Sampling

The collection of data from respondents requires a lot of resources such as time and monetary costs. Therefore it is not always possible to collect data from the entire target population. For instance in the current study, collecting data from all SMEs conducting construction activities in King William’s Town and Port Elizabeth was very expensive and time consuming. Hence, the researcher collected data from a sample which had the same characteristics with the population. According to Babbie (2009:213), a sample is a representative of the population from which it is selected if the aggregate characteristics of the sample closely approximate those same aggregate characteristics in the population. Sekaram (2003:269) points out that there are two (2) types of sampling methods namely the non-probability sampling and probability sampling. However, for the purpose of this study, the researcher employed the probability sampling method.

4.4.2 Probability sampling technique

According to Sekeran and Boungie (2010:275), probability sampling refers to selection procedures in which elements are randomly selected from the sampling frame and each element has a known, non-zero chance of being selected. The probability sampling comprises of various sampling designs. Table 4.1 depicts on the sampling designs.

Table 4.1 Probability sampling design

SAMPLING DESIGN DESCRIPTION ADVANTAGES DISADVANTAGES

Simple random

sampling

All elements in the

population are

considered and

each element has an equal chance of High generalizability of findings. Not as efficient as stratified sampling

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being chosen as the subject.

Systematic sampling Every nth element in the population is chosen starting from a random point in the sampling frame Easy to use if sampling frame is available. Systematic biases are possible. Stratified random sampling Population is first divided into meaningful segments; thereafter subjects are drawn in proportion to their original numbers in population. Based on criteria other than their original population numbers. Most efficient among all probability designs.

All groups are adequately

sampled and

comparisons among groups are possible. Stratification must be meaningful. More time consuming than simple random sampling or systematic sampling.

Sampling frame for each stratum is essential.

Cluster sampling Groups that have

heterogeneous members are first identified; then some are chosen at random, all the members in each of

the randomly

chosen groups are studied.

In geographic

clusters, costs of data collection are low.

The least reliable and efficient among

all probability

sampling designs since subsets of clusters are more homogeneous than heterogeneous.

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Area sampling Cluster sampling

within a particular area or locality. Cost-effective. Useful for decisions relating to a particular location. Takes time to

collect data from an area.

Source adapted Sekaran and Bougie (2010:279)

After carefully analysing the advantages and disadvantages of the probability sampling methods in table 4.1, the researcher chose and employed the simple random sampling method in this study. The main objective of choosing the simple random method was that each member of the population had an equal likelihood of being selected and also there was little or no room for interviewer bias. This therefore means that each SME owner/manager in the construction industry in King William’s Town and Port Elizabeth had an equal chance of being selected. The availability of a sampling frame from the CIDB allowed the use of simple random sampling technique.

4.4.3 Sample size

In Section 4.4.1, a sample has been defined as a representative of the population from which it is selected if the aggregate characteristics of the sample closely approximate those same aggregate characteristics in the population. Thus, a researcher should be in a position of identifying or determining an ideal sample size which represents the target population. Zindiye (2008:126) points out that there are no specific rules for determining sample sizes. Chimucheka (2012:97) argues that a sample size ought to be big enough to ensure that reliable and valid conclusions can be made about the population. In most cases the calculation of a sample size depends on the research approach employed and the nature of the population. The study at hand employed a rao-soft sample size calculator to compute the sample size. A rao-soft sample size calculator is a statistical software package which is used to compute sample sizes. The rao-soft sample size calculator relies on three most essential variables namely:

 margin of error;

 confidence level and

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The researcher assumed a 5% margin of error, 95% confidence level and 50% response distribution. The minimum sample size required for the study obtained from the computation using a population of 133 active SMEs in the construction industry was 99 contractors. The sample size is relatively small since there is a high failure rate of SMEs in the construction industry. Van Wyk (2003) agrees that there is a high failure rate of SMEs.

4.5 RESEARCH METHODOLOGY

In Section 4.1, it was established that the research methodology comprises of two main approaches which are used to solve research problems, namely the quantitative and the qualitative research. Musara (2010:88) agrees that research methods are classified into quantitative and qualitative research methods. Cooper and Schindler (2003:45) points out that quantitative research encompasses the systematic and scientific collection of primary data to investigate the quantitative properties and phenomena and their relationship with the intention of projecting the results to a wider population, whereas the qualitative research employs non- numerical means to understand a problem and provide a solution. Table 4.2 depicts different characteristics of quantitative and qualitative research.

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Table 4.2 Characteristics of quantitative and qualitative research

QUANTITATIVE RESEARCH

APPROACH

QUALITATIVE RESEARCH

APPROACH

Objective Subjective

Research questions: How many?

Strength of association?

Research questions: What? Why?

"Hard" science "Soft" science

Literature review must be done early in study

Literature review may be done as study progresses or afterwards

Test theory Develops theory

One reality: focus is concise and narrow Multiple realities: focus is complex and broad

Facts are value-free and unbiased Facts are value-laden and biased

Reduction, control, precision Discovery, description, understanding, shared interpretation

Measurable Interpretive

Mechanistic: parts equal the whole Organismic: whole is greater than the parts

Report statistical analysis.

Basic element of analysis is numbers

Report rich narrative, individual; interpretation. Basic element of analysis is words/ideas.

Researcher is separate Researcher is part of process

Subjects Participants

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Hypothesis Research questions

Reasoning is logistic and deductive Reasoning is dialectic and inductive Establishes relationships, causation Describes meaning, discovery

Uses instruments Uses communications and observation

Strives for generalization

Generalizations leading to prediction, explanation, and

Understanding

Strives for uniqueness

Patterns and theories developed for understanding

Highly controlled setting: experimental setting (outcome oriented)

Flexible approach: natural setting (process oriented)

Sample size: n Sample size is not a concern; seeks

"informal rich" Sample

"Counts the beans " Provides information as to "which

beans are worth counting" Source adapted from Anderson (2006)

The study at hand employed the quantitative research methodology. The quantitative research method was mainly used because the study met the criteria characteristics depicted in table 4.2. Musara (2010:90) points out that quantitative research are classified as either descriptive (subjects usually measured once) or experimental (subjects measured before and after a treatment). A descriptive study establishes only associations between variables. An experiment establishes causality. A descriptive research was followed in this study. A descriptive research provides answers to questions of who, what, where and how of the phenomenon of interest (Gerbel-Nel et al., 2005:33). The descriptive research in this study established the

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level of awareness and use of risk management techniques by SMEs in the construction industry.