group that is compared. There is homogeneity of variance.
In case the data does not meet the assumptions, then you can consider using the non-parametric alternatives to the t test. A good example of non-parametric alternative that would apply is the Wilcoxon Signed-Rank test for data using the unequal variances.
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Which type of t test should I use?
Before applying the t test, it is necessary to understand the right types of t test to use. Two sets of considerations or criterion are important. First, find out whether the groups being compared are from a single population or they are from two different populations. Also find out whether you want to test the difference in a given direction.
Should I use One-sample, two sample, or paired t test?
Perform a paired t test if the groups are from a single population. The groups are measuring before and after an experimental treatment. This is a within-subjects design.
Perform two-sample t test if the groups come from two different populations (two different varieties or people from two different countries). This is also referred to as the independent t test. This is the between-subjects design.
Perform one-sample t test if there is one group being compared against a standard value. For instance, comparing the performance of a student to the mean score mark of 50%.
One-tailed t test Vs. Two-tailed t-test
If your concern is whether the two populations are different from one another, then do two-tailed t test.
If your concern is to find out if one population mean is greater than or less than the other then do one-tailed t test.
Example of t test
In your test of whether the knee surgery costs differ by hospitals:
Your observations are derived from two separate populations (different types of hospitals) so you perform a two-sample t test.
You do not consider the direction of the difference, only whether there is a difference, so you choose a two-tailed t test.
Statistical Analysis in 5 steps: A step by step guide
Who can help me with statistical analysis? Worry no more, experts at Assignmentgiant.com are ready to heed to your call.
Statistical analysis refers to the ways through which different trends, patterns and relationships are established through quantitative data. Statistical analysis is used by different institutions to understand the existing trends, the patterns, and relationships. Some of the institutions that use them include research experts, scientists, governments, businesses, NGOs.
To attain meaningful conclusions, statistical analysis must include proper planning right from the beginning of the research process. The first process is understanding the hypothesis and the mentioning the hypotheses. A decision needs to be made concerning the research design, sample size and sampling procedure to be applied.
After data collection from the identified sample, you can opt to organize and summarize the data using descriptive statistics. The next step is using inferential statistics to formally test hypotheses and make estimates regarding the population. The last phase is interpretation and generalization of the findings.
This step by step guide is a real introduction for beginners or even middle level analysts who may include students and research personnel. Each step detailed herein has examples. The examples are experimental and correlational. The experimental ones establishes the likely cause-and-effect relationships between the variables. On the other hand, the correlational investigates the potential correlation between the different variables.
Step 1: Research hypotheses and research design
In this first step, as a researcher, you have to write your hypotheses and plan the research design. The specification of the two will help in collection of valid data.
Write the Statistical Hypotheses
Many often ask, “what is the goal of research?” The primary objective of any research is to establish a relationship between variables in the target population. You commence by prediction and then apply statistical analysis to test the prediction. Statistical hypothesis is a formal means of writing a prediction regarding a given population. Begin by rephrasing the research prediction into null and alternative hypotheses. The rephrasing into the hypotheses must be done in a way that can be easily tested using sample data.
The null hypothesis predicts existence of no effect or no relationship between variables. Alternative hypothesis states the research prediction of a relationship or an effect.
Example of Hypotheses: Statistical hypotheses to test an effect
Null hypothesis: Drinking 5 liters of water has no effect on hydration levels of teenagers
Alternative hypothesis: Drinking 5 liters of water improves hydration levels of teenagers
Example of Hypotheses: Statistical Hypotheses to test a correlation
Null hypothesis: Student’s comprehension level and their GPA scores have no relationship
Alternative hypothesis: Student’s comprehension level and the GPA scores are positively correlated
Plan the Research Design
The research design is the overall strategy used in data collection and analysis. It determines the statistical tests that one can apply to test their chosen hypothesis later.
It is at this point that you will have to decide if your research will use descriptive, correlational or experimental research design.
The experiments done directly impacts the variables, while descriptive and correlational studies helps in measuring variables.
Experimental Design Vs. Correlation Design Vs. Descriptive Design
- Experimental Design- Helps you to establish the cause and effect relationship using statistical tests of comparison or regression (e.g The effect of drinking water and hydration levels).
- Correlational Design- Through it, you can explore the relationships between variables and this is done without assuming existence of causality. It happens through correlation coefficients and significance tests (e.g student’s comprehension level and GPA scores).
- Descriptive Design- Entails studying the features of the target population or phenomenon using statistical tests and then drawing inferences from the sample data (the prevalence of malaria in tropical regions)
The choice of the research design is also determined with whether you will need to compare participants at the group level or individual level or use both.
The between-subjects design– In this case a comparison of the outcomes is done at the group-level and includes those who were subjected to different treatments (students who drink 5litres of water vs. those who do not drink 5 liters of water).
Within-Subjects design– There is comparison of repeated measures from the different participants who have been in all the treatments of the study (the hydration levels of the students before and after drinking 5 liters of water).
Mixed (Factorial) Design– In this case, there is alteration of one variable between subjects and another is also altered within the subjects (there is establishing pretest and post test scores from the various students who did or did not take the 5 liters of water).
Decide on how to measure the Variables
When planning the research design, the next step should be about operationalization of the variables. At this point you are deciding how the variables will be measured.
In statistical analysis, it is necessary to consider the level of measurement for the variables. The consideration tells the type of data that is contained. The measurements can be two:
Categorical- This will represent the groupings. They may be nominal e.g gender or they may be ordinal like the class level.
Quantitative- This represents the amounts. They may be interval scale e.g (test score) or ratio scale (age).
The variables may be measured at varied levels of precision. For instance, age data may be quantitative (10 years old) or may be categorical (young). For instance, coding a variable numerically like level of agreement in scale of 1 to 5, it does not imply that it is automatically quantitative and not categorical.
Identification of the measurement levels is necessary when one is selecting the right statistics and hypothesis tests. For instance, you can calculate the mean score with quantitative data and not using categorical data. However, the same categorical data may be used to establish the modal score.
In any research study, along with the measures of the variables of interest, you will collect data on relevant participant features.
Step 2: Data Collection from the Decided Sample
In several scenarios, it is often cumbersome to collect data from every member of the population that the study is interested in. Therefore, data is collected from the sample and as such it needs to be representative of the population.
Sampling for statistical analysis
Two primary approaches exist to be used in selection of the sample.
- Probability Sampling- In this case, each member of the population stands a chance of being selected for the study and this is done through random selection.
- Non-probability sampling- There are certain members of the population who stand a chance of being selected than others and this is because the criteria set may be convenience or voluntary.
In theory, for the findings that are generalized, you should consider probability sampling method. Using the random selection method reduces several forms of bias that may arise like the sampling bias. It also ensures that data from the sample selected is fully representing the population. The study may require that parametric tests be used to arrive at strong statistical inferences whenever data is collected using the probability sampling technique.
Therefore, it is never easy to arrive at an ideal sample. Even though the non-probability samples are more likely to be risked for biases such as self-selection bias, it is easier for them to recruit and collect data from them. The non-parametric tests are often very appropriate to be used in non-probability samples, though they often end up giving weaker inferences regarding the population.
If you need to use the parametric tests for non-parametric tests for the non-probability samples, then you must hold that:
- Your sample is representative of the population you are generalizing your findings to.
- The sample lacks systematic bias.
External validity means that you can generalize the conclusions to the others who share the characteristics of your sample. The results from the college students in U.S may not be generalized to all students globally because of the varied characteristics and demographics.
In case the parametric tests is applied to data from non-probability samples, it is necessary to elaborate on the extent to which the results may be generalized. The elaboration is done in the discussion section.
Come up with the right sampling procedure
In accordance to the resources that are available for the research, it is important to decide on the best way to recruit the participants.
Are there resources to market the study and have it done in several areas? Outside the nearest setting?
Are there means of getting diverse sample representing a broad population?
Is there time for contacting and following up with members of groups that are not easily reached?
|Variable||Type of data|
|Race or ethnicity||Categorical (nominal)|
|Baseline test scores||Quantitative (interval)|
|Final test scores||Quantitative (interval)|
Calculate sufficient sample size
Your sample size should be determined before you begin recruiting participants. You may do this by looking at existing studies that have been conducted in your field or by utilizing statistics. If you take too little of a sample, the results may not be reflective of the whole, but if you take too large of a sample, the costs will be higher than they need to be.
On the internet, you may find a lot of different sample size calculators. Different formulae are utilized based on whether or not subgroups are included in the study as well as the level of scrutiny that should be applied (e.g., in clinical research). As a general rule, there must be at least 30 units in each subgroup in order for it to be considered valid.
To use these calculators, you have to understand and input these key components:
- Significance level (alpha): the percentage of false null hypotheses that you are ready to risk rejecting; in most cases, this value is fixed at 5%.
- Statistical power: the likelihood that your research will find an impact of a particular magnitude if such an effect exists; this probability should be at least 80% in most cases.
- Expected effect size: a standardized estimate of the size of the predicted outcome of your investigation, which is typically based on the results of previous studies that are comparable.
- Population standard deviation: an estimate of the parameter for the population based on a prior research or on a pilot study that you conducted on your own.
Prevention of obesity
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