Further development of an already established problem statement and research questions

Attached is a document that i’ve completed. I do no understand exactly what the professor is asking of me. Thus, was hoping someone could take where i am and turn into a problem statement and rephrase the questions to make them correct.

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Problem Statement
Divorce rate amongst men and women, who were reared in an absent father home.
“In this current form, it is not yet a problem statement. It does describe a potential topic for research
problem to surface. But a research problem comes from a gap. And a gap is a lack of knowledge. A lack of
explanation. It migjt be helpful if you posed your topic area within the context of IV-DV relationship or
predictor – criterion relationship. A gap is a lack of explanation as to why the IV affects the DV. Or a lack
of explanation as to what the IVs are that affect the DV. (This is typical in a quantitative research. We will
discuss in another venue, the nature of a gap in qualitative research). So a gap is a lack of the middle
variable—usually. A watered-down example would be that in the olden times people thought that Time
(IV) caused the spoon to rust (DV). But what was in Time that led to the RUST? Now that’s the gap. A
lack of explanation. It was when scientists posed Oxidation as the variable that immediately explained the
rusting better that the gap was filled. The new variable, the middle variable was oxidation. That was the
contribution to the literature.”
Scholarly Resources
Cherlin, A. J. (2005). American Marriage in the Early Twenty-First Century.
Future Of Children, 15(2), 33-55.
Gladdings, S. T., & Huber, C. H. (1984). The Position of the Single-Parent
Father. Journal Of Employment Counseling, 21(1), 13. doi:10.1002/j.21611920.1984.tb00783.x
Klaus, D., Nauck, B., & Steinbach, A. (2012). Relationships to stepfathers and
biological fathers in adulthood: Complementary, substitutional, or
neglected?. Advances In Life Course Research, 17(Special issue on
Intergenerational Relations), 156-167. doi:10.1016/j.alcr.2012.01.006
Lauroba, E. e. (2014). The Effects of Divorce on Children. International Journal
Of Legal Information, 42(1), 55-66.
Mack, K. (2001). Childhood family disruptions and adult well-being: the
differential effects of divorce and parental death. Death Studies, 25(5),
MAJA, P. (2017). The importance of the absence of the father in the narrations of
Adult Children of Divorce. Wychowanie W Rodzinie , Vol III, Iss 3/2011,
Pp 115-134 (2017), (3/2011), 115. doi:10.23734/wwr20113.115.134
The Quantitative Method
Utilizing the Quantitative Method could provide numerical data that would
be used to statistically analyze data in order to highlight the link if any between
divorce, gender, age, and absent fathers. The Quantitative Method identifies the
relationship between the four aforementioned variables. This would identify
cause and effect; thus, could make a successful prediction.
In this particular instance it would be best to use the experimental design
since there is a need to establish a cause and effect relationship among men and
women ages 18-40 (this will be broken down into two groups 18-28 and then 2938) that were reared without their biological father. Divorce would be the
independent variable as there is hope that it would be manipulated based on a
father’s presence.
The purpose of this Quantitative Study is to identify the relationship
between divorce and marital success in participants reared in an absent father
**** FIX THESE TO BE CORRECT?****Research Questions
1. What is the relationship between gender and divorce amongst participants
who were reared in an absent father home?
2. What is the relationship between gender, age, and geographical location
and divorce?
3. How common is divorce amongst participants who were raised with both
biological parents?
Divorce is more common amongst men and women, ages 29-38 that were
reared without their biological father in the home.
Population and Sample
To avoid bias it is best to use random sampling so that each participant is
given the opportunity to participate. The population to be analyzed are men and
women between the ages of 18-40 who live in the following cities in Alabama;
Huntsville, Decatur, and Madison. Who were previously divorced or are divorced
currently. Due to the population size, it would be in the best interest of the study
to select individuals to participate who would be representatives sampling thus
random selection procedures.
This data is best analyzed by utilizing the nominal scale. This is because
there is intent to place people and attributes into categories based on how they
were reared, gender, and a specific age bracket. The next step is to identify the
number of times in which divorce or martial success occurred. The desired end
state requires the use of inferential statistics as well as the need to predict/ infer
success or failure in the identified population. It is even more practical to use
correlation over regression.
Type I and Type II Errors (also known as alpha and beta errors)
Type I is best defined as a false positive, rejecting a null hypothesis is
correct (Derks, Zwinderman, & Gamazon, 2017). Type II is best defined as a
failure to reject a false null hypothesis, a false negative (Derks, Zwinderman, &
Gamazon, 2017). In the posed problem I believe a Type II would be more
common in that the answer would be incredibly different depending on when it
was conducted. For instance, if this study was done some ten years ago when
singly parent homes were less prevalent it could alter the results and need for the
study drastically. The best way to avoid type II errors is to depend heavily on
estimation utilizing confidence intervals (Derks, Zwinderman, & Gamazon, 2017).
The changes of a study detecting an effect where there is an effect to be
detected is known as statistical power. Statistical power can be influenced in this
proposed study by the sample size, experimental design, and variance. With
random sampling and the age ranging from 18-40 it yield the higher statistical
Derks, E. M., Zwinderman, A. H., & Gamazon, E. R. (2017). The relation
between inflation in type-I and type-II error rate and population divergence
in genome-wide association analysis of multi-ethnic populations. Behavior
Genetics, 47(3), 360-368. doi:10.1007/s10519-017-9837-3
Judd, C. M., Westfall, J., & Kenny, D. A. (2017). Experiments with more than one
random factor: Designs, analytic models, and statistical power. Annual
Review Of Psychology, 68601-625. doi:10.1146/annurev-psych-122414033702

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