View/Open - ARAN - National University of Ireland, Galway

125
Chapter 3 Research Framework
words, reliability is about how stable the research instrument is and if it was used
repeatedly would the same results be yielded. Validity is defined as “the degree to
which an instrument measures what it is intended to measure” (Polit and Beck, 2006:
512). Validity is about demonstrating confidence in the accuracy of the results, or in
other words, do the results actually reflect what is happening. There are several types
of validity and the main ones referred to in the literature are construct validity,
criterion validity and content validity (Burns and Grove, 2010; Parahoo, 2006).
Wainer and Braun (1998) explained that the “construct” in construct validity is the
initial concept, notion, question or hypothesis that determines which data is to be
gathered and how it is to be gathered. It determines the validity of a measure or scale
and how meaningful it is. Criterion validity is a measure of how well the measure or
scale can test or predict future outcomes or how well the scale’s score correlates with
some “gold standard” measurement scale of the same variable. Finally, content
validity is the extent to which the items in the scale reflect the measured concept
(Patton, 2002).
An additional approach that researchers use in order to add to the rigour of the data
analysis for both qualitative and quantitative data is triangulation, and the next
section will discuss this.
3.10.3 Triangulation and Rigour
Denzin and Lincoln (1994) stated that triangulation adds rigour, breadth and depth to
any investigation. According to Shih (1998), triangulation was first used in the social
sciences as a metaphor to characterise the use of multiple methods to measure a
single construct. However, Sandelowski (1986) argues that there is no consensus
about the use of the term “triangulation”. Shih (1998) explained that there are two
purposes for adopting triangulation: for confirmation and for completeness.
Triangulation to confirm the data focuses on the measurement of discrete variables,
while triangulation for completeness focuses on capturing a more complete, holistic
and contextual portrayal of the unit(s) under study. Each data source adds to the
overall findings.
Several authors have described the types of triangulation available to the researcher
(Polit and Beck, 2006; Adami and Kiger, 2005; Speziale and Carpenter, 2003;