Page 137 - Encyclopedia of Nursing Research

104 n DATA CoLLECTion METhoDS

Loftus, 1993; Rozeboom, 1960; Walker, 1986), quantitative analyses, computer programs for
null hypothesis statistical testing still domi- qualitative data analysis are merely aids for
D nates analytic approaches. the tedious and error prone tasks of analysis.
Some of the articles and arguments Using them still requires the investigator to
about the limits of null hypothesis statistical make the relevant and substantive decisions
testing have led to more emphasis on the use and interpretations about codes, categories,
of confidence intervals. Confidence intervals and themes.
provide more information about our find- Although quantitative data analysis
ings, particularly about the precision of pop- allows for statistical probabilistic statements
ulation estimates from our sample data, but to support the investigator’s interpretations
they are based on the same null hypothesis and conclusions, qualitative data analysis
statistical testing logic that generates p val- depends more exclusively on the strength
ues. Thus, confidence intervals are subject to and logic of the investigator’s arguments.
the same issues with respect to Type 1 errors nonetheless, both types of data analysis ulti-
(rejecting the null when it is true) and Type 2 mately rest on the strength of the original
errors (failing to reject the null when it is study design and the ability of the investiga-
false). tor to appropriately and accurately execute
increased attention and sensitivity to the analytic method selected.
factors that contribute to findings of statisti-
cal significance has also led to more attention Lauren S. Aaronson
to power, sample sizes, and role of effect sizes
(for substantive significance) for valid quan-
titative data analysis. if the sample size is too
small, the study may be underpowered and Data ColleCtion MethoDs
unable to detect an important finding even
if it is there. Conversely, if the sample size is
too large, the study may be overpowered and in research, data are the pieces of informa-
may result in statistically significant findings tion that are gathered in an effort to address
that are substantively or clinically insignif- a research question. Data collection typically
icant. Either could be challenged on ethical is one of the most challenging and costly
grounds, stressing the importance of appro- steps in the research process. Researchers
priately powering studies for the planned make a number of decisions in designing a
data analysis. data collection plan, and these decisions can
in contrast to quantitative data analysis have a profound effect on the quality of evi-
which requires that the investigator assign a dence that a study yields. nurse researchers
numeric code to all data before beginning the use a wide variety of methods for collecting
analyses, qualitative data analysis consists data, and these methods vary on a number of
of coding words, objects, and/or events into important dimensions.
meaningful categories and/or themes as part A fundamental dimension involves
of the actual data analyses. Because qualita- whether the data being collected are quan-
tive data analysis involves nonnumeric data, titative or qualitative in nature. Quantitative
there are no statistical probabilistic tests to data yield information about a research var-
apply to the coding of qualitative data. iable in numeric form, ranging from simple
Coding of qualitative data historically binary values (e.g., 1 = yes, 2 = no) to more
has been done manually, but computer complex numeric expressions (e.g., values
programs (e.g., QSR) are now available to for the body mass index). To collect quanti-
aid the investigator in this laborious effort. tative data, researchers use structured meth-
however, as with the computer programs for ods and formal instruments in which the