The Quantitative Section consists of two questions types and these question types are interspersed with one another. The first is called problem-solving and this is just ordinary five-answer multiple choice. Now you have seen this on standardized test, maybe the ACT or the SAT.
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By the end of this section, you will be able to compare effective note-taking strategies for quantitative courses against those for other courses. You will be able to identify strategies for reading quantitative texts. You will also learn how to i dentify math study strategies for before, during, and after taking quantitative classes.
Learning Objectives. By the end of this section, you will be able to: Describe how personal attitudes toward quantitative courses can impact success. Compare effective note-taking strategies for quantitative courses against those for other courses. Identify strategies for reading quantitative texts.
Nov 16, 2021 · The Quantitative Reasoning section assesses basic mathematical skills, understanding of elementary math concepts, and the ability to reason quantitatively. First things first, don't worry, unless...
May 09, 2022 · The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem. The methods section should be presented in the past tense.
Quantitative researchers believe there is an objective reality, which can be measured. "Objective" here means that the researcher is not relying on their own perceptions of an event. S/he is attempting to gather "facts" which may be separate from people's feeling or perceptions about the facts. These facts are often conceptualized as "causes" and "effects." When you ask research questions or pose hypotheses with words in them such as "cause," "effect," "difference between," and "predicts," you are operating under assumptions consistent with quantitative methods. The overall goal of quantitative research is to develop generalizations that enable the researcher to better predict, explain, and understand some phenomenon.
Students sometimes want to avoid doing quantitative research because of fear of math/statistics, but if their questions call for that type of research, they should forge ahead and use it anyway.
The third method of quantitative data collection is the use of already-existing artifacts. With this method, you choose certain artifacts (e.g., newspaper or magazine articles; television programs; webpages) and code their content, resulting in a count of whatever you are studying. With this data collection method, researchers most often use what is called quantitative content analysis. Basically, the researcher counts frequencies of something that occurs in an artifact of study, such as the frequency of times something is mentioned on a webpage. Content analysis can also be used in qualitative research, where a researcher identifies and creates text-based themes but does not do a count of the occurrences of these themes. Content analysis can also be used to take open-ended questions from a survey method, and identify countable themes within the questions.
Planning your study is one of the most important steps in the research process when doing quantitative research. As seen in the diagram below, it involves choosing a topic, writing research questions/hypotheses, and designing your study. Each of these topics will be covered in detail in this section of the chapter.
There are three possible types of studies you may choose to do, when embarking on quantitative research: (a) True experiments, (b) quasi-experiments, and (c) non-experiments.
The third step in planning your research project, after you have decided on your topic/goal and written your research questions/hypotheses, is to design your study which means to decide how to proceed in gathering data to answer your research question or to test your hypothesis.
The third type of question is a relationship/association question or hypothesis , and will often have the word "relate" or "relationship" in it, as the following example does: "There is a relationship between number of television ads for a political candidate and how successful that political candidate is in getting elected." Here the independent (or predictor) variable is number of TV ads, and the dependent (or criterion) variable is the success at getting elected. In this type of question, there is no grouping being compared, but rather the independent variable is continuous (ranges from zero to a certain number) in nature. This type of question can be worded as either a hypothesis or as a research question, as stated earlier.
In the past, when some people get a problem wrong, they might have thought that they just don’t have the ability to study math–that they’re not math people. But when you talk to professional mathematicians, the people who are best at math, it turns out that they work a long time on the same problem–and they only spend their ...
Many students work hard in math classes—studying long hours, nights and weekends—yet many of them do so using ineffective strategies. Others simply withdraw effort soon after the course begins, or they make mistakes. To help you successfully complete your academic goals, we want you to both persist in your studying and attendance ( tenacity) and to do so efficiently and effectively ( good strategies ). This is called productive persistence. The next few pages of this course will help you develop a plan for how you can implement the idea of productive persistence as an effective way of passing a math course.
Sometimes we need to try a homework problem over and over before we understand how to find a solution. In our hectic lives, it is important for us to allow ourselves time to reflect on the messy parts so we can tidy them up in our minds.
The last thing she wants to do is prepare for class when she gets home from work at night. Despite her exhaustion, she takes 15-20 minutes before bed to check the syllabus from her math class to see what topic will be covered in lecture the next day. She then finds the text material related to the lecture topic and quickly skims it, reading over the headings.
Preparation. Prepare for your classes as you would practice for an upcoming athletic event. Know the topics you are going to cover, and make a goal of being current with your assignments . Learning is not passive, so if you want to learn from your class time, you must prepare yourself to learn.
Cornell notes —good old “C Notes”—can be helpful, but require some deeper thinking than you may be able to do on the fly during a lecture. Consider saving C notes for use while reading your textbook, instead. If you don’t understand something, write it down anyway and mark clearly that you have a question about it.
There are many popular styles of note-taking and if you have one you prefer, there is no reason to change. If you want to explore more ideas that have worked well for other students in math courses, consider these:
Arithmetic . The most basic sub-section of the GRE Quantitative Reasoning section is comprised of questions that deal with only arithmetic. This should be a strong suit. After all, you've been performing arithmetic since the first days of school. Addition, subtraction, multiplication, and division are all covered here.
The Quantitative Reasoning section assesses basic mathematical skills, understanding of elementary math concepts, and the ability to reason quantitatively. First things first, don't worry, unless you are taking the GRE exam especially for math or physics, you probably won't see much college-level math. For business and economics students who are using the GRE, instead of a more specialized exam like the GMAT, that's a big relief. Of course, for some of us, high school might have been the last time we excelled in math. In that case, the GRE Quantitative Reasoning section could feel intimidating. Will this lesson put all those fears to rest and provide a thorough review of all the subject matter? Absolutely not. We've only got about five minutes. However, it will give you a good introduction to the material on the exam, allowing you to start your own study plan that will help you best address your weaknesses.
First things first, don't worry, unless you are taking the GRE exam especially for math or physics, you probably won't see much college-level math. For business and economics students who are using the GRE, instead of a more specialized exam like the GMAT, that's a big relief.
The methods section of a quantitative study should describe how each objective of your study will be achieved. Be sure to provide enough detail to enable the reader can make an informed assessment of the methods being used to obtain results associated with the research problem.
Introduction. The introduction to a quantitative study is usually written in the present tense and from the third person point of view. It covers the following information: Identifies the research problem -- as with any academic study, you must state clearly and concisely the research problem being investigated.
Your goal in conducting quantitative research study is to determine the relationship between one thing [an independent variable] and another [a dependent or outcome variable] within a population. Quantitative research designs are either descriptive [subjects usually measured once] or experimental [subjects measured before and after a treatment]. A descriptive study establishes only associations between variables; an experimental study establishes causality.
Among the specific strengths of using quantitative methods to study social science research problems: Allows for a broader study, involving a greater number of subjects, and enhancing the generalization of the results; Allows for greater objectivity and accuracy of results.
NOTE: When using pre-existing statistical data gathered and made available by anyone other than yourself [e.g., government agency], you still must report on the methods that were used to gather the data and describe any missing data that exists and , if there is any, provide a clear explanation why the missing data does not undermine the validity of your final analysis.
A descriptive study establishes only associations between variables; an experimental study establishes causality. Quantitative research deals in numbers, logic, and an objective stance.
Basic Research Design for Quantitative Studies. Before designing a quantitative research study, you must decide whether it will be descriptive or experimental because this will dictate how you gather, analyze, and interpret the results.
The overarching aim of a quantitative research study is to classify features, count them, and construct statistical models in an attempt to explain what is observed.
A descriptive study is governed by the following rules: subjects are generally measured once; the intention is to only establish associations between variables; and, the study may include a sample population of hundreds or thousands of subjects to ensure that a valid estimate of a generalized relationship between variables has been obtained.
In quantitative research, your goal is to determine the relationship between one thing (an independent variable) and another (a dependent or outcome variable) in a population. Quantitative research designs are either descriptive (subjects usually measured once) or experimental ...
Quantitative research deals in numbers, logic and the objective, focusing on logic, numbers, and unchanging static data and detailed, convergent reasoning rather than divergent reasoning. The data is usually gathered using more structured research instruments. The results are based on larger sample sizes that are representative of the population.
Summary of findings – synthesize the answers to your research questions. Do not report any statistical data here; just provide a narrative summary of the key findings and describe what learned that you did not know before conducting your study.
Limitations -- describe any limitations or unavoidable bias in your study and, if necessary, note why these limitations did not inhibit effective interpretation of the results. Conclusion. End your study by to summarizing the topic and provide a final comment and assessment of the study.
1. Review math basics. The main math concepts tested on the GMAT are relatively simple – arithmetic, algebra, geometry – but you probably haven’t studied them since high school. Your GMAT prep will get nowhere if you don’t first review basic concepts in these three areas.
Evaluate the statements one at a time: Check out the first statement, and cover up the second statement if you have to. If you determine that the first statement is sufficient, then you can knock off the second, third, and fifth answer choices.
Many people agree that one of the most difficult things about the GMAT Quantitative section are the Data Sufficiency questions. You’ve likely never seen questions like these before. They take a little getting used to, but the more you practice them, the easier they become.
If you are having trouble with geometry questions about angles, you need to practice geometry questions about angles. Work on as many questions like this as you can find.
The GMAT Quantitative section is enough to make any MBA cringe. But data sufficiency and other GMAT math problems can easily be overcome with these GMAT prep tricks from Varsity Tutors. Oh, the Quantitative section of the GMAT. It’s enough to make the most numbers-happy MBA cringe.
Obviously, there are many mathematical topics that you need to understand in order to score well on the Quant section of the GMAT – but taking GMAT practice tests is just as important in order to achieve this.
In fact, creating a spreadsheet will do more to prepare you for business school than anything that the GMAT tests!
Another way to collect quantitative data is through questionnaires and surveys. Nowadays, it’s easy to create a survey and distribute it online—with tools like Typeform, SurveyMonkey, and Qualtrics, practically anyone can collect quantitative data. Surveys are a useful tool for gathering customer or user feedback, and generally finding out how people feel about certain products or services. To make sure you gather quantitative data from your surveys, it’s important that you ask respondents to quantify their feelings—for example, asking them to rate their satisfaction on a scale of one to ten.
But, to summarize, the differences between quantitative and qualitative data are as follows: Quantitative data is countable or measurable, relating to numbers; qualitative data is descriptive, relating to words. Quantitative data lends itself to statistical analysis; qualitative data is grouped and categorized according to themes.
Discrete data is quantitative data that can only take on certain numerical values. These values are fixed and cannot be broken down. When you count something, you get discrete data. For example, if a person has three children, this is an example of discrete data. The number of children is fixed—it’s not possible for them to have, say, 3.2 children. Another example of discrete quantitative data could be the number of visits to your website; you could have 150 visits in one day, but not 150.6 visits. Discrete data is usually visualized using tally charts, bar charts, and pie charts.
The main advantages of working with quantitative data are as follows: Quantitative data is relatively quick and easy to collect, allowing you to gather a large sample size. And, the larger your sample size, the more accurate your conclusions are likely to be . Quantitative data is less susceptible to bias.
Examples of quantitative data include numerical values such as measurements, cost, and weight; examples of qualitative data include descriptions (or labels) of certain attributes, such as “brown eyes” or “vanilla flavored ice cream”. Now we know the difference between the two, let’s get back to quantitative data.
Continuous data, on the other hand, can be infinitely broken down into smaller parts. This type of quantitative data can be placed on a measurement scale; for example, the length of a piece of string in centimeters, or the temperature in degrees Celsius. Essentially, continuous data can take any value; it’s not limited to fixed values. What’s more, continuous data can also fluctuate over time—the room temperature will vary throughout the day, for example. Continuous data is usually represented using a line graph.
Qualitative data cannot be used for statistical analysis; to make sense of such data, researchers and analysts will instead try to identify meaningful groups and themes.
This process can be used for any type of problem, but the quantitative reasoning comes in at steps two and three when we devise a plan and carry it out. Knowing how to identify the relationships among the quantities in the problem and connect those relationships to appropriate operations is quantitative reasoning at its finest. Consider another example.
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Many standardized tests have a quantitative reasoning section. Tackling these types of problems can be done using a number of strategies. First and foremost, when dealing with any type of quantitative reasoning problem, it's a good idea to have a plan.