![]() ![]() Since the same participants were measured at these two time points, the groups are related. For example, you might have measured 100 participants' salary in US dollars (i.e., the dependent variable) before and after they took an MBA to improve their employability and salary (i.e., the two "time points" where participants' salary was measured – "before" and "after" the MBA course – reflect the two "related groups" of the independent variable). The reason that it is possible to have the same subjects in each group is because each subject has been measured on two occasions on the same dependent variable. "Related groups" indicates that the same subjects are present in both groups. Assumption #2: Your independent variable should consist of two categorical, "related groups" or "matched pairs".If you are unsure whether your dependent variable is continuous or ordinal, see our Types of Variable guide. ![]() Examples of ordinal variables include Likert items (e.g., a 7-point scale from strongly agree through to strongly disagree), physical activity level (e.g., 4 groups: sedentary, low, moderate and high), customers liking a product (ranging from "Not very much", to "It is OK", to "Yes, a lot"), the pain felt by patients after hip replacement surgery (e.g., "No pain", "Mild pain", "Moderate pain", "Strong pain" and "Severe pain"), amongst other ways of ranking categories (e.g., a 5-point scale explaining how much a customer liked a product, ranging from "Not very much" to "Yes, a lot"). ![]() Examples of continuous variables include weight (measured in kilograms), temperature (measured in ☌), salary (measured in US dollars), revision time (measured in hours), intelligence (measured using IQ score), speed (measured in mph), exam performance (measured from 0 to 100), sales (measured in US dollars), and so forth. Assumption #1: Your dependent variable should be measured on a continuous (i.e., interval or ratio) or ordinal level.These four assumptions are explained below: If these assumptions are not met, there is likely to be a different statistical test that you can use instead. However, you should check whether your study meets these four assumptions before moving on. You cannot test these assumptions with SPSS Statistics because they relate to your study design and choice of variables. You need to do this because it is only appropriate to use a sign test if your data "passes" four assumptions that are required for a sign test to give you a valid result. When you choose to analyse your data using a sign test, part of the process involves checking to make sure that the data you want to analyse can actually be analysed using a sign test. However, before we introduce you to this procedure, you need to understand the different assumptions that your data must meet in order for a sign test to give you a valid result. This "quick start" guide shows you how to carry out a sign test using SPSS Statistics, as well as interpret and report the results from this test. You could also use the sign test to determine whether there was a median difference in reaction times under two different lighting conditions (i.e., your dependent variable would be "reaction time", measured in milliseconds, and the two conditions would be testing reaction time in a room using "blue light" and a room using "red light"). However, two different groups of participants are possible as part of a "matched-pairs" study design.įor example, you could use the sign test to understand whether there was a median difference in smokers' daily cigarette consumption before and after a 6-week hypnotherapy programme (i.e., your dependent variable would be "daily cigarette consumption", with the two time points being "before" and "after" the hypnotherapy programme). Most commonly, participants are tested at two time points or under two different conditions on the same continuous dependent variable. The test can be considered as an alternative to the dependent t-test (also called the paired-samples t-test) or Wilcoxon signed-rank test when the distribution of differences between paired observations is neither normal nor symmetrical, respectively. The "paired-samples sign test", typically referred to as just the "sign test", is used to determine whether there is a median difference between paired or matched observations. Sign Test using SPSS Statistics Introduction ![]()
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