Discussion Board Forum 1 – Variables, Research Questions, and Data Coding
BUSI820 – Quantitative Research Methods
Liberty University
D1.1 Variables
Independent and independent variables, one variable is usually the effect or cause of
another. The purpose of research studies is to find out unknown characteristics or qualities of
something or a person. For the qualities to get rated, the variables need to get defined. An
independent variable usually influences the dependent variable directly or indirectly.
Independent variables are of two types, which are active and attributes. An active independent
variable can get its values manipulated to study how it affects other variables; therefore, it is
necessary to infer cause (Morgan et al., 2013). However, one cannot always infer cause with the
active variable as not all aspects can get changed. For example, anxiety levels can get altered to
determine whether pain reduction medication affects patients' responsiveness. On the other hand,
causal inferences are questionable when attribute variables cannot get changed, such as a
person's age or nationality, but people of different ages can get studied.
D1.2 Research Questions I
Researchers use research questions to give answers, solutions, or insight on current world
problems. There are three different types of questions which are different, associational, and
descriptive. Descriptive research questions are aimed at given details concerning the variables to
get measured (Morgan et al., 2013). The descriptive question aims to quantify the variables
getting studied and uses words such as what percentage? What are? What is? How much?
Among others. On the other hand, the difference question is also known as comparative research
questions. These questions aim to scrutinize the difference between one or more groups and the
dependent variable. The questions start by asking what the difference is between? A particular
dependent variable and a certain group of people. Lastly, associational questions are also referred
to as relationship research questions. These questions are usually interested in trends,
interactions, associations, or casual relationships among several group variables. Most
associational questions are phrased, such as what is the relationship between? Certain
independent variables and dependent variables.
D1.3 Research Questions II
D1.3a Association Question
Examples of the research question will be bases on HSB variables such as mosaic pattern,
visualization score, and religion. Example of an association question include:
1. What is the relationship between mosaic pattern and wealth in ancient Rome?
2. What is the relationship between creativity and data visualization among technology
firms?
3. What is the relationship between faith and reason in religion?
D1.3b Difference Question
Example of the different questions include:
1. What is the difference between the mosaic pattern and the various types of patterns in art?
2. What is the difference between creative visualization and data visualization?
3. What is the difference between an atheist, a pagan, and a believer?
D1.3c Descriptive Question
Examples of descriptive questions include:
1. How often do people in America opt for Mosaic patterns for decorating their houses?
2. What proportion of banks implements visualization scores in their business operation in
the US?
3. How often do religious people fall into temptation after they have decided to follow the
righteous way?
D1.4 Data Coding I
I think several changes need to add for the data coding of questionnaires to make them
more reliable. For instance, the questionnaire which connects the researcher, interviewer, and
respondent should be readable and easily analyzed by the respondent. The questionnaire should
not become too hard to understand as the respondent might get bored. On the other hand, the
computer will also find it difficult when coding the responses in a much understandable format.
More so, it should be concise as it takes a lot of time to grab someone's attention and seconds to
lose it. The questions should be complete and straight to the point, which will make coding
easier. The researcher should write up a list of topics that they want to get information from to
avoid asking questions that are unrelated to the survey. Another change is to ensure that the
language used on the questionnaire and coding of data is not too technical for easier
understanding.
D1.5 Data Coding II
The completed questionnaire in chapter two, problem 2.1, had several problems. The
questionnaire suggested that the respondents circle or supply their answers. The instructions were
not quite clear at the very end; hence there is no uniformity, especially from participant seven to
twelve. The lack of uniformity will make it difficult when coding the data to come up with
overall easy-to-understand information (Morgan et al., 2013). For example, some participants
choose two answers; others opted to indicate an X or circle, while others decided to write down.
The questionnaire by participant ten was incomplete therefore cannot be used to compute the
final data. Pilot tests would best suit the problem where the researcher gets to ask participants
about the clarity of the items and what needs to be added or removed. Another solution would be
to involve experts to evaluate the content validity to ensure all aspects are covered. When the
questionnaires are not completed as desired, the researcher should then work on the completed
ones for valid results.
D1.6 Data Coding III
It is important to check raw data before entering it into the computer for coding to ensure
that all the sheets are marked appropriately. Checking before enables the researcher to find out
which of the questionnaires have information relevant to the study and are complete with all the
items correctly marked. More so, when checking, the researcher will get to see whether there are
double answers for items that require a single answer and consider the particularly marked sheet
not relevant to the study. Additionally, checking raw data is essential to ensure that the researcher
can clean up the collected data and ensure it is consistent, readable, and clear before coding
begins (Scherbaum & Shockley, 2015). On the other hand, errors that occur in coding or during
data entry can also get solved. For instance, an entry outside the range of codes allocated to a
particular variable will show on the table. This problem can get solved by the questionnaires
were numbers and will require checking the respondent's number and finding out where the
wrong codes have been entered.
References
Morgan, G., Leech, N., Gloeckner, G., Barrett, K. (2013). IBM SPSS for Introductory Statistics
(5th Ed.). New York, NY
Scherbaum, C., & Shockley, K. (2015). Analyzing quantitative data for business and
management students. Sage.
