Question

Indicate whether we should trust the results of the study. Is the method of data collection biased? If it is, explain why. Ask a random sample of people in a given school district, "Excellent teachers are essential to the well-being of children in this community, and teachers truly deserve a salary raise this year. Do you agree?" Use the results to estimate the proportion of all people in the school district who support giving teachers a raise.

Short answer

The method of data collection is biased due to the leading nature of the question asked in the study. While the results can give an indication of the sentiment in the district, the validity is questionable due to this bias. Therefore, it would be advised not to fully trust the results of this study.

Step-by-step solution

Evaluate the question

Firstly, let's look at the question that is being asked: 'Excellent teachers are essential to the well-being of children in this community, and teachers truly deserve a salary raise this year. Do you agree?' This question is leading and suggestive in nature, introducing a bias into the method of data collection. It has a built-in assumption which is that excellent teachers are essential and they deserve a raise. This phrasing could potentially sway the respondents towards agreeing.

Evaluate the sample selection

Secondly, let's evaluate the sample selection. The people surveyed are a random sample from a given school district. If the sample is truly random and representative, then this aspect of the study will not introduce bias.

Evaluate the validity of the results

Finally, based on the bias introduced in the way the question was phrased and assuming that the sample chosen was truly random and representative, we can conclude that while the results might give an indication of the sentiment in the district, the bias in the question poses a potential problem for the validity of the results.

Key concepts

Survey Question Bias

When conducting research, it's crucial to draft unbiased survey questions to get a true sense of the respondents' opinions. In the given exercise, the question posed to the random sample is, unfortunately, a prime example of survey question bias. With its leading language, the question suggests an expectation of a positive response by stating a widely valued belief ('Excellent teachers are essential...') before asking if they deserve a raise.

Improving this question requires removing the suggestive statement and asking more directly, 'Do you believe teachers in this community should receive a salary raise this year?' This neutral phrasing allows individuals to provide an answer based on their own beliefs rather than feeling pressured to agree with the sentiment the question's phrasing implies.

Sample Selection

The sample selection process greatly influences the reliability of research findings. For the survey on whether teachers deserve a raise, a random sample from a given school district is chosen, which is a good practice to avoid selection bias. It is imperative for the randomness of the sample to be assured to maintain its representativeness of the larger population. This means that every individual within the target population should have an equal chance of being selected.

To improve upon this, researchers could use measures such as stratified sampling to ensure various subgroups within the district are equally represented, reflecting a diverse range of views from parents, non-parents, teachers, and other stakeholders.

Validity of Research Results

The validity of research results hinges on how rigorously the study is designed and conducted. A key component to consider is whether the research methods used could lead to biased outcomes. As identified in the question phrasing from our exercise, the suggestive language introduces bias, impacting the study's validity. Even with a truly random sample, if the question presented leads respondents towards a particular answer, the results cannot be taken as an accurate representation of the general opinion.

Ensuring validity would involve crafting neutral questions and establishing clear, unbiased procedures for selecting a sample. Furthermore, analyzing the consistency of results across different samples or conducting similar studies in varied settings can help reinforce the validity of the initial findings.