The Sudden Infant Death Syndrome Essay

Introduction

1. Hair color, gender, and birthplace are examples of qualitative variables. A qualitative variable represents quality as opposed to a numerical measurement (Stangor 17). Therefore, it cannot be expressed in a numerical form. The measurement of a qualitative variable involves a nominal scale, which groups data into categories (Creswell 90). Hair color can be black, brown, brunette, or blonde while gender can be either male or female. Birthplace is a person’s place of origin in terms of country, state, or county. The place of birth often varies between individuals.
   Qualitative variables can be categorical or ordinal. A categorical variable comprises “two or more categories that lack intrinsic ordering” (Creswell 91). A person’s gender can be either male or female. In this case, there is no clear ordering of the two categories. Hair color comprises many categories that cannot be ordered from the lowest to the highest. Similarly, birthplace, as a variable, cannot be ordered in clear categories. Therefore, a categorical variable has no clear ordering or ranking of categories.
2. Radioactive emissions recorded by a Geiger counter are examples of quantitative variables. A quantitative variable is expressed in terms of numbers. Numeric scales, namely, “interval, ratio, or ordinal scales”, are used to measure quantitative variables (Carlson and Winquist 27). Radioactive emissions are measured in counts per second, which are numerical or quantitative data. Counts per second are represented as a ratio of the amount of radioactive particles emitted from a radioactive substance in a second.
   Ordinal variables show a “clear ordering of the categories” (Creswell 92). They allow ordering of values into low, medium, and high. The categories can be ranked from the smallest to the largest with the difference between them being either uniform or unequal (Stangor 34). An interval scale is used to measure a variable with equal sizes between categories while an ordinal scale ranks values into unequally spaced categories.
   A ratio scale is a numerical scale that “lacks a clear zero point” (Stangor 35). In a ratio scale, the size interval represents a ratio or proportion of the total values. Counts per second represent a ratio scale that measures radioactivity dose in seconds. Therefore, the amount of radioactive particles emitted during nuclear decay depends on time. A longer decay period will result in high radioactive emissions.
3. Researchers studying the sudden infant death syndrome (SIDS) would be interested in the risk factors. SIDS is the “unexpected death of healthy infants” aged under one year (Hymel 422). Investigations into SIDS prevalence in a given population usually focus on factors related to the pregnancy, birth, and infant lifespan. Therefore, the common study variables include demographic variables, such as maternal age and the age of the infant. Researchers also investigate variables related to the pregnancy and birth, such as maternal pregnancy history, birth weight, smoking/drinking during pregnancy and UTI infections (Sullivan and Barlow 148). Other variables related to SIDS death include the last sleep duration, co-sleeping, infant stress, and insufficient sleep.
4. Table 1: Frequency table of SIDS with the relative frequency

• The relative frequency of SIDS at the age of six months
  In quantitative data, the number of occurrences in a particular data set is called the frequency (Maronna, Martin, and Yohai 77). Relative frequency is the ratio between particular occurrences and the total frequency. The frequency of deaths at the age of six months is 10. Thus, in this study, the number of infants who died suddenly in their sleep at the age of six months is 10.
  From Table 1 above, the total frequency of infant deaths is 72. It represents the cumulative frequency of infants dying under the age of 18 months. Therefore, relative frequency of SIDS is the ratio between the sample size (deaths) at the age of six months and the total frequency.
  Relative frequency (RF) = Sample size/Total frequency
  = 10/72
  = 0.139 or 13.9%
  The result indicates that 13.9% of the infants in this study died at the age of six months. It can also be expressed as a fraction (5/36) or decimal (0.139). It represents the probability of an infant dying at the age of six, ten, twelve, or eighteen months. In Table 1 above, the relative frequencies sum up to one. From the table, the proportion of infants dying at the age of 10 is 20/72 or 0.278. In addition, 47.2% and 11.1% of the infants sampled died at the age of 12 and 18 respectively. The death rate shows that infants are more likely to die at the age of 12 months from SIDS than at any other age. The risk of dying from SIDS is lowest at the age of 18 months.
• The proportion of SIDS death for infants less than or equal to 10 months old
  Table 2: Frequency table of SIDS with the cumulative relative frequency

In Table 2 above, the second and third rows contain the number of infants dying before or at the age of 10 months. There are 10 + 20 = 30 infants dying between the age of six and ten months. Therefore, the proportion of deaths of infants aged 10 and below months is 30/72 or 41.7%. In addition, the cumulative relative frequency at the age of 10 months is equivalent to 41.7%. Thus, 41.7% of the 72 deaths recorded involved infants aged between six and ten months.

Cumulative relative frequency is “the accumulation of previous relative frequencies” (Maronna, Martin, and Yohai 77). The cumulative relative frequency at each row is the sum of the frequencies in the preceding and current rows. In Table 2, the cumulative relative frequency in the first, second, and third rows is 0.139, 0.417, and 0.889 respectively. The cumulative relative frequency in the last row is one.

Key Concepts

I will seek clarification on the following concepts:

1. How to assign numerical values to qualitative variables to aid in calculations
2. How to differentiate between qualitative and quantitative variables
3. How to distinguish between categorical and ordinal variables in qualitative research
4. How to determine the average of a qualitative variable

References

Carlson, Kyle, and John Winquist. An Introduction to Statistics. New York: SAGE Publications, 2014. Print.

Creswell, John. Research Design: Qualitative, Quantitative, and Mixed Methods Approaches. New York: SAGE Publications, 2013. Print.

Hymel, Peter. “Distinguishing sudden infant death syndrome from child abuse fatalities.” Pediatrics 118.1 (2006): 421–427. Print.

Maronna, Ricardo, Douglas Martin, and Victor Yohai. Robust Statistics: Theory and Methods. London: Wiley, 2006. Print.

Stangor, Charles. Research Methods for the Behavioral Sciences. Wadsworth: Cengage Learning, 2011. Print.

Sullivan, Fred, and Sam Barlow. “Review of risk factors for Sudden Infant Death Syndrome”. Paediatric Perinatal Epidemiology 15.2 (2001): 144–200. Print.