{"id":249,"date":"2020-02-13T14:00:08","date_gmt":"2020-02-13T19:00:08","guid":{"rendered":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/?post_type=chapter&p=249"},"modified":"2024-08-01T16:19:30","modified_gmt":"2024-08-01T20:19:30","slug":"6-1-1-random-assignation","status":"publish","type":"chapter","link":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/chapter\/6-1-1-random-assignation\/","title":{"raw":"6.1.1 Random Assignation","rendered":"6.1.1 Random Assignation"},"content":{"raw":"As previously mentioned, one of the characteristics of a true experiment is that researchers use a random process to decide which participants are tested under which conditions. Random assignation <\/em><\/strong>is a powerful research technique that addresses the assumption of pre-test equivalence \u2013 that the experimental and control group are equal in all respects before the administration of the independent variable (Palys & Atchison, 2014).\r\n\r\nRandom assignation is the primary way that researchers attempt to control extraneous variables across conditions. Random assignation is associated with experimental research methods. In its strictest sense, random assignment should meet two criteria.\u00a0 One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus, one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands on the heads side, the participant is assigned to Condition A, and if it lands on the tails side, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and, if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions\u2014one for each participant expected to be in the experiment\u2014is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested.\r\n\r\nHowever, one problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible.\r\n\r\nOne approach is block randomization. In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these \u201cblocks,\u201d the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. When the procedure is computerized, the computer program often handles the random assignment, which is obviously much easier. You can also find programs online to help you randomize your random assignation. For example, the Research Randomizer website<\/a> will generate block randomization sequences for any number of participants and conditions.\r\n\r\nRandom assignation is not guaranteed to control all extraneous variables across conditions. It is always possible that, just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this may not be a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population take the \u201cfallibility\u201d of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions\u2014although not infallible in terms of controlling extraneous variables\u2014is always considered a strength of a research design. Note: Do not confuse random assignation with random sampling. Random sampling is a method for selecting a sample from a population; we will talk about this in Chapter 7.<\/a>","rendered":"

As previously mentioned, one of the characteristics of a true experiment is that researchers use a random process to decide which participants are tested under which conditions. Random assignation <\/em><\/strong>is a powerful research technique that addresses the assumption of pre-test equivalence \u2013 that the experimental and control group are equal in all respects before the administration of the independent variable (Palys & Atchison, 2014).<\/p>\n

Random assignation is the primary way that researchers attempt to control extraneous variables across conditions. Random assignation is associated with experimental research methods. In its strictest sense, random assignment should meet two criteria.\u00a0 One is that each participant has an equal chance of being assigned to each condition (e.g., a 50% chance of being assigned to each of two conditions). The second is that each participant is assigned to a condition independently of other participants. Thus, one way to assign participants to two conditions would be to flip a coin for each one. If the coin lands on the heads side, the participant is assigned to Condition A, and if it lands on the tails side, the participant is assigned to Condition B. For three conditions, one could use a computer to generate a random integer from 1 to 3 for each participant. If the integer is 1, the participant is assigned to Condition A; if it is 2, the participant is assigned to Condition B; and, if it is 3, the participant is assigned to Condition C. In practice, a full sequence of conditions\u2014one for each participant expected to be in the experiment\u2014is usually created ahead of time, and each new participant is assigned to the next condition in the sequence as he or she is tested.<\/p>\n

However, one problem with coin flipping and other strict procedures for random assignment is that they are likely to result in unequal sample sizes in the different conditions. Unequal sample sizes are generally not a serious problem, and you should never throw away data you have already collected to achieve equal sample sizes. However, for a fixed number of participants, it is statistically most efficient to divide them into equal-sized groups. It is standard practice, therefore, to use a kind of modified random assignment that keeps the number of participants in each group as similar as possible.<\/p>\n

One approach is block randomization. In block randomization, all the conditions occur once in the sequence before any of them is repeated. Then they all occur again before any of them is repeated again. Within each of these \u201cblocks,\u201d the conditions occur in a random order. Again, the sequence of conditions is usually generated before any participants are tested, and each new participant is assigned to the next condition in the sequence. When the procedure is computerized, the computer program often handles the random assignment, which is obviously much easier. You can also find programs online to help you randomize your random assignation. For example, the Research Randomizer website<\/a> will generate block randomization sequences for any number of participants and conditions.<\/p>\n

Random assignation is not guaranteed to control all extraneous variables across conditions. It is always possible that, just by chance, the participants in one condition might turn out to be substantially older, less tired, more motivated, or less depressed on average than the participants in another condition. However, there are some reasons that this may not be a major concern. One is that random assignment works better than one might expect, especially for large samples. Another is that the inferential statistics that researchers use to decide whether a difference between groups reflects a difference in the population take the \u201cfallibility\u201d of random assignment into account. Yet another reason is that even if random assignment does result in a confounding variable and therefore produces misleading results, this confound is likely to be detected when the experiment is replicated. The upshot is that random assignment to conditions\u2014although not infallible in terms of controlling extraneous variables\u2014is always considered a strength of a research design. Note: Do not confuse random assignation with random sampling. Random sampling is a method for selecting a sample from a population; we will talk about this in Chapter 7.<\/a><\/p>\n","protected":false},"author":31,"menu_order":2,"template":"","meta":{"pb_show_title":"on","pb_short_title":"","pb_subtitle":"","pb_authors":[],"pb_section_license":""},"chapter-type":[],"contributor":[],"license":[],"class_list":["post-249","chapter","type-chapter","status-publish","hentry"],"part":195,"_links":{"self":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapters\/249","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapters"}],"about":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/wp\/v2\/types\/chapter"}],"author":[{"embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/wp\/v2\/users\/31"}],"version-history":[{"count":6,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapters\/249\/revisions"}],"predecessor-version":[{"id":1119,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapters\/249\/revisions\/1119"}],"part":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/parts\/195"}],"metadata":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapters\/249\/metadata\/"}],"wp:attachment":[{"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/wp\/v2\/media?parent=249"}],"wp:term":[{"taxonomy":"chapter-type","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/pressbooks\/v2\/chapter-type?post=249"},{"taxonomy":"contributor","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/wp\/v2\/contributor?post=249"},{"taxonomy":"license","embeddable":true,"href":"https:\/\/pressbooks.bccampus.ca\/jibcresearchmethods\/wp-json\/wp\/v2\/license?post=249"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}