Three sample populations are obtained in this analysis, and range in size from a
minimum of 19 participants in the focus groups to a maximum of 367 participants in
the CVM. An 11.3 per cent response rate was achieved by the focus groups. However,
response rates to qualitative research are rarely reported in the literature, rather the
focus is on the reasons why participants did not participate. Many women who were
invited to participate in the focus groups reported that they did not feel comfortable
sharing their views in a group setting, while others cited their inexperience as the main
reason why they did not want to participate. A response rate of 22 per cent (37 women)
was expected, although 18 women could not attend the scheduled sessions.
The response rate to MAMS was poorer than anticipated. A response rate of 28 and
36.7 per cent was generated by the DCE and CVM, respectively. This contrasts sharply
with the pilot studies which achieved a considerably higher response rate. For instance,
in its second iteration, MAMS achieved a 60 per cent response rate. It is difficult to
determine the reasons for the low response rate to the main DCE and CVM. It is
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the surveys were distributed during a mid-term break. The response rate in CUMH
was stronger than all other hospitals for the DCE and CVM; therefore, it is assumed
that the response rate was stronger in this hospital as women knew the research centre
and its affiliated researchers. It is believed that there may have been reluctance among
women from other maternity units to participate in the studies given their lack of
knowledge of the research centre.
Small sample size likely affects the representativeness of the samples to the population
due to non-response bias. This bias describes the differences that might exist between
respondents and non-respondents. Ideally, the demographic characteristics of both
groups are compared to explore whether meaningful differences exist between
responders and non-responders. However, this study was not granted ethical approval
to access the personal information of the sample frame. Therefore, it was not possible
to compare the characteristics of the responders with non-responders.
It is difficult to compare the samples with the low risk obstetric population also. While
the National Perinatal Reporting System (NPRS) publishes nationally representative
data on the obstetric population on an annual basis, it cannot distinguish between low
and high risk women (NPRS 2013). The data presented in this thesis describes the
demographic characteristics of low risk women, which are expected to differ to the
overall obstetric population. For instance, it is expected that the average age of low
risk women is lower than the average age of the obstetric population, while the
percentage of mothers delivering for the first time is also expected to be higher than
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Table 4.10 compares the samples with the general obstetric population using available
data. Also included in the table is the demographic information of women included in
the MidU (2009) study. These data are compared with the samples presented in this
thesis as they are expected to more closely resemble the demographic characteristics
of low risk women. Across the DCE and CVM, the average age of participants is lower
than the general obstetric population, although it is higher than the MidU study. The
average age of participants in the focus groups is higher than all other data. This may
be due to the fact that 42 per cent of this group comprised private patients, which were
slightly older than public patients. The percentage of women delivering for the first
time in in the samples presented in this thesis is comparable with the MidU (2009)
study, where all data are higher than the obstetric population. There are some
differences in the number of women that report being married. The CVM data are
similar to the general obstetric population data, while the DCE data closely resemble
the MidU (2009) study data. Irish nationality is comparable across all data, where
roughly 75 per cent of deliveries are by women with an Irish heritage.
Differences in demographics may be attributable to differences in sample size. For
instance, the MidU (2009) study contained 552 participants. At its highest, the CVM
included 331 women, while the DCE contained 96 participants. With only 19 women
in the focus groups it is difficult to meaningfully compare these demographics with
other data. Non-response bias may be a problem with the data; however, it is
impossible to reveal the extent of this bias. In addition, the data obtained from the
MidU (2009) study might not reflect the low risk obstetric population. Without
nationally representative data on the demographic characteristics of low risk women,
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Table 4.10: Demographic characteristics of sample compared with MidU study and general obstetric population
Focus groups DCE CVM MidU* General
population†
Age, mean (SD) 32.3 (5.2) 30.2 (4.3) 31.2 (4.1) 28.7 (5.0) 31.9
Parity 9 (47.3) 51 (53.1) 155 (46.8) 276 (50.0) 28,169 (39.1) Married No data 51 (53.7) 223 (67.6) 312 (56.5)^ 46,071 (64.0)
Irish nationality 14 (73.7) 71 (74.4) 258 (78.4) No data 54,709.4 (76.0)
Total 19 96 331 552 71,986
Notes:
Abbreviations: DCE, discrete choice experiment; CVM, contingent valuation method.
* MidU (2009). † NPRS (2013).
^ Includes women that were not separated.
Nevertheless, it is assumed that the data presented in this thesis closely resemble low
risk women given comparable characteristics with the MidU (2009) study and overall
population.
4.5 Conclusion
The various stages involved in designing the DCE and CVM are described in detail in
this chapter. The data instruments used for both SP techniques are described in terms
of distribution, response rates, and participant characteristics.
Each SP technique carries several important objectives. In terms of the DCE, a number
of discrete choice analyses are performed, as outlined in section 4.2. In the first instance, women’s strengths of preferences for the different attributes is explored on the unforced choice scenario (section 4.2.2.1). The DRC is then explored to identify
the best specification of preferences across the unforced and forced choice scenarios
(section 4.2.2.2). Underlying assumptions about preferences are explored using a
flexible model and compared with the benchmark model to find the model of best fit
(section 4.2.2.3). Two factors that are assumed to influence preferences are
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different attributes (section 4.2.2.3). The DCE is also used for policy analysis and to
measure welfare change. Women’s individual WTP for care in a CLU, MLU, and DOMINO scheme is calculated using state-of-the-world models (section 4.2.3.1). An
analysis of potential market uptake for the different models of care is then calculated
(section 4.2.3.2), and used to measure welfare change (section 4.2.3.3).
In terms of the CVM, several objectives contained within this analysis are also
presented in this chapter (section 4.3). First, the data are used to explore the
characteristics of zero and protest responders against positive WTP responders
(section 4.3.2.1). Positive WTP responses are then analysed to elicit CV, and
compared across several important demographics such as experience, geographic
location, income, and PHI (section 4.3.2.2). Regression analysis is performed to
predict WTP (section 4.3.2.2), and to investigate the characteristics of women that
prefer consultant-led care over midwifery-led care (section 4.3.2.3).
Given the extent of the different objectives contained within the DCE and CVM, the
findings of the different SP techniques are reported in separate chapters. The results
from the DCE are presented in Chapter 5, while the results of the CVM are presented
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5 THE DISCRETE CHOICE EXPERIMENT
5.1 Introduction
This chapter presents the results of the various analyses employed to address the
objectives of the DCE. In section 5.2, a summary of the sample and breakdown of
respondents’ choices is provided. Section 5.2.1 presents the first analysis of the DCE, which aims to identify the best specification of the continuous attributes and
preferences on the unforced choice scenario. The DRC is explored in section 5.2.2.
This section seeks to identify the best specification of preferences across the two
choice scenarios. Choice consistency is compared between the unforced and forced
choice scenario in this section also. Section 5.2.3 explores flexible models and
investigates how preferences change when assumptions about respondents’ choices
are relaxed. Finally, in section 5.2.4, issues relevant to policy analysis and welfare
measures are explored. This includes an analysis of market uptake and estimation of
CV. A summary of the various findings presented in this chapter is outlined in section
5.4 and placed in the context of existing literature.