Zero hour contracts have been debated for a long time. They became controversial after they were common during the financial crisis between 2007 and 2009. Employees working in zero hour contracts have no specific working hours though some of them support the existence of the contract. Having employees working in these conditions is beneficial to the company since there are no agency fees paid. Companies can also hire employees when they suddenly require more staff, for example, during a company event. These contracts affect the motivation of the employees based on whether they are allowed to work for other companies or restricted to working for one.
Some individuals prefer this contract since they do not have to commit to working a certain number of hours in a week. During their free time, they can work on other things they are passionate about or responsible for. They are incredibly beneficial to students and retirees who only need some extra money and whose stability does not depend on the income they generate. However, a job without steady hours can be difficult for individuals who have to provide for families or pay bills. It may result in individuals being anxious since their finances are not adequately catered to. Workers may, therefore, have to accept work even when they are not ready to guarantee that they have a place in the company in the future, and they have income (Pyper, 2013). Some zero hour employees also report feeling undervalued; thus, they may not be able to speak up when they have issues. They may also feel underqualified for other roles in the workplace resulting in them sticking to these contracts instead of being permanently employed. To ensure these employees stay motivated, their employers can provide them with benefits like providing them with annual leave, a statement of the terms and conditions of their contract and salaries at the minimum wage. Employees may then work harder in the hopes of earning a promotion or not work as hard because they are not assured of work with the company. Therefore, all employees should be treated fairly and equally to encourage a safe working environment.
Data collection is an integral part of the research. Proper techniques, therefore, have to be used to ensure the collection of qualitative data is done consistently and scientifically. Great data collection techniques will make sure that the research findings are accurate, reliable and valid. Many factors are considered when choosing the appropriate method of data collection. They include the cost of the data collection method, the time needed to complete it, the staff required for the process, and whether the people, information and records required are accessible. There are also sampling issues that need to be taken into consideration. Is there a sampling frame for the population to be studied? How large of a sample is needed and how generalizable will the results be? Finally, there is the question of whether approval is required for this study to take place. Once all of these issues are covered, data collection can begin.
I will be using sampling techniques in my study. Sampling is a method used by researchers to systematically choose a small number of individuals or items from a larger group who will be subjected to observation according to the objectives of the study. For example, 100 individuals may be picked form a population of 1000 people to be a sample. This subset of the population must suitable for research purposes and meet the cost, time and convenience goals. Sampling techniques also consider factors such as the size of the population, its diversity, objectives of the study, how precise the results of the study should be, the financial implications of the survey amongst others. There are two types of sampling; probability and non-probability sampling (Tennakoon, 12). In probability sampling, the probability that someone will be chosen is usually the same. It requires more effort, but it is, fortunately, more accurate. It is also referred to as random sampling. Non-probability sampling is usually based on judgement. These two sampling methods are further subdivided into groups. In probability sampling, there is simple random sampling, systematic sampling, stratified sampling and cluster sampling. A non-probability sampling includes quota sampling, purposive sampling, self-selection sampling and snowball sampling.
There are pro and cons to each sampling method. In simple random sampling, each individual is given an equal chance to become a subject. Its advantages are that assembling a sample population is easy. It also represents the population accurately, and there is an unbiased selection which is usually random. The disadvantages of this sampling method are that first, a list of all members of the population is required. It needs to be up to date, and this becomes difficult in mostly populated areas. Systematic sampling is where the first unit selected determines how the other samples will be collected (Sharma, 2017). Its pros are that the sample is more even across the population and easier to conduct than a random selection. However, it may be affected by a hidden trait in the community that happens periodically. Stratified sampling is when the population is divided into smaller numbers based on shared characteristics, and a random sample from each group is chosen. It is beneficial since the chances of human bias taking over are minimized, and accurate generalizations can easily be made. Unfortunately, it is not worthwhile if the population cannot be divided into subgroups since data from each group is usually vital. Cluster sampling is done on groups that occur naturally. Its pros are that costs re reduced and there is an increase in the variability of results. It is also easy to use this method on a large population. Its cons are that there may be biased sampling and errors in the procedure.
Quota sampling is whereby the aim is to end up with groups which accurately represent the broader population. Its benefits are that it is quicker than probability sampling techniques, and it ensures the groups chosen are not overrepresented. However, random selection is not utilized, which might make it challenging to recognize errors. Generalizations cannot be made, and the sample size required is usually larger requiring more costs and time to be spent. In purposive sampling, the judgement of the researcher is used to choose groups. Fortunately, generalizations can be made in this process, and the research has multiple phases that provide a range of no-probability sampling techniques (Sharma, 2017). The cons are that there may be researcher bias and the subjectivity of the study heavily relies on the way the units were selected. Therefore, convincing the reader that appropriate judgements were made is difficult. Self-selection sampling is when individuals are allowed to choose whether they want to take part in the research. It can reduce how much time is required to look for appropriate units, and these units will likely be happy to take part in the study, thus providing more insight. However, there may be self-selection bias in the participants, which can result in the sample not being an adequate representation of the population. Snowball sampling is when study subjects choose other subjects from people they know (Etikan, 2016). Its advantage is that the researcher can access groups that are difficult to contact. However, it is difficult to determine errors that are made and impossible to make generalizations since it is not a representation of the population being studied.