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Answer:

Every details in a reference are important. Harvard reference style has a standard format. Even the full stop and commas must be put only in the prescribed format.

For a journal article the reference list using Harvard referencing includes:

• Author or authors. The surname is followed by first initials.
• Year of publication of the article.
• Article title (in single inverted commas).
• Journal title (in italics).
• Volume of journal.
• Issue number of journal.
• Page range of article.

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1.Three ways bias may occur in a quantitative research study:

Bias is the mortal enemy of all surveys, and as a survey creator it’s important to guard against it to make sure you get reliable results. Over the years, we’ve offered best practices for designing surveys that address different types of bias in research, such as unbiased wording, structure, and styling. But if you’re not careful, there are a few ways you can still introduce bias without even knowing it.

Of all the different types of bias in research, many come directly from the survey writer. This bias is sneaky. It’s caused by survey creators who innocently influence the results in an effort to reach their desired outcome. But in doing so, they influence the credibility and value of the results themselves.

(i) Asking the wrong questions

It’s impossible to get the right answers if you ask the wrong questions. Unfortunately, survey results are easily compromised by questions that fall short of capturing the entire scope of a survey’s issue. Say, for example, your survey was created to understand your employees’ favorite type of pizza. You ask, “Do you like pepperoni, meat lovers, or vegetarian pizza the best?” Though there are many other types of pizza, they did not come to your mind and were left out of the question. Now instead of measuring the most popular pizza, the study measures the preference between these three types.

(ii) Surveying the wrong people

Choosing your respondent group may seem like a no-brainer, but it often leads to something called selection bias. When conducting a survey, it’s imperative to target a population that fits your survey goals. If you incorrectly exclude or include participants, you may get skewed data results.

Usually this bias happens when you lack of a clearly defined target population. For example, say you want to limit your survey to people with low economic standings. This population could be defined in many ways: people with low income, people who lack disposable income, or people who have a low net worth after taking into account their property, income, and debt. Each of these three descriptions can successfully be used to address the broad population you hope to reach. But, each definition could provide different results for your study.

(iii) Misinterpreting your data results

This form of bias is introduced when raw data is transformed into misinterpreted findings. Usually it’s a case of inappropriate or inaccurate statistical techniques, which lead to the incorrect interpretation of the survey results. For example, bias can come into play when a survey creator gets excited about a finding that meets their hypothesis but overlooks the fact that the survey result is only based on a handful of respondents.