DataInfrastuctionProject/data.html at main · liquidph34r/DataInfrastuctionProject · GitHub

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<!DOCTYPE html>
<html class = "all" lang = "en">
    <head>
        <meta charset="utf-8">
        <title> Data </title>
        <link rel="stylesheet" href="css/datastyles.css">
    </head>

    <body>
        <div class= "test">
        <nav>
            <ul class = "links">
                <li><a href="./index.html">Home</a></li>
                <li><a href="./topic.html">Topic</a></li>
                <li><a href="./research.html">Research</a></li>
                <li><a href="./data.html">Data</a></li>
                <li><a href="./references.html">References</a></li>
                <li><a href="./style.html">Style</a></li>
                <li><a href="./layout.html">Layout</a></li>
                <li><a href="./modality.html">Modality</a></li>
                <li><a href="./prof.html">Professionalization</a></li>
            </ul>
        </nav>
        </div>
        <div class = "main">
        <h1>
            How to organize and analyze research data
        </h1>
        <p>
            This short lesson is intended for graduate and advanced undergraduate students undertaking original research projects. After completing the 30-minute lesson, you will be able to to:
            Format raw research data in an Excel spreadsheet for efficient analysis
            Run basic descriptive statistics in Excel
            Prepare the document for statistical analysis in the software package PASW\
        </p>
            <aside class="aside">
                For this lesson, you'll need the following:
                <ul>
                    <li>Excel</li>
                    <li>PASW</li>
                </ul>
                <p>You should already be familiar with:</p>


                <ul>
                    <li>The basic functions and tools of Excel</li>
                    <li>Descriptive statistics and inferential statistics</li>
                    <li>Research design and how to identify independent variables and dependent variables</li>
                </ul>
            </aside>

        <h2>
            Format data for analysis
        </h2>
        <p>Make sure the computer you are working on has Microsoft Excel installed. Open the document ToolBox_DataLesson.xls [[[NOTE: NEED LINK TO DOWNLOAD TUTORIAL EXCEL FILE]]] in Excel and follow along with the lesson, taking action as instructed.
            In the Raw_Dataset spreadsheet, the name of each variable has been entered in the first row of each column.</p>
            <ol>
                <li>Each variable name must be different from other variable names.</li>
                <li>The first variable (in column A) is a unique identifier.</li>
                <li>Variable names must start with a letter (not numbers or special characters), so change 4tutorial_types to tutorial_types.</li>
                <li>Name variables so that they are intuitive to you. Therefore, change use and useful to perc_ease_of_use and perc_usefulness, respectively.</li>
            </ol>
            <p>Your spreadsheet should look like Figure 1, and it should now be readily apparent that Column F refers to Perceived Usefulness and Column G refers to Perceived Ease of Use.</p>
            <figure class = "figure">
                <img src="./figures/datalesson_fig3.png" alt="Figure of sheets">
                <figcaption> Tutorial type, gender, experience, and task categorical values have been formatted as dichotomous variables.</figcaption>
            </figure>
            <p>Data values, or rows, have been input for each subject in the experiment.</p>
            <ol>
                <li>Decide on input conventions and stick to them. In gender, change the "female" value to "F".</li>
                <li>Separate data into component values whenever possible by adding new columns. For example, tasks_completed values (Y/Y/N) can be broken up into three components. So, add columns task1, task2, and task3, and reformat values appropriately.</li>
                <li>Double-check to ensure no data entry errors have been made, then delete the old tasks_completed column.</li>
                <li>Your Raw_Dataset spreadsheet should now look like Figure 2.</li>
            </ol>
            <figure class = "figure">
                <img src="./figures/datalesson_fig2.png" alt="Figure of sheets">
                <figcaption>Values for each variable are entered in a consistent format.</figcaption>
            </figure>
            <aside class="aside">
                <p>Replace categorical data values with "0" or "1" (0=no, 1=yes) to indicate whether or not the value is represented for the given subject/item. This makes the categorical values dichotomous, which gives the researcher maximum flexibility in testing for relationships or correlations.</p>
            </aside>

            <ol>
                <li>Copy the Raw_Dataset values into the Sheet2 tab. Rename this sheet Formatted_Data.</li>
                <li>Rename columns according to each possible categorical value. Copy and paste the values from the original column.</li>
                <li>Replace dichotomous data values with "0" or "1" (0=no, 1=yes) using the replace function: Edit > Replace.</li>
                <li>Your Formatted_Data spreadsheet should now look like Figure 3.</li>
                <li>In the Formatted_Data sheet, add new columns with Insert > Columns for each distinct value in categorical data columns (tutorial_types, gender, exp_level, task1, task2, and task3). You do not have to add a new column for task1, task2, and task3 because their values are already dichotomous.</li>
            </ol>
            <figure class="figure">
                <img src="./figures/datalesson_fig3(1).png" alt="Figure of sheets">
                <figcaption> Tutorial type, gender, experience, and task categorical values have been formatted as dichotomous variables.</figcaption>
            </figure>
        </div>
    </body>
</html>