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1/19 Quantitative data analysis

❶Fahrenheit degrees Remember that ratios are meaningless for interval data. You should start with a burning question and then narrow it down more to make it manageable enough to be researched effectively.

Analyze Quantitative Data

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Surveys — Written questionnaires and open ended surveys about ideas, perceptions, and thoughts are other ways by which you can collect data for your qualitative research. For example, in your study of second career schoolteachers, perhaps you decide to do an anonymous survey of teachers in the area because you're concerned that they may be less forthright in an interview situation than in a survey where their identity was anonymous.

There are lots of different kinds of documents, including "official" documents produced by institutions and personal documents, like letters, memoirs, diaries and, in the 21st century, social media accounts and online blogs.

For example, if studying education, institutions like public schools produce many different kinds of documents, including reports, flyers, handbooks, websites, curricula, etc. Maybe you can also see if any second career teachers have an online meet group or blog. Document analysis can often be useful to use in conjunction with another method, like interviewing.

Once you have collected your data, you can begin to analyze it and come up with answers and theories to your research question. Although there are a number of ways to analyze your data, all modes of analysis in quantitative research are concerned with textual analysis, whether written or verbal. Start out with a pre-set list of codes that you derived from your prior knowledge of the subject. For example, "financial issues" or "community involvement" might be two codes you think of after having done your literature review of second career teachers.

You then go through all of your data in a systematic way and "code" ideas, concepts and themes as they fit categories. You will also develop another set of codes that emerge from reading and analyzing the data.

For example, you may see while coding your interviews, that "divorce" comes up frequently. You can add a code for this. Coding helps you organize your data and identify patterns and commonalities. Descriptive statistics help describe, show or summarize the data to highlight patterns. For example, if you had principal evaluations of teachers, you might be interested in the overall performance of those students. Descriptive statistics allow you to do that. Essentially, you are trying to make sense of the object of study and bring to light some sort of underlying coherence.

Put differently, you try to identify structures and patterned regularities in the verbal or written text and then make inferences on the basis of these regularities. Write up your research. When preparing the report on your qualitative research, keep in mind the audience for whom you are writing and also the formatting guidelines of the research journal you wish to submit your research to. You will want to make sure that your purpose for your research question is compelling and that you explain your research methodology and analysis in detail.

How do I construct a research question on reading culture among school children? First, you must determine the children's geographical background to find out their language capacity. For instance, if you are focusing on the English language, you need to know whether it is the children's native language or second language. The next step is to find out or look for a proper strategy for reading. There are lots of different models and strategies, but once again, these depend on your subjects' geographical background.

Not Helpful 1 Helpful I've been using Survey Monkey, and plan to use it more frequently as I move forward with the study. The app breaks down the information nicely, as far as how many respondents answered a certain way, number of total responses, etc. Not Helpful 0 Helpful 6. Record the interview you can download apps on to your phone to do this , and take notes of any common themes or relevant ideas as you listen to the interview. Not Helpful 2 Helpful 4. How do I construct a research study about the importance of a travel agency?

Not Helpful 4 Helpful 4. It's wise to research a career, so you understand all the details and, that way, you know all the things involved in the career. Not Helpful 0 Helpful 0. How do I start with conducting research about studying the dynamics of a hook-up between two people? Answer this question Flag as How do I state a step by step ways of collection and analysis of data using qualitative research on leadership and development challenge in Nigeria? How do I do a study on church attendance?

How can I conduct qualitative research on perceptions about happiness in mental health professionals? Include your email address to get a message when this question is answered. Already answered Not a question Bad question Other. See the Research Ready: Scales of Measurement module for more information on the scales of measurement. This is an important first step because it will help you determine how best to organize the data. It can be difficult to identify patterns or visualize what the data is showing if you are just looking at raw data.

Following is a list of commonly used descriptive statistics:. It is now apparent why determining the scale of measurement is important before beginning to utilize descriptive statistics.

For example, nominal scales where data is coded, as in the case of gender, would not have a mean score. Therefore, you must first use the scale of measurement to determine what type of descriptive statistic may be appropriate. The results are then expressed as exact numbers and allow you to begin to give meaning to the data. For some studies, descriptive statistics may be sufficient if you do not need to generalize the results to a larger population. For example, if you are comparing the percentage of teenagers that smoke in private versus public high schools, descriptive statistics may be sufficient.

However, if you want to utilize the data to make inferences or predictions about the population, you will need to go anther step farther and use inferential statistics. Inferential statistics examine the differences and relationships between two or more samples of the population.

These are more complex analyses and are looking for significant differences between variables and the sample groups of the population.

Inferential statistics allow you test hypotheses and generalize results to population as whole. Following is a list of basic inferential statistical tests:. Finally, the type of data analysis will also depend on the number of variables in the study. Studies may be univariate, bivariate or multivariate in nature. The most common descriptives used are:. Depending on the level of measurement, you may not be able to run descriptives for all variables in your dataset.

The mode most commonly occurring value is 3, a report of satisfaction. By looking at the table below, you can clearly see that the demographic makeup of each program city is different. You can also disaggregate the data by subcategories within a variable. This allows you to take a deeper look at the units that make up that category.

In the table below, we explore this subcategory of participants more in-depth. From these results it may be inferred that the Boston program is not meeting the needs of its students of color. This result is masked when you report the average satisfaction level of all participants in the program is 2.

In addition to the basic methods described above there are a variety of more complicated analytical procedures that you can perform with your data. These types of analyses generally require computer software e. We provide basic descriptions of each method but encourage you to seek additional information e. For more information on quantitative data analysis, see the following sources: A correlation is a statistical calculation which describes the nature of the relationship between two variables i.

An important thing to remember when using correlations is that a correlation does not explain causation. A correlation merely indicates that a relationship or pattern exists, but it does not mean that one variable is the cause of the other. An analysis of variance ANOVA is used to determine whether the difference in means averages for two groups is statistically significant.

For example, an analysis of variance will help you determine if the high school grades of those students who participated in the summer program are significantly different from the grades of students who did not participate in the program. Regression is an extension of correlation and is used to determine whether one variable is a predictor of another variable. A regression can be used to determine how strong the relationship is between your intervention and your outcome variables.

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Quantitative data can be analyzed in a variety of different ways. In this section, you will learn about the most common quantitative analysis procedures that are used in small program evaluation. You will also be provided with a list of helpful resources that will assist you in your own evaluative efforts.

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1/19 Quantitative data analysis. First of all let's define what we mean by quantitative data analysis. It is a systematic approach to investigations during which numerical data is collected and/or the researcher transforms what is .

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A simple summary for introduction to quantitative data analysis. It is made for research methodology sub-topic. In quantitative data analysis you are expected to turn raw numbers into meaningful data through the application of rational and critical thinking. Quantitative data analysis may include the calculation of frequencies of variables and differences between variables. A quantitative approach is usually.

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A source of confusion for many people is the belief that qualitative research generates just qualitative data (text, words, opinions, etc) and that quantitative research generates just quantitative data (numbers). Sometimes this is the case, but both types of data can be generated by each approach. This depends on the volume and type of data you are gathering from your quantitative research. Also, analysis should be already thought out and model created even before research is conducted so that you begin with the end in mind.