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Discussion: Descriptive and Inferential Statistics

Discussion: Descriptive and Inferential StatisticsDiscussion: Descriptive and Inferential StatisticsPr a c t ic e M atter sR e s e a r c h 101Sample size in quantitativeresearchSample size will affect the significance of your research.By Susan B. Fowler, PhD, RN, CNRN, FAHA, and Valerie Lapp, PhD, RN, NEA-BC, CPNEditor’s note: This ispart of the American Nurse TodayResearch 101 series. To read other articles in the series,visit americannursetoday.com/category/Researcbl01.You’ve probably been asked (or have asked) thequestion: How many subjects do I need for my re­search study? That’s your sample size—the numberof participants needed to achieve valid conclusions orstatistical significance in quantitative research. (Quali­tative research requires a somewhat different approach.In this article, we’ll answer these questions aboutsample size in quantitative research: Why does samplesize matter? How do I determine sample size? Whichsampling method should I use? What’s sampling bias?ORDER A PLAGIARISM-FREE PAPER HEREWhy does sample size matter?Discussion: Descriptive and Inferential StatisticsWhen sample sizes are too small, you run the risk ofnot gathering enough data to support your hypothesesor expectations. The result may indicate that relation­ships between variables aren’t statistically significantwhen, actually, they are. You also may be missing sub­jects who might give a different answer or perspectiveto your survey or interview. Samples that are too largemay provide data that describe associations or relation­ships that are due merely to chance. Large samples al­so may waste time and money.How do I determine sample size?Larger sample sizes typically are more representative ofthe population you’re studying, but only if you collectdata randomly and the population is heterogeneous.Large samples also reduce the chance of outliers. How­ever, large samples are no guarantee of accuracy. Ifyour population of interest is homogenous, you mayneed only a small sample.Discussion: Descriptive and Inferential StatisticsIf you’re studying subjects over longer periods oftime, as in longitudinal designs, you can expect subjectattrition. Know your population and how responsivethey may be to repeated questionnaires and interven­tions. Even if you’re not conducting a longitudinal study,be realistic about how many people would agree to par­ticipate in research.For a pilot study (a small-scale version of a biggerstudy testing the efficacy of an intervention), you’dusually need around 30 subjects, although that numbervaries according to different experts.No matter the type of study you’re conducting, takeinto account time (yours and the subjects’), subject co­operation, and resources (such as statistical assistance,access to subjects, managerial support for your study,and co- or sub-investigators).Discussion: Descriptive and Inferential StatisticsIrIiPower analysisPower analysis is a robust way to determine sample sizeand decrease the risk of type II errors (false-negativeconclusions that a finding was due to chance when ac­tually it was the result of the intervention). Apoweranalysis calculation includes a significance criterion, ef­fect size, and power to arrive at a sample size. The sig­nificance criterion is referred to as alpha and usually isset at 0.05, which means that in 5 of 100 situations theresult would be due to chance and not the intervention.Effect size (usually described as small, moderate, or large)is the magnitude or strength of the relationship betweenthe variables you’re studying. In nursing, we oftenpropose that variables moderately affect one anotherAmericanNurseToday.com May 2019 American Nurse Today 61Discussion: Descriptive and Inferential Statisticsor are correlated. Forexample, when on­cology nursing stud­ies about the effec­tiveness of symptommanagement interven­tions were combinedand analyzed, a mod­erate to large effectwas found. Power (1-beta) usually is set at.80, which means thatthere’s a 20% risk ofcommitting a type IIerror. (See Feel thepower?)Which samplingmethod shouldI use?The sampling methodisn’t the same as thesample. It’s the proce­dure you’ll use to selectstudy participants. We’lllook at two samplingmethods: nonproba­bility and probability.Discussion: Descriptive and Inferential StatisticsNonprobabilitysamplingConvenience samplingand snowball sampling are common nonprobabilitymethods. Convenience samples consist of people whoare easily accessed and volunteer; however, the samplemay not be representative of the population of interestin your study. Convenience sampling is considered theweakest form of sampling.With snowball sampling, participants are referred byother participants. This method can be used when youhave difficulty locating participants. For example, wheninterviewing undocumented immigrants, the researchergains the trust of a few participants and relies on themto identify other undocumented immigrants who mightparticipate.Discussion: Descriptive and Inferential StatisticsProbability samplingWith probability sampling, everyone in an identifiedpopulation has an equal chance of being in the sam­ple. You can use a variety of approaches, includingsimple random, stratified random, multistage cluster,and systematic random sampling. For example, system­atic random sampling of patients on a medical-surgicalfloor for an intervention study may include selectingevery sixth room number. (Visit bit.ly/2FZLzYX to learnmore about types ofprobability sampling.)What’s samplingbias?Sampling bias canoccur when a partic­ular overrepresen­tation or underrep­resentation of thepopulation occurs.Discussion: Descriptive and Inferential StatisticsFor example, if a re­searcher wants tostudy which methodof education is moreeffective by genderin reducing hospitalreadmissions, thenumber of men andwomen should beevenly distributed.Bias occurs when theresearcher deliberate­ly omits or makes aconscious decision toexclude a participantw ho’s had several re­admissions for exac­erbation of his heartfailure. Both omis­sions reflect bias andmay distort study re­sults and undermine the validity of the study.What are the practice implications?As nurses become more involved in evidence-basedpractice projects and research investigations, they’llneed to understand key elements of research, such assample size, so they can critically appraise and gener­ate evidence. Remember that the “right” number ofsubjects in your investigation impacts statistical and clin­ical significance support for your study findings. ★Susan B. Fow ler is a nurse scie n tist at O rlando H ealth in O rlando, F lorida, m e n ­to r fa c u lty a t Thomas Edison State U n iversity in T renton, New Jersey; and con­trib u tin g fa c u lty a t W alden U n iversity in M inne apolis, M inne sota . Valerie Lappis a program m anager fo r nursing and special projects and M agnet® coo rd in a to ra t A rnold Palm er M edical Center in O rlando, Florida.Selected referencesFaber J, Fonseca LM. How sample size influences research outcomes.Dental PressJ Orthod. 2014; 19C4):27-P.Polit DF, Beck CT. Nursing Research: Generating and Assessing Evi­dencefo r Nursing Practice. Philadelphia, PA: Wolters Kluwer; 2017.Schmidt SAJ, Lo S, Hollestein LM. Research techniques made simple:Sample size estimation and power calculation. / Invest Dermatol. 2018;138(8):l678-82.Discussion: Descriptive and Inferential StatisticsFeel the powerBetty, a pediatric nurse, wants to study the effect o f distraction on children’sdiscom fort during insertion o f an I.V. catheter before a procedure in the ra­diology departm ent. She reaches o u t to experts at her facility to help herdeterm ine how many subjects she needs fo r her study.Researchers assist her using G*Power, a free online pow er analysis tool.(Vanderbilt University also has a free pow er and sample size calculationprogram th a t can be dow nloaded at biostat.m c.vanderbilt.edu/w iki/M ain/PowerSampleSize.)W ith significance set at 0.05, a m oderate effect size o f 0.3, and pow er at.80, Betty w ill need 82 subjects (see below).62 American Nurse Today Volume 14, Number 5 AmericanNurseToday.comDiscussion: Descriptive and Inferential Statistics

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