# Exercise 1-HLT-362V, Ordinal Levels Of Measurement (Other (Not Listed) Sample)

NEED TO ANSWER QUESTION TO BE GRADED ACCORDING TO THIS TABLE AND OTHER INFORMATION EXERCISE 1 IN THE BOOK.

Identifying Levels of Measurement

Nominal, Ordinal, Interval, and Ratio

Statistical Technique in Review

The levels of measurement were identified in 1946 by Stevens, who organized the rules for assigning numbers to objects so that a hierarchy of measurement was established. The levels of measurement, from lowest to highest level, are nominal, ordinal, interval, and ratio. Figure 1-1 provides the rules for the four levels of measurement.

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FIGURE 1-1 SUMMARY OF THE RULES FOR THE LEVELS OF MEASUREMENT.

Nominal and Ordinal Levels of Measurement

Variables measured at the nominal level of measurement are at the lowest level and must conform to the following two rules: (1) the data categories must be exclusive (each datum will fit into only one category) and (2) the data categories must be exhaustive (each datum will fit into at least one category). The data categories are developed for the purpose of naming or labeling the variables for a study (Grove, Burns, & Gray, 2013). For example, the variable gender is measured at the nominal level and includes two categories, male and female. Variables measured at the nominal level that are frequently described in studies include gender, race/ethnicity, marital status, and medical diagnoses.

Ordinal level of measurement includes categories that can be rank ordered and, like nominal level measurement, the categories are exhaustive and mutually exclusive (see Figure 1-1). In ranking categories of a variable, each category must be recognized as higher or lower or better or worse than another category. However, with the ordinal level of measurement, you do not know exactly how much higher or lower one subject's score is on a variable in relation to another subject's score. Thus variables measured at the ordinal level do not have a continuum of values with equal distances between them like variables measured at the interval and ratio levels. For example, you could have subjects identify their levels of acute pain as no pain, mild pain, moderate pain, or severe pain. Pain is measured at the ordinal level because the categories can be ranked from a low of no pain to a high of severe pain; however, even though the subjects' levels of pain can be ranked, you do not know the differences between the levels of pain. The difference between no pain and mild pain might be less than that between moderate and severe pain. Thus ordinal level data have unknown, unequal intervals between the categories, such as between the levels of pain (Grove et al., 2013).

Nonparametric or distribution-free analysis techniques can be used to analyze nominal and ordinal levels of data to describe variables, examine relationships among variables, and determine differences between groups in distribution-free or nonnormally distributed samples. The assumptions of nonparametric statistics are as follows: (1) values from measurement of study variables need not be normally distributed in the sample, and (2) the level of measurement of study variables is usually nominal or ordinal. The measure 4of central tendency, which is used to describe variables measured at the nominal level, is the mode or the most frequently occurring value in the data set. The median or middle value in a data set is calculated to describe variables measured at the ordinal level (see Exercise 8). Descriptive statistical analyses, such as frequencies and percentages, are often calculated to describe demographic variables measured at the nominal and ordinal levels in a study (see Exercise 6). Range is calculated to determine the dispersion or spread of values of a variable measured at the ordinal level (see Exercise 9).

Chi-square analysis is calculated to examine differences in variables measured at the nominal level (see Exercise 19). The Spearman Rank-Order Correlation Coefficient is calculated to examine relationships among variables measured at the ordinal level (see Exercise 20). The Mann-Whitney U and Wilcoxon Signed-Ranks tests can be conducted to determine differences among groups when study data are measured at the ordinal level (see Exercises 21 and 22). Nonparametric analyses are also conducted when interval and ratio level data are not normally distributed.

Interval and Ratio Levels of Measurement

With the interval level of measurement, the distances between intervals of the scale are numerically equal. However, there is no absolute zero, meaning that a score of zero does not indicate that property being measured is absent. Examples of interval scales include Fahrenheit and centigrade temperature scales. The ratio level of measurement is the highest form of measurement; it adheres to the same rules as interval level measurement, with numerically equal intervals on the scale (see Figure 1-1; Grove et al., 2013). In addition, ratio level measurement has an absolute zero point, where at zero the property is absent, such as zero weight meaning absence of weight. In nursing, many variables are measured at the ratio level, such as blood pressure, pulse, respiration, body mass index (BMI), and laboratory values. Variables measured at the interval and ratio levels are also referred to as continuous variables. The data obtained from measuring continuous variables can usually be analyzed with parametric statistics.

Parametric statistics are powerful analysis techniques conducted on interval and ratio levels of data to describe variables, examine relationships among variables, and determine differences among groups. The assumptions of parametric statistics include the following: (1) the distribution of scores in a sample is expected to be normal or approximately normal; (2) the variables are continuous, measured at the interval or ratio level; 5and (3) the data can be treated as though they were obtained from a random sample. Parametric analyses are the same for variables measured at either the interval or ratio levels of measurement. For example, means and standard deviations can be calculated to describe study variables measured at the interval or ratio levels (see Exercises 8 and 9). Pearson's correlational coefficient (see Exercise 13) is computed to determine relationships between variables and the t-test (see Exercises 16 and 17) or analysis of variance (ANOVA; see Exercise 18) are calculated to determine significant differences among groups. Significant results are those in keeping with the outcomes predicted by the researcher, where the null hypothesis is rejected. Significant results are usually identified by * or p values less than or equal to alpha (α), which is often set at 0.05 in nursing research. The symbol ≤0.05 mean less than or equal to 0.05, so any p values ≤0.05 are considered significant. Because the analysis techniques are similar for variables measured at the interval and ratio levels, these levels of measurement are sometimes referred to as the interval/ratio levels; the variables are identified as continuous in this text.

Research Article

Source

Popp, J. M., Robinson, J. L., Britner, P. A., & Blank, T. O. (2014). Parent adaptation and family functioning in relation to narratives of children with chronic illness. Journal of Pediatric Nursing, 29(1), 58–64.

Introduction

Popp and colleagues (2014) conducted a mixed methods study to assess the experiences of parents and their children who have been diagnosed with a chronic disease, either diabetes or asthma. Mixed methods studies include both quantitative and qualitative research methodologies (Creswell, 2014; Grove et al., 2013). Popp et al. conducted their study using descriptive quantitative and narrative qualitative methodologies. The results of this study identified that 41% of the parents were unresolved about their child's diagnosis of either diabetes or asthma, regardless of the time since diagnosis. The parents who were unresolved with their child's diagnosis reported lower family functioning, and the children experienced more family conflict. The findings from this study provided insights into families' adjustments to children's chronic illnesses and provided a basis for future research that might focus on interventions to promote family communications and expression of emotions.

Relevant Study Results

“Participants included 66 children (37 with diabetes, 29 with asthma) and their parents (66 mothers, 43 fathers). Child mean age was 6.8 years (SD = 1.01; range = 5–8 years). The sample included children who were White (76%), Hispanic (11%), Black (7%), and biracial (6%), according to parent report. Means and standard deviations of family and illness characteristics can be found in Table 1. The diabetes and asthma groups were similar on most demographic variables except for marital status of parents (p = 0.04) and child gender (p = 0.05)” (Popp et al., 2014, p. 59).

TABLE 1

DEMOGRAPHIC CHARACTERISTICS BETWEEN ILLNESS GROUPS

(Grove 3-5)

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

The citation provided is a guideline. Please check each citation for accuracy before use.

Demographic Variable Diabetes (n = 37) Asthma (n = 29) p

Family race/ethnicity 0.68

White 29 (78%) 21 (72%)

Hispanic 3 (8%) 4 (14%)

Black 2 (5%) 3 (10%)

Biracial 3 (8%) 1 (3%)

Mean age mother (SD) 38 (6) 38 (8) 0.91

Mean age father (SD) 43 (6) 41 (8) 0.52

Parent education 0.73

12th grade or less 20 (54%) 13 (44%)

Associate's degree 2 (5%) 3 (10%)

College degree 6 (16%) 7 (24%)

Graduate school 9 (24%) 4 (21%)

Income 0.37

<$40,000 9 (24%) 9 (29%)

$40,000–$60,000 8 (20%) 6 (21%)

$60,000–$80,000 4 (11%) 0 (0%)

>$80,000 16 (43%) 14 (48%)

Parent marital status 0.04

Married 31 (83%) 19 (66%)

Not married 1 (3%) 7 (24%)

Divorced 5 (13%) 3 (10%)

Child mean age (SD) 6.8 (1.13) 6.6 (1.04) 0.47

Child gender—male (%) 15 (40%) 19 (65%) 0.05

Median time since diagnosis 18–24 months 24–36 months 0.17

Illness severity (0 to 3 scale) 1.62 (0.95) 1.37 (0.82) 0.28

Mean hospitalizations in past year 0.48 (0.90) 0.51 (0.94) 0.89

Popp, J. M., Robinson, J. L., Britner, P. A., & Blank, T. O. (2014). Journal of Pediatric Nursing, 29(1), p. 59.

QUESTIONS TO BE GRADED;

1. In Table 1, identify the level of measurement for the income variable. Provide a rationale for your answer.

2. In Table 1, what is the level of measurement for the marital status variable? Provide a rationale for your answer.

3. What is the mode for the child gender variable in the diabetes group? Provide a rationale for your answer.

4. What are the mean, SD, and range for the variable age of the 66 children included in this study?

5. Were nonparametric or parametric analysis techniques used to analyze the child age data for the asthma group? Provide a rationale for your answer.

6. Identify the nonparametric analyses conducted to describe variables in this study.

7. Is there a significant difference in gender between the diabetes and asthma groups? Provide a rationale for your answer.

8. Ordinal level data need to have exclusive and exhaustive categories that can be ranked. Does the income variable follow these three rules? Provide a rationale for your answer.

9. Identify the number of parents included in this study. What were the frequency and percentage of the parents who were fathers? Round your answer to the nearest tenth of a percent (%).

10. What are the mean and SD for the illness severity of the children with asthma in this study? What was used to measure the illness severity variable in this study? What resources are available for nurses to use to decrease illness severity scores for children with asthma

Grove, Susan K., Daisha Cipher. Statistics for Nursing Research: A Workbook for Evidence-Based Practice, 2nd Edition. Saunders, 022016. VitalBook file.

Book name' statistics for nursing research' workbook for Evidence-based practice 2nd edition writer Susan K Grove,Daisha J ciper. SHOULD BE 100% PLAGIARISM FREE. MS word use to answer this question.

EXERCISE 1-HLT-362V

Name

Institution

Date

QUESTIONS TO BE GRADED;1. In Table 1, identify the level of measurement for the income variable. Provide a rationale for your answer.

The income variable is the interval level of measurement, where the income levels are ranked and intervals are equally spaced. The lowest income level is $40,000 and the difference with the next interval was $ $ 60,000.2. In Table 1, what is the level of measurement for the marital status variable? Provide a rationale for your answer.

The marital status variable is in the nominal level of measurement as people are categorized into married, not married and divorced groups, which are distinct categories. The nominal level of measurement indicates qualitative rather than quantitative differences.3. What is the mode for the child gender variable in the diabetes group? Provide a rationale for your answer.

The child gender variable placed into the diabetes group was 15 males accounting for 40% of the sample of 37 children, since the child mean for the diabetes group is 6.8, the male gender is also likely to follow eh same pattern and the modern would be 7.4. What are the mean, SD, and range for the variable age of the 66 children included in this study?

The Child mean age was 6.8 years (SD = 1.01; range = 5

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