1. What are the frequency and percentage of the COPD patients in the severe airﬂow limitation group who are employed in the Eckerblad et al. (2014) study?
2. What percentage of the total sample is retired? What percentage of the total sample is on sick leave?
3. What is the total sample size of this study? What frequency and percentage of the total sample were still employed? Show your calculations and round your answer to the nearest whole percent.
4. What is the total percentage of the sample with a smoking history—either still smoking or former smokers? Is the smoking history for study participants clinically important? Provide a rationale for your answer.
5. What are pack years of smoking? Is there a signiﬁcant difference between the moderate and severe airﬂow limitation groups regarding pack years of smoking? Provide a rationale for your answer.
6. What were the four most common psychological symptoms reported by this sample of patients with COPD? What percentage of these subjects experienced these symptoms? Was there a signiﬁcant difference between the moderate and severe airﬂow limitation groups for psychological symptoms?
7. What frequency and percentage of the total sample used short-acting β 2 -agonists? Show your calculations and round to the nearest whole percent.
8. Is there a signiﬁcant difference between the moderate and severe airﬂow limitation groups regarding the use of short-acting β 2 -agonists? Provide a rationale for your answer.
9. Was the percentage of COPD patients with moderate and severe airﬂow limitation using short-acting β 2 -agonists what you expected? Provide a rationale with documentation for your answer.
10. Are these ﬁndings ready for use in practice? Provide a rationale for your answer.
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 deﬁned 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:
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 ( Table 1 ). A lower proportion of patients with moderate airﬂ ow limitation used inhalation treatment with glucocorticosteroids, long-acting β 2 -agonists and short-acting β 2 -agonists, but a higher proportion used analgesics compared with patients with severe airﬂ ow limitation.
Symptom prevalence and symptom experience The patients reported multiple symptoms with a mean number of 7.9 ( ± 4.3) symptoms (median = 7, range 0–32) for the total sample, 8.1 ( ± 4.4) for moderate airﬂ ow limitation and 7.7 ( ± 4.3) for severe airﬂ ow limitation ( p = 0.36) . . . .
Highly prevalent physical symp-toms ( ≥ 50% of the total sample) were shortness of breath (90%), cough (65%), dry mouth (65%), and lack of energy (55%). Five additional physical symptoms, feeling drowsy, pain, numbness/tingling in hands/feet, feeling irritable, and dizziness, were reported by between 25% and 50% of the patients. The most commonly reported psychological symptom was difﬁculty sleeping (52%), followed by worrying (33%), feeling irritable (28%) and feeling sad (22%). There were no signiﬁ cant differences in the occurrence of physical and psy-chological symptoms between patients with moderate and severe airﬂ ow limitations” ( Eckerblad et al., 2014 , p. 353).