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Quantitative Analysis

When you are collecting numerical data to answer your research question (not just for demographic purposes) then you are using a quantitative design. Often, this data is generated from surveys, tests, or from archival data of test scores, attendance data, or other information on your population of interest. This data will be examined using statistical analysis. This page will provide you with key information on collecting and cleaning data, and running the statistical analyses needed to answer your research questions. I encourage you to read each of the sections below in order to help you build a strong project that can produce meaningful data. 

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If you have already read through all the information and are looking for something specific, you can click on a specific step below to take you directly to that section. 

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Quantitative Research Questions

Quantitative research looks at relationships between variables, differences between groups, and changes or impacts that occur due to an intervention or other experience. Research questions for quantitative studies should reflect this. The terms "relationship", "difference", "change/impact", or "effect" will likely be in your research question.  

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Here is a great video that highlights the differences between quantitative and qualitative research.

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Statistics: The Bare Bone Background

Statistics really comes down to two things: probability and variability. Don't panic. You already know what these are. First, probability. How likely it is for something to happen.  You get data and look at how common something is. Lots of the time we actually use the average (or mean) for this.  It tells us what "score" was common.  

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Second, variability.  This tells us what would happen if we got a second set of data.  Would we get the same average score? Of course not (well, if you are working with humans at least).  It would be weird to get the same scores. Humans are not robots. They, ready for it...VARY from each other.  Okay, so how much would we expect one sample to differ from another?  A lot? A little?  Now, split your data into two groups.  What are the means for those two groups?  Does the difference between them exceed the difference we would expect between two samples of data?  They do?  Wow, that's a big deal then.  We might even call it significant...

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Keep re-reading this as you go through a stats course or analyzing your data. Yes, I know, I only talked about splitting data into two groups. Use your imagination.  What if it was three groups, or a relationship between variables?  What would you expect (based on standard variability/deviation) and what did you find?  Compare them and you will have your answer. 

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For a quick explaination of what statistics are what they are doing check out this video. 

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Inferential Statistics Are... 

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For more in depth explanation of how statistics work, watch the following three video in order:

  1. Background, Part 1

  2. The Dance of the p-values

  3. Background, Part 2

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Collecting and Cleaning Data

To collect data, you need to determine a few things. First, who are your participants, and importantly how  many of them do you need. After this, you need to know how you will collect the data. This involves choosing instruments (e.g., surveys, tests, metrics) that will give you the quality data you need to answer your specific research question. Lastly, when you have the data, you need to 'clean' it to ensure it is numerically coding in an acceptable way to allow you to analyze it in a statistical software program. I provide details on each of these below. 

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Part 1: Sample Size

A good sample size is necessary for any research project. when doing quantitative research, the quality of the statistical results are influenced directly by your sample size. Therefore, it is important to know how many people you need in your sample.  To determine this, you will run a power analysis. Click here to see a tutorial on using G*Power, a free tool for running a PowerAnalysis.

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To do a power analysis, you need to know the type of statistical test you will run. you can determine this by consulting your textbook and notes from your quantitative research course. You can also schedule a time to talk with me and we can determine the types of tests together. 

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Part 2: Instruments

It is tempting to make your own instrument. Do not do this. Look in the literature to find surveys or tests that already exist and have validity and reliability information about them. If you make a new instrument it will not have any reliability or validity testing; doing that testing is a project in and of itself. The process for this includes generating questions, testing those questions with experts in the field to refine the language, then piloting the instrument with a small group of the target population to look for initial pit falls, followed by a full scale research study with a large sample to examine the reliability and validity. Those are measured with extensive statistical analysis. Refinements are made based on these statistical results. Only then do you have a final instrument that is considered to have some level of validity and reliability. It's a long process. Find an instrument that has already been developed this way.

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You can find instruments by looking in the methods sections of articles that have done research with the variable you are interested in examining. For example, if you are looking at student motivation, and you cite Eccles and Wigfield's research on this, go find the surveys they have developed (meaning they tested them for validity and reliability). You will need to find a copy of the survey, or a copy of the questions inside the article (sometimes they are in a table that shows average scores for each question). Then find the article that states statistics on the validity and reliability of the instrument. It takes some digging, but it is worth it as you will get data that is reliable. 

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After collecting your data, you will need to check for reliability among the data you have.  This is straightforward process. Here is a video that will show you how to do this in JASP: Reliability Analysis (go to minute 13:00)

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Part 3: Download and cleaning data

You data will need to be in spreadsheet form. Any questions you wish to run statistical analysis on will need to be in numeric form. Your survey platform (SurveyMonkey, GoogleForm, etc.) will not necessarily save everything in number form. For that reason, you will have to go into the spreadsheet and edit things to be numbers. This handout will walk you through that process. We can also schedule a Zoom session together where I can go through this process with you. 

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Statistical Software

To do statistical analysis, you need appropriate software. I suggest you use JASP. My tutorial videos below are all done with JASP.  Here are some videos on how to use JASP:

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Video Tutorials of Different Statistical Analyses

Now that you have data from a sufficient sample, using valid and reliable measures, downloaded and cleaned your data and reviewed how to use JASP, you are ready to analyze you data. You will need to follow the plan of statistical tests that you created before running your power analysis. If you would like to review that plan (they have a way of going in one ear and out the other) or work on developing a plan you can schedule a time to talk with me. 

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Here is a collection of videos that you can reference to help you run different statistical tests. 

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Writing Up Your Results

Quantitative results will be written in chapter 4. In this section, refrain from discussing what the results mean, that's for Chapter 5. Chapter 4 is just about the facts.

 

Open with a brief summary of what you were after and the type of study you conducted. Then present the findings. It will need to include (and I suggest in this order):

  • results of your reliability analysis (see video above).

    • You do not need any tables in your document for this. Just a simple statement that you ran reliability, what questions were included, and what the statistic was ("alpha=.56". Find the "alpha" symbol in "insert symbols" in Word). You can conclude with whether this was acceptable or reliability. If you are unsure of whether it is acceptable, send me an email so that we can discuss it. ​

  • descriptive statistics for demographic variables: table/graphs and a written summary (this can go in chapter 3 or 4--ask your advisor for their preference). 

  • descriptive statistics for the key variables in your research questions.

    • The tables and graphs for this will be generated when you run the statistical test in JSAP. For example, if you are examining motivation among students, and you have a survey with three subscales, make a table that has the sample size (N), mean, standard deviation, and 95% confidence interval for the total score and each subscale. this will results in a table that that has: 

      • five column: N, M, SD, Lower 95% CI, Upper 95% CI

      • and four rows of data: total scores, subscale 1, subscale 2, subscale 3, subscale 4​

       You may want to include some graphs​ as well. For example, if you are comparing the motivation scores by           students' year in school, you could make an error bar chart of the subscale scores with four bars: one for             each year in school (first year, sophomore, junior, senior). There is a difference between a table and                     figure.Also, APA requires different title formatting for tables and figures. Please review the differences here,         an example of an APA format of a table title here, and of a figure title here.   â€‹

  • written narrative of the descriptive statistics for each key variables. ​​

    • After you past the tables and/or graphs into your dissertation, write a short paragraph above each explaining what the images are telling us. Remember, just the facts (YES: "This table shows that first year students have the highest motivation M=4.67(.23) and it decreases each year ending at M=2.45(.43) in the senior year"). You do not have to get into detailed discussion about what this means (NO: "This means we need to help students not loose their motivation they enter in with at the start of college"). If those thoughts come to you, make a note of them for chapter 5.  â€‹

    • Notice how I wrote out the mean and standard deviation above, M=2.45(.43). The mean ALWAYS goes after "M=" and the standard deviation goes in parentheses "(.43)" immediately after it. No space between the mean and the parentheses. 

  • written narrative of each statistical test run (chi-squares, t-tests, ANOVAs, correlations, regressions). Review your notes from EDU807 for more detail on this. You can also review information here about how to do this. If needed, please schedule a time to speak with me and we can go through it together. 

  • summary. Remember, just the facts. Short and sweet here (one-two paragraphs). 

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