Understanding Frequencies and Percentages

Understanding Frequencies and Percentages STATISTICAL TECHNIQUE IN REVIEW Frequency is the number of times a score or value for a variable occurs in a set of data. Frequency distribution is a statistical procedure that involves listing all the possible values or scores for a variable in a study. Frequency distributions are used to organize study data for a detailed examination to help determine the presence of errors in coding or computer programming ( Grove, Burns, & Gray, 2013 ). In addition, frequencies and percentages are used to describe demographic and study variables measured at the nominal or ordinal levels. Percentage can be defi ned as a portion or part of the whole or a named amount in every hundred measures. For example, a sample of 100 subjects might include 40 females and 60 males. In this example, the whole is the sample of 100 subjects, and gender is described as including two parts, 40 females and 60 males. A percentage is calculated by dividing the smaller number, which would be a part of the whole, by the larger number, which represents the whole. The result of this calculation is then multiplied by 100%. For example, if 14 nurses out of a total of 62 are working on a given day, you can divide 14 by 62 and multiply by 100% to calculate the percentage of nurses working that day. Calculations: (14 ÷ 62) × 100% = 0.2258 × 100% = 22.58% = 22.6%. The answer also might be expressed as a whole percentage, which would be 23% in this example. A cumulative percentage distribution involves the summing of percentages from the top of a table to the bottom. Therefore the bottom category has a cumulative percentage of 100% (Grove, Gray, & Burns, 2015). Cumulative percentages can also be used to deter-mine percentile ranks, especially when discussing standardized scores. For example, if 75% of a group scored equal to or lower than a particular examinee ’ s score, then that examinee ’ s rank is at the 75 th percentile. When reported as a percentile rank, the percentage is often rounded to the nearest whole number. Percentile ranks can be used to analyze ordinal data that can be assigned to categories that can be ranked. Percentile ranks and cumulative percentages might also be used in any frequency distribution where subjects have only one value for a variable. For example, demographic characteristics are usually reported with the frequency ( f ) or number ( n ) of subjects and percentage (%) of subjects for each level of a demographic variable. Income level is presented as an example for 200 subjects: Income Level Frequency ( f ) Percentage (%) Cumulative % 1. < $40,000 2010%10% 2. $40,000–$59,999 5025%35% 3. $60,000–$79,999 8040%75% 4. $80,000–$100,000 4020%95% 5. > $100,000 105%100% EXERCISE 6 60EXERCISE 6 • Understanding Frequencies and PercentagesCopyright © 2017, Elsevier Inc. All rights reserved. In data analysis, percentage distributions can be used to compare fi ndings from different studies that have different sample sizes, and these distributions are usually arranged in tables in order either from greatest to least or least to greatest percentages ( Plichta & Kelvin, 2013 ). RESEARCH ARTICLE Source Eckerblad, J., Tödt, K., Jakobsson, P., Unosson, M., Skargren, E., Kentsson, M., & Thean-der, K. (2014). Symptom burden in stable COPD patients with moderate to severe airfl ow limitation. Heart & Lung, 43 (4), 351–357. Introduction Eckerblad and colleagues (2014 , p. 351) conducted a comparative descriptive study to examine the symptoms of “patients with stable chronic obstructive pulmonary disease (COPD) and determine whether symptom experience differed between patients with mod-erate or severe airfl ow limitations.” The Memorial Symptom Assessment Scale (MSAS) was used to measure the symptoms of 42 outpatients with moderate airfl ow limitations and 49 patients with severe airfl ow limitations. The results indicated that the mean number of symptoms was 7.9 ( ± 4.3) for both groups combined, with no signifi cant dif-ferences found in symptoms between the patients with moderate and severe airfl ow limi-tations. For patients with the highest MSAS symptom burden scores in both the moderate and the severe limitations groups, the symptoms most frequently experienced included shortness of breath, dry mouth, cough, sleep problems, and lack of energy. The research-ers concluded that patients with moderate or severe airfl ow limitations experienced mul-tiple severe symptoms that caused high levels of distress. Quality assessment of COPD patients ’ physical and psychological symptoms is needed to improve the management of their symptoms. Relevant Study Results Eckerblad et al. (2014 , p. 353) noted in their research report that “In total, 91 patients assessed with MSAS met the criteria for moderate ( n = 42) or severe airfl ow limitations ( n = 49). Of those 91 patients, 47% were men, and 53% were women, with a mean age of 68 ( ± 7) years for men and 67 ( ± 8) years for women. The majority (70%) of patients were married or cohabitating. In addition, 61% were retired, and 15% were on sick leave. Twenty-eight percent of the patients still smoked, and 69% had stopped smoking. The mean BMI (kg/m 2 ) was 26.8 ( ± 5.7). There were no signifi cant differences in demographic characteristics, smoking history, or BMI between patients with moderate and severe airfl ow limitations