{"id":9,"date":"2019-04-09T16:57:08","date_gmt":"2019-04-09T16:57:08","guid":{"rendered":"https:\/\/wp.media.unc.edu\/biostatistics\/?page_id=9"},"modified":"2020-08-10T14:58:31","modified_gmt":"2020-08-10T14:58:31","slug":"lesson-1-data-characteristics","status":"publish","type":"page","link":"https:\/\/wp.media.unc.edu\/biostatistics\/lesson-1-data-characteristics\/","title":{"rendered":"Lesson 1: Data Characteristics"},"content":{"rendered":"<div class=\"\"><ul class=\"nav nav-tabs\" id=\"oscitas-tabs-0\"><li class=\"active\"><a class=\"\" href=\"#pane-0-0\" data-toggle=\"tab\">Introduction<\/a><\/li><li class=\"\"><a class=\"\" href=\"#pane-0-1\" data-toggle=\"tab\">Terminology<\/a><\/li><li class=\"\"><a class=\"\" href=\"#pane-0-2\" data-toggle=\"tab\">Basic Concept<\/a><\/li><li class=\"\"><a class=\"\" href=\"#pane-0-3\" data-toggle=\"tab\">Reported Results<\/a><\/li><li class=\"\"><a class=\"\" href=\"#pane-0-4\" data-toggle=\"tab\">Assessment<\/a><\/li><\/ul><div class=\"tab-content\"><div class=\"tab-pane active\" id=\"pane-0-0\"><div id=\"quicktabs-tabpage-my_tabs-0\" class=\"quicktabs-tabpage\">\n<article id=\"node-72\" class=\"node node-book clearfix\">\n<header><\/header>\n<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\">\n<div class=\"field-items\">\n<div class=\"field-item even\">\n<div class=\"tex2jax\">\n<div class=\"intro\">\n<div class=\"introtopper\">\n<p>This lesson reviews the basic characteristics of data. At the end of the lesson, you will be able to:<\/p>\n<\/div>\n<div class=\"objectives\">\n<ol>\n<li>Explain what a variable is and differentiate between independent and dependent variables<\/li>\n<li>Determine the measurement scale of a variable<\/li>\n<li>Describe continuous, discrete, and dichotomous variables<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div><\/div><div class=\"tab-pane \" id=\"pane-0-1\"><div id=\"quicktabs-tabpage-my_tabs-1\" class=\"quicktabs-tabpage\">\n<article id=\"node-74\" class=\"node node-book clearfix\">\n<header><\/header>\n<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\">\n<div class=\"field-items\">\n<div class=\"field-item even\">\n<div class=\"tex2jax\">\n<div class=\"terminology\">\n<p>Terms that appear frequently throughout this lesson are defined below:<\/p>\n<table class=\"table\">\n<tbody>\n<tr>\n<td class=\"termdef\"><strong>Term<\/strong><\/td>\n<td class=\"termdef\"><strong>Definition<\/strong><\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\"><strong>Dependent variable<\/strong><\/td>\n<td style=\"vertical-align: top;\">The output, outcome, or effect of interest<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\"><strong>Independent variable<\/strong><\/td>\n<td style=\"vertical-align: top;\">An input, which may be varied or simply observed by the researcher. Sometimes called an experimental or predictor variable<\/td>\n<\/tr>\n<tr>\n<td class=\"tableseparator\" style=\"background-color: #efefef;\"><strong>Measurement Scale<\/strong><\/td>\n<td class=\"tableseparator\" style=\"background-color: #efefef;\"><\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Nominal<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Named category<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Dichotomous<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">A nominal variable that contains only two categories or levels (e.g., yes\/no, male\/female, community\/hospital)<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Ordinal<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Ordered categories<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Interval<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Each value on the scale has a unique meaning, can be rank ordered, and are equally spaced<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Ratio<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Each value on the scale has a unique meaning, can be rank ordered, are equally spaced, and has a minimum value of zero<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Continuous<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Variables that can take on any value in a given range; typically interval or ratio variables and sometimes ordinal variables<\/td>\n<\/tr>\n<tr>\n<td style=\"vertical-align: top;\">\n<p style=\"margin-left: 40px;\"><strong>Discrete<\/strong><\/p>\n<\/td>\n<td style=\"vertical-align: top;\">Variables that have a finite number of possible values; typically nominal or ordinal variables<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div><\/div><div class=\"tab-pane \" id=\"pane-0-2\"><div id=\"quicktabs-tabpage-my_tabs-2\" class=\"quicktabs-tabpage\">\n<article id=\"node-75\" class=\"node node-book clearfix\">\n<header><\/header>\n<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\">\n<div class=\"field-items\">\n<div class=\"field-item even\">\n<div class=\"tex2jax\">\n<table class=\"table table-centered\">\n<tbody>\n<tr>\n<td class=\"emptyCell\"><\/td>\n<th colspan=\"4\" rowspan=\"1\" scope=\"\">Variable<\/th>\n<\/tr>\n<tr>\n<td class=\"emptyCell\"><\/td>\n<th colspan=\"2\" rowspan=\"1\">Categorical<\/th>\n<th colspan=\"2\" rowspan=\"1\">Quantitative<\/th>\n<\/tr>\n<tr>\n<th>Level<\/th>\n<td>Nominal<\/td>\n<td>Ordinal<\/td>\n<td>Interval<\/td>\n<td>Ratio<\/td>\n<\/tr>\n<tr>\n<th>Defining Feature<\/th>\n<td>Distinct Categories<\/td>\n<td>Ordered Categories<\/td>\n<td>Meaningful Distances<\/td>\n<td>Absolute Zero<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>In biostatistics, an <strong>independent variable <\/strong>(also called<em> predictor<\/em> or <em>experimental<\/em>) is a variable that is observed or manipulated in order to determine its relationship with the <strong>dependent variable<\/strong> (also called <em>outcome, output<\/em>, or <em>effect of interest<\/em>).<\/p>\n<ol>\n<li><strong>Discrete<\/strong> <strong>variables<\/strong> (also called <em>categorical<\/em>) have a finite number of possible responses, which are typically categories:\n<ol type=\"a\">\n<li><strong>Nominal variables<\/strong> fall into two or more named categories. For example, city is a nominal variable. Possible categories could include Atlanta, Boston, Cleveland, Dallas, and Phoenix.\n<ol style=\"list-style-type: lower-roman;\" start=\"1\">\n<li><strong>Dichotomous variables<\/strong> (also called <em>binary<\/em>) are nominal variables that fall into only two categories. For example, a coin toss can be categorized as heads or tails.<\/li>\n<\/ol>\n<\/li>\n<li><strong>Ordinal<\/strong> <strong>variables<\/strong> are clearly ordered categories. For example, the answer to a survey question may be categorized as low, medium, or high; highest level of education may be categorized as some high school, completed high school, some college, completed bachelors degree, some graduate school, completed graduate degree.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<ol start=\"2\">\n<li><strong>Continuous<\/strong> <strong>variables<\/strong> can take on any value in a certain range:\n<ol type=\"a\">\n<li><strong>Interval variables<\/strong> have meaningful intervals between measurements. The difference between a temperature of 70 and 80 degrees, for example, is the same difference as between 80 and 90 degrees.<\/li>\n<li><strong>Ratio variables<\/strong> include the value 0. Weight, height, and enzyme activity are examples.<\/li>\n<\/ol>\n<\/li>\n<\/ol>\n<div class=\"alert alert-warning\"><span class=\"glyphicon glyphicon-exclamation-sign red\">\u00a0<\/span> <strong>BEWARE!<\/strong> Sometimes, ordinal data is treated as continuous data. A common example of this is the Likert agreement scale (i.e., strongly agree to strongly disagree). While some researchers argue that the scale can be treated as continuous under certain conditions, others argue that it should never be treated as a continuous variable.<\/div>\n<div class=\"alert alert-warning\">\n<p><span class=\"glyphicon glyphicon-exclamation-sign red\">\u00a0<\/span> <strong>BEWARE! <\/strong>Generally, the amount of information captured by data increases as you move from: <strong>nominal \u2192 ordinal \u2192\u00a0 interval \u2192 ratio.<\/strong> Restructuring continuous data into categorical data is akin to throwing data away. For example, recoding an exam score measured on a 100 point scale to Pass\/Fail for the purposes of analysis reduces the amount of information in the data.<\/p>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div><\/div><div class=\"tab-pane \" id=\"pane-0-3\"><div id=\"quicktabs-tabpage-my_tabs-3\" class=\"quicktabs-tabpage\">\n<article id=\"node-81\" class=\"node node-book clearfix\">\n<header><\/header>\n<div class=\"field field-name-body field-type-text-with-summary field-label-hidden\">\n<div class=\"field-items\">\n<div class=\"field-item even\">\n<div class=\"tex2jax\">\n<p>The following table presents adherence data broken down by patient characteristics. Multiple types of data are used. Consider the following questions:<\/p>\n<p>1. What is the dependent variable? What are the independent variables?<\/p>\n<p>2. What scale of measurement is used for each variable?<\/p>\n<p style=\"text-align: center;\"><strong>Patient Characteristics by Category of Adherence<sup>a<\/sup><\/strong><\/p>\n<div class=\"table-responsive\">\n<table class=\"table-condensed\">\n<tbody>\n<tr>\n<th>Characteristics<\/th>\n<th style=\"text-align: center;\">Adherent to Three Classes<\/th>\n<th style=\"text-align: center;\">Adherent to Two Classes<\/th>\n<th style=\"text-align: center;\">Adherent to One Class<\/th>\n<th style=\"text-align: center;\">Nonadherent to Any Classes<\/th>\n<th style=\"text-align: center;\"><em>P<\/em> Value<\/th>\n<\/tr>\n<tr>\n<th>Number of patients<\/th>\n<td style=\"text-align: center;\">201, 459<\/td>\n<td style=\"text-align: center;\">134, 694<\/td>\n<td style=\"text-align: center;\">77, 696<\/td>\n<td style=\"text-align: center;\">79, 760<\/td>\n<td style=\"text-align: center;\">\u2014<\/td>\n<\/tr>\n<tr>\n<th>Age, y (mean \u00b1 SD)<\/th>\n<td style=\"text-align: center;\">71.9 (8.9)<\/td>\n<td style=\"text-align: center;\">71.4 (9.4)<\/td>\n<td style=\"text-align: center;\">71.1 (9.7)<\/td>\n<td style=\"text-align: center;\">69.9 (10.5)<\/td>\n<td style=\"text-align: center;\">&lt; .001<\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">&lt; 65, %<\/p>\n<\/td>\n<td style=\"text-align: center;\">12.7<\/td>\n<td style=\"text-align: center;\">15.2<\/td>\n<td style=\"text-align: center;\">16.7<\/td>\n<td style=\"text-align: center;\">20.9<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">65-74, %<\/p>\n<\/td>\n<td style=\"text-align: center;\">49.1<\/td>\n<td style=\"text-align: center;\">47.0<\/td>\n<td style=\"text-align: center;\">46.3<\/td>\n<td style=\"text-align: center;\">45.4<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">\u2265 75, %<\/p>\n<\/td>\n<td style=\"text-align: center;\">38.2<\/td>\n<td style=\"text-align: center;\">37.8<\/td>\n<td style=\"text-align: center;\">37.1<\/td>\n<td style=\"text-align: center;\">33.8<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<th>Female, %<\/th>\n<td style=\"text-align: center;\">55.6<\/td>\n<td style=\"text-align: center;\">59.4<\/td>\n<td style=\"text-align: center;\">61.1<\/td>\n<td style=\"text-align: center;\">59.8<\/td>\n<td style=\"text-align: center;\">&lt; .001<\/td>\n<\/tr>\n<tr>\n<th>Race, %<\/th>\n<td style=\"text-align: center;\"><\/td>\n<td style=\"text-align: center;\"><\/td>\n<td style=\"text-align: center;\"><\/td>\n<td style=\"text-align: center;\"><\/td>\n<td style=\"text-align: center;\">&lt;.001<\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">White<\/p>\n<\/td>\n<td style=\"text-align: center;\">68.6<\/td>\n<td style=\"text-align: center;\">66.0<\/td>\n<td style=\"text-align: center;\">62.6<\/td>\n<td style=\"text-align: center;\">57.9<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">Black<\/p>\n<\/td>\n<td style=\"text-align: center;\">12.8<\/td>\n<td style=\"text-align: center;\">15.0<\/td>\n<td style=\"text-align: center;\">17.2<\/td>\n<td style=\"text-align: center;\">20.8<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">Hispanic<\/p>\n<\/td>\n<td style=\"text-align: center;\">6.5<\/td>\n<td style=\"text-align: center;\">8.5<\/td>\n<td style=\"text-align: center;\">10.0<\/td>\n<td style=\"text-align: center;\">11.0<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<td>\n<p style=\"margin-left: 40px;\">Other<\/p>\n<\/td>\n<td style=\"text-align: center;\">12.0<\/td>\n<td style=\"text-align: center;\">10.6<\/td>\n<td style=\"text-align: center;\">10.2<\/td>\n<td style=\"text-align: center;\">10.4<\/td>\n<td style=\"text-align: center;\"><\/td>\n<\/tr>\n<tr>\n<th>CCI (mean \u00b1 SD)<\/th>\n<td style=\"text-align: center;\">0.73 (1.54)<\/td>\n<td style=\"text-align: center;\">1.04 (1.83)<\/td>\n<td style=\"text-align: center;\">1.22 (1.99)<\/td>\n<td style=\"text-align: center;\">1.30 (2.07)<\/td>\n<td style=\"text-align: center;\">&lt; .001<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n<p><em>CCI, Deyo-adapted Charlson Cormorbidity Index.<br \/>\n<sup>a<\/sup> Adherence was defined as proportion of days covered \u2265 80%.<\/em><\/p>\n<p><strong>Dependent variable:<\/strong> category of adherence<\/p>\n<p><strong>Independent variables: <\/strong>patient characteristics (i.e. age, gender, race, CCI)<\/p>\n<p><strong>Nominal: <\/strong>gender, race<\/p>\n<p><strong>Ordinal:<\/strong> age, category of adherence<\/p>\n<p><strong>Ratio: <\/strong>number of patients, age, CCI<\/p>\n<p>Notice that age is represented on two scales: 1) as a continuous variable (as represented by mean +\/- SD) and 2) as a categorical variable (as %s in &lt; 65, 65 &#8211; 74, and &gt; = 75)<\/p>\n<div class=\"well moreinformation\">\n<p>For more information<\/p>\n<ul>\n<li class=\"citation\">Table 1: Yang Y, et al. <a href=\"https:\/\/pdfs.semanticscholar.org\/6a2a\/6d6c1a2b1d57a9d341acc12c1eb33afe5408.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">Medication nonadherence and the risks of hospitalization, emergency department visits, and death among Medicare Part D enrollees with diabetes.<\/a> <cite>Drug Benefit Trends.<\/cite> 2009; 21(330): 8.<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/article>\n<\/div><\/div><div class=\"tab-pane \" id=\"pane-0-4\"><div class=\"h5p-iframe-wrapper\"><iframe id=\"h5p-iframe-2\" class=\"h5p-iframe\" data-content-id=\"2\" style=\"height:1px\" src=\"about:blank\" frameBorder=\"0\" scrolling=\"no\" title=\"Assessment (for Lesson 1)\"><\/iframe><\/div><\/div><\/div><\/div>\n","protected":false},"excerpt":{"rendered":"","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"_links":{"self":[{"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/pages\/9"}],"collection":[{"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/comments?post=9"}],"version-history":[{"count":120,"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/pages\/9\/revisions"}],"predecessor-version":[{"id":1413,"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/pages\/9\/revisions\/1413"}],"wp:attachment":[{"href":"https:\/\/wp.media.unc.edu\/biostatistics\/wp-json\/wp\/v2\/media?parent=9"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}