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The Association Between Marital Status And Relationship Quality Gcse History Essay Help

Model Description

            The study adopts a cross-sectional analysis of a country’s comparative approach on the association between marital status and relationship quality. The individual-level variables consider four main aspects. The dependent variable is the current marital status of each individual in both countries. The current marital status indicates whether an individual is married or not. The other three variables include independent variables that guide the aim of this study. They formulate the research questions:

How does living with a partner before marriage (v235) affect the current marital status of individuals in both Austria and Armenia?
How does living with a partner right (v236) now affect individual, marital status in Austria and Armenia?
How does having a steady relationship (v237) affect individual, marital status in Austria and Armenia?

 

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Data Description and Empirical Analysis

            The researcher used the GESIS data that focuses on European Values Study across 36 countries within Europe for the analysis. This is a large-scale, cross-national, and longitudinal research survey dealing with individual thoughts of work, family, religion, society, and politics. This study adopted two main research statistical approaches to meet the main objectives. These approaches used by the researcher were Descriptive Statistics in examining the mean, standard deviation, and the Variance of the variables and the inferential statistical design in assessing the predictions of the research hypothesis of the underlying population.

Descriptive Statistics

The descriptive statistics summarizes the maximum, minimum, mean, standard deviation, and Variance of the variables. The following table summarizes the descriptive statistics of each variable based on the two countries.

 

Multinomial Logistic Regression, Austria do my history assignment

Multinomial Logistic Regression, Austria

Legal Marital Status
Living with a partner before marriage
Currently living with a partner
Having a Steady relationship

Married
 
 
 

Registered Partnership
 
 
 

Widowed
0.656
0.709
0.122

Divorced
0.030
0.174
0.595

 

Multinomial Logistic Regression, Armenia

Legal Marital Status
Living with a partner before marriage
Currently living with a partner
Having a Steady relationship

Married
0.907
 
0.249

Registered Partnership
 
0.929
0.608

Widowed
0.297
 
 

Divorced
 
 
 

Separated
0.004
0.001
0.001

 

Table 6 and Table 7 indicates the logistic regression analysis for the two countries. The odds ratio for the selected values is less than 1. This indicates that there is a weak association between the variables. The following equation shows the classification algorithm form in the event of the success of the outcome of the dependent variable.

Substituting the beta coefficient for the model fosters the predictive value of the dependent variable.

Summary and Conclusion

Individual quality of a relationship is a broad area perceived to vary according to the categories of marital status. The study outlined how individuals living with a partner and the steadiness of a relationship affected marital status. A steady relationship, defining a romantic relationship, indicates that the family norms have no direct association between the societal contexts due to the rise of these alternative arrangements. Most critics argue that married people enjoy greater longevity than unmarried hence living a higher quality of life. More than 130 empirical studies indicate that married men and women are less stressed than single people (“Subjective well-being measure,” 2014).

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Multinomial Logistic Regression, Armenia

Legal Marital Status
Living with a partner before marriage
Currently living with a partner
Having a Steady relationship

Married
0.907
 
0.249

Registered Partnership
 
0.929
0.608

Widowed
0.297
 
 

Divorced
 
 
 

Separated
0.004
0.001
0.001

 

Table 6 and Table 7 indicates the logistic regression analysis for the two countries. The odds ratio for the selected values is less than 1. This indicates that there is a weak association between the variables. The following equation shows the classification algorithm form in the event of the success of the outcome of the dependent variable.

Substituting the beta coefficient for the model fosters the predictive value of the dependent variable.

 

Marital quality, socioeconomic status, and physical health help with history assignment

How does marital status affect the quality of a relationship?

A research study in Austria and Armenia

 

Introduction

People believe that living is a free personal choice in today’s society. Marriage and individual relationship status have long been the standard of life. However, most people have opted to live a cohabitation life either from divorce or same-sex relationships, accepted in most countries. The Australian Civil Code law governs the legal relationship between parents, spouses, and children (Dush & Amato, 2017). The legal code entails the Austrian Marriage Act that provides the legal requirements of marriage entry. In 1961, the Austrian government declared civil ceremonies the only legal way of getting married. However, citizens are unrestricted to other forms of marriage such as traditional ceremonies, religious or concubine. These forms are not yet declared legal by the government (Choi & Marks, 2019). The acceptable minimum age of marriage is 16 with the consent of the parent and 18 without. All forms of relationship status, whether single or duet, is accepted in Austria.

On the other hand, Armenia’s legal minimum age of marriage is 18 years. The only acceptable type of marriage in Armenia is any marriage recognized by legal bodies. Any church marriages or cohabitations are identified as illegal by the Armenian government. Unlike Austria, where citizens are free to marry from outside countries, the Armenian government only allows state foreign marriages upon presenting the required documents to the Civil Status Acts Registration Agency of the Ministry of Justice (Choi & Marks, 2019). In 2017, the state government announced that same-sex marriage is illegal and signed an Istanbul Convection to protect health and morals.

This study aims to develop a deep understanding of the relationship between marital status and the quality of relationships in Austria and Armenia. The study is guided by the research question: How does marital status affect the quality of relationships in Austria and Armenia? The study will replicate the literature review findings of marital status in a cross-national context, exploring the role of marital status and lastly to uncover the quality of relationships in marital satisfaction.

Model Description

            The study adopts a cross-sectional analysis of a country’s comparative approach on the association between marital status and relationship quality. The individual-level variables consider four main aspects. The dependent variable is the current marital status of each individual in both countries. The current marital status indicates whether an individual is married or not. The other three variables include independent variables that guide the aim of this study. They formulate the research questions:

How does living with a partner before marriage (v235) affect the current marital status of individuals in both Austria and Armenia?
How does living with a partner right (v236) now affect individual, marital status in Austria and Armenia?
How does having a steady relationship (v237) affect individual, marital status in Austria and Armenia?

The study also adopted the following logistic regression model for analysis;

Where y is the dependent variable; Marital Status,  is the Coefficient of intercept,  are the corresponding Coefficient for the respective independent variables, while  are the independent variables: “living with a partner before marriage” (v235), “currently living with a partner” (v236), and “having a steady relationship” (v237), respectively.

Data Description and Empirical Analysis

            The researcher used the GESIS data that focuses on European Values Study across 36 countries within Europe for the analysis. This is a large-scale, cross-national, and longitudinal research survey dealing with individual thoughts of work, family, religion, society, and politics. This study adopted two main research statistical approaches to meet the main objectives. These approaches used by the researcher were Descriptive Statistics in examining the mean, standard deviation, and the Variance of the variables and the inferential statistical design in assessing the predictions of the research hypothesis of the underlying population.

Descriptive Statistics

The descriptive statistics summarizes the maximum, minimum, mean, standard deviation, and Variance of the variables. The following table summarizes the descriptive statistics of each variable based on the two countries.

Table 1: Descriptive Statistics

Mean
Standard Deviation
Variance
Minimum
Maximum
Total Number

Country
45.25
5.495
30.196
Austria
Armenia
3144

Legal Marital Status
2.83
2.140
4.581
Married
Never married and never registered partnership
3132

Living with a partner before marriage
1.68
0.468
0.219
Yes
No
2266

Currently living with a partner
1.85
0.359
0.129
Yes
No
1394

Having a Steady relationship
1.83
0.374
0.140
Yes
No
1190

 

The country variable sorts the other selected variables. The minimum respondents came from Austria, as indicated. The dependent variable, Legal marital status, the mean was 2.83 (3), and the standard deviation was 4.581 (5). Out of 3,132 respondents, most were neither in a relationship (never married) nor had registered in any partnership. Only a few of the respondents were married. As the mean indicates the arithmetic average, it is clear from the table that the approximate mean from the variable was 3, indicating that the average respondents were widowed. Most respondents in the independent variable declared a “no” to the respective variables. Most people had never lived with a partner before; neither were they currently living with them or having a steady relationship. The three variables’ mean was 1.68, 1.85, and 1.83, respectively. This mean was close to 2, indicating that most respondents were not associated with any form of relationship within the two countries.

Reliability and Validity

Reliability and validity (variability) are statistical concepts used in quantitative research. Factor analysis is a metric used to detail the variability of the related variables within the provided data. The aspect reduces a large number of variables to fewer factors. Kaiser-Meyer-Olkin (KMO) and Bartlett’s Test examines the study’s validity. The following were the results of the tests as obtained by the researcher.

Table 2: Factor Analysis

Austria
Armenia

Kaiser-Meyer-Olkin (KMO)
0.56
0.536

Bartlett’s Test
21.391
6.698

 

Table 2 shows the outcomes of KMO and Bartlett’s Test to test validity. From the rule of thumb, when the test value is less than 0.5, then there is an insignificant substantial relationship between the data. This variable collinearity indicates how strongly a variable is related to each other. The value for both KMO and Bartlett’s Tests was more significant than 0.5 and 0.05, respectively, indicating that the variables were valid for this test.

On the other hand, a reliability test is a metric used to test to what extent the measure occurs without error. It is another approach to validity. The researcher adopted the Cronbach Alpha test in measuring the internal consistency between the variables. Table 3 indicates the outcomes of the reliability test for both countries.

Table 3: Reliability Statistics

Austria
Armenia

Cronbach Alpha (Standardized)
0.77
0.712

 

From the rule of thumb, Cronbach alpha below 0.50 indicates inappropriate variables. The good practice is that the level of reliability should be above 0.7 for acceptability. Values higher than 0.8 might be an indication of redundancy.

Inferential Statistics

Inferential statistics is a statistical metric that compares the association of two or more different groups. The study adopted inferential statistics by using the regression analysis model in identifying the relationship between the variables (Hanninghofer et al., n.d.). The researcher used the correlation method to determine the association between the dependent and independent variables. The researcher also adopted logistic regression analysis to measure the probability of outcomes on the dependent variable based on the likelihood of the independent variables through a multinomial approach. The following table summarizes the correlation coefficients for each country’s dependent and independent variables.

Table 4: Correlation Analysis: Austria

Correlation Coefficients

Legal Marital Status
1

Living with a partner before marriage
0.031

Currently living with a partner
-0.181

Having a Steady relationship
-0.130

 

Table 5: Correlation Analysis: Armenia

Correlation Coefficients

Legal Marital Status
1

Living with a partner before marriage
0.019

Currently living with a partner
0.115

Having a Steady relationship
0.114

 

Table 4 and Table 5 indicate the correlation analysis for the two countries. In Austria, there was a positive correlation coefficient between marital status and the “people living with a partner before marriage” variable of 0.031. The value indicated was close to zero and far from one. This shows that the association between marital status and living with a partner before marriage had a weak positive correlation. The relationship between these variables is not strong. “Current living with a partner” and “having a steady relationship” variables in Table 4 indicated a weak relationship or association with the dependent variable. The correlation coefficient values were negative for the two independent variables, -0.181 and -0.13, respectively. This indicates a weak negative correlation since the values were far from -1 and close to 0. When the independent variable increased, the dependent variable would tend to decrease in an unreliable manner. The researcher concluded that significantly, there was no relationship between marital status and the three other variables; “living with a partner before marriage,” “currently living with a partner,” and “having a steady relationship.”

Table 5 shows the correlation coefficient for the population in Armenia. It is evident that the correlation coefficients for the three variables were positive, that is, 0.019, 0.115, and 0.115 for “living with a partner before marriage,” “currently living with a partner,” and “having a steady relationship,” respectively. However, the coefficients were close to 0 and far from 1, indicating a weak positive correlation. When the value of the independent variable increases, the dependent variable tends to increase in an unreliable manner (Hanninghofer et al., n.d.). There is no significant relationship between marital status and the three variables.

Logistic Regression

            Multinomial logistic regression is a classification of generalized logistic regression in multiclass problems. The model adopts the odds ratio as the measure of association between the variables. From the rule of thumb, an odds ratio greater than one indicates that there is a higher risk of no association between the variables in the group. The table below indicates the outcomes of the odds ratio for variables in both countries.

Table 6: Multinomial Logistic Regression, Austria

Legal Marital Status
Living with a partner before marriage
Currently living with a partner
Having a Steady relationship

Married

Registered Partnership

Widowed
0.656
0.709
0.122

Divorced
0.030
0.174
0.595

 

Table 7: Multinomial Logistic Regression, Armenia

Legal Marital Status
Living with a partner before marriage
Currently living with a partner
Having a Steady relationship

Married
0.907

0.249

Registered Partnership

0.929
0.608

Widowed
0.297

Divorced

Separated
0.004
0.001
0.001

 

Table 6 and Table 7 indicates the logistic regression analysis for the two countries. The odds ratio for the selected values is less than 1. This indicates that there is a weak association between the variables. The following equation shows the classification algorithm form in the event of the success of the outcome of the dependent variable.

Substituting the beta coefficient for the model fosters the predictive value of the dependent variable.

Summary and Conclusion

Individual quality of a relationship is a broad area perceived to vary according to the categories of marital status. The study outlined how individuals living with a partner and the steadiness of a relationship affected marital status. A steady relationship, defining a romantic relationship, indicates that the family norms have no direct association between the societal contexts due to the rise of these alternative arrangements. Most critics argue that married people enjoy greater longevity than unmarried hence living a higher quality of life. More than 130 empirical studies indicate that married men and women are less stressed than single people (“Subjective well-being measure,” 2014). The study concluded that the individual status of marriage has no unique association with the quality of the relationship. The literature on the quality of relationships tends to focus more on the marital status prior to breaking up. Hence it becomes difficult to pre-determine the issues that might arise in relationships. Other limitations include a reporting bias when using the GESIS cross-sectional data set. Individuals might be exposed to a pre-disposed to pessimistic assessment. The data set entails most subcategories in each variable, making it difficult to surpass the outliers.

Despise these limitations; this study is fit for future research. Researchers can use the report as a secondary source to determine any relationship between marital status and the quality of relationships in the two countries, Austria and Armenia. The literature viewpoint of this study also provides a full understanding of the familiar context in marriage relationships.

 

 

 

References

Choi, H., & Marks, N. F. (2019). Marital quality, socioeconomic status, and physical health. Journal of Marriage and Family, 75(4), 903-919. https://doi.org/10.1111/jomf.12044

Custer, L. (2020). Marital satisfaction and quality. Encyclopedia of Human Relationships. https://doi.org/10.4135/9781412958479.n331

Dush, C. M., & Amato, P. R. (2017). Consequences of relationship status and quality for subjective well-being. Journal of Social and Personal Relationships, 22(5), 607-627. https://doi.org/10.1177/0265407505056438

Hanninghofer, J., Foran, H., Hahlweg, K., & Zimmermann, T. (n.d.). Impact of relationship status and quality (Family type) on the mental health of mothers and their children: A 10-Year longitudinal study. Frontiers. https://www.frontiersin.org/articles/10.3389/fpsyt.2017.00266/full

Is marital status associated with quality of life? (2014, August 8). BioMed Central. https://hqlo.biomedcentral.com/articles/10.1186/s12955-014-0109-0

Subjective well-being measure. (2014). Encyclopedia of Quality of Life and Well-Being Research, 6450-6450. https://doi.org/10.1007/978-94-007-0753-5_104074

 

 

 

 

Appendix

Descriptive Statistics

[Country Code]

 

 

 

Descriptive Statistics

N
Minimum
Maximum
Sum
Mean
Std. Deviation
Variance

Statistic
Statistic
Statistic
Statistic
Statistic
Statistic
Statistic

country code (ISO 3166-1 Alpha-2 code)
3144
40
51
142260
45.25
5.495
30.196

Valid N (listwise)
3144

 

[Armenia]

 

 

Descriptive Statistics

N
Minimum
Maximum
Mean
Std. Deviation
Variance

Statistic
Statistic
Statistic
Statistic
Statistic
Statistic

current legal marital status respondent (Q72)
1497
1
6
2.68
2.149
4.619

lived with a partner before marriage (Q73)
1072
1
2
1.91
.283
.080

living with a partner (Q74)
568
1
2
1.93
.247
.061

having a steady relationship (Q75)
546
1
2
1.88
.320
.102

Valid N (listwise)
155

 

 

 

 

 

 

 

 

 

[Austria]

 

 

 

Descriptive Statistics

N
Minimum
Maximum
Mean
Std. Deviation
Variance

current legal marital status respondent (Q72)
1635
1
6
2.96
2.124
4.512

lived with a partner before marriage (Q73)
1194
1
2
1.46
.499
.249

living with a partner (Q74)
826
1
2
1.79
.409
.167

having a steady relationship (Q75)
644
1
2
1.79
.410
.168

Valid N (listwise)
329

 

Factor Analysis

[Austria]

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.560

Bartlett’s Test of Sphericity
Approx. Chi-Square
21.391

df
3

Sig.
.000

 

 

Communalities

Initial
Extraction

current legal marital status respondent (Q72)
1.000
.509

lived with a partner before marriage (Q73)
1.000
.402

having a steady relationship (Q75)
1.000
.390

Extraction Method: Principal Component Analysis.

 

 

 

 

Total Variance Explained

Component
Initial Eigenvalues
Extraction Sums of Squared Loadings

Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %

1
1.301
43.360
43.360
1.301
43.360
43.360

2
.893
29.757
73.117

3
.806
26.883
100.000

Extraction Method: Principal Component Analysis.

 

[Armenia]

 

KMO and Bartlett’s Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
.536

Bartlett’s Test of Sphericity
Approx. Chi-Square
6.698

df
3

Sig.
.082

 

 

Communalities

Initial
Extraction

current legal marital status respondent (Q72)
1.000
.495

lived with a partner before marriage (Q73)
1.000
.255

having a steady relationship (Q75)
1.000
.475

Extraction Method: Principal Component Analysis.

 

 

Total Variance Explained

Component
Initial Eigenvalues
Extraction Sums of Squared Loadings

Total
% of Variance
Cumulative %
Total
% of Variance
Cumulative %

1
1.225
40.827
40.827
1.225
40.827
40.827

2
.941
31.377
72.204

3
.834
27.796
100.000

Extraction Method: Principal Component Analysis.

 

Reliability

[Austria]

 

Reliability Statistics

Cronbach’s Alpha
Cronbach’s Alpha Based on Standardized Items
N of Items

.781
.77
3

[Armenia]

 

Reliability Statistics

Cronbach’s Alpha
Cronbach’s Alpha Based on Standardized Items
N of Items

.709
-.712
3

 

 

 

Correlation

[Armenia]

 

Correlations

current legal marital status respondent (Q72)
lived with a partner before marriage (Q73)
living with a partner (Q74)
having a steady relationship (Q75)

current legal marital status respondent (Q72)
Pearson Correlation
1
.019
.115**
.114**

Sig. (2-tailed)

.531
.006
.008

N
1497
1072
568
543

lived with a partner before marriage (Q73)
Pearson Correlation
.019
1
.106
-.076

Sig. (2-tailed)
.531

.154
.320

N
1072
1072
181
173

living with a partner (Q74)
Pearson Correlation
.115**
.106
1
.b

Sig. (2-tailed)
.006
.154

.000

N
568
181
568
511

having a steady relationship (Q75)
Pearson Correlation
.114**
-.076
.b
1

Sig. (2-tailed)
.008
.320
.000

N
543
173
511
546

**. Correlation is significant at the 0.01 level (2-tailed).

b. Cannot be computed because at least one of the variables is constant.

[Austria]

 

Correlations

current legal marital status respondent (Q72)
lived with a partner before marriage (Q73)
living with a partner (Q74)
having a steady relationship (Q75)

current legal marital status respondent (Q72)
Pearson Correlation
1
.019
.115**
.114**

Sig. (2-tailed)

.531
.006
.008

N
1497
1072
568
543

lived with a partner before marriage (Q73)
Pearson Correlation
.019
1
.106
-.076

Sig. (2-tailed)
.531

.154
.320

N
1072
1072
181
173

living with a partner (Q74)
Pearson Correlation
.115**
.106
1
.b

Sig. (2-tailed)
.006
.154

.000

N
568
181
568
511

having a steady relationship (Q75)
Pearson Correlation
.114**
-.076
.b
1

Sig. (2-tailed)
.008
.320
.000

N
543
173
511
546

**. Correlation is significant at the 0.01 level (2-tailed).

b. Cannot be computed because at least one of the variables is constant.

 

 

 

 

 

 

 

 

Logistic Regression

[Austria]

 

Parameter Estimates

current legal marital status respondent (Q72)a
B
Std. Error
Wald
df
Sig.
Exp(B)
95% Confidence Interval for Exp(B)

Lower Bound
Upper Bound

widowed
Intercept
.928
2.083
.199
1
.656

v235
-.322
.863
.139
1
.709
.724
.133
3.934

v236
0b
.
.
0
.
.
.
.

v237
1.383
.894
2.395
1
.122
3.987
.692
22.986

divorced
Intercept
4.362
2.015
4.685
1
.030

v235
-1.160
.853
1.850
1
.174
.313
.059
1.668

v236
0b
.
.
0
.
.
.
.

v237
.458
.862
.282
1
.595
1.581
.292
8.570

a. The reference category is: separated.

b. This parameter is set to zero because it is redundant.

 

 

[Armenia]

 

Parameter Estimates

current legal marital status respondent (Q72)a
B
Std. Error
Wald
df
Sig.
Exp(B)
95% Confidence Interval for Exp(B)

Lower Bound
Upper Bound

married
Intercept
-1.495
.323
21.385
1
.000

v239a
3.365
.219
236.575
1
.000
28.946
18.851
44.446

v239b
4.010
.995
16.231
1
.000
55.137
7.839
387.820

v240
-.098
.085
1.329
1
.249
.907
.768
1.071

registered partnership
Intercept
-4.061
.572
50.403
1
.000

v239a
2.917
.274
112.987
1
.000
18.483
10.794
31.649

v239b
4.464
1.002
19.847
1
.000
86.859
12.185
619.145

v240
-.073
.143
.263
1
.608
.929
.703
1.229

widowed
Intercept
-3.260
.456
51.165
1
.000

v239a
3.620
.246
217.226
1
.000
37.322
23.063
60.395

v239b
4.866
.998
23.753
1
.000
129.851
18.345
919.110

v240
-.414
.122
11.592
1
.001
.661
.521
.839

divorced
Intercept
-.451
.432
1.090
1
.297

v239a
3.249
.306
112.598
1
.000
25.763
14.138
46.949

v239b
3.523
1.007
12.238
1
.000
33.892
4.708
243.992

v240
-.981
.162
36.483
1
.000
.375
.273
.516

separated
Intercept
-2.083
.722
8.323
1
.004

v239a
3.213
.426
56.872
1
.000
24.844
10.780
57.257

v239b
3.559
1.042
11.672
1
.001
35.115
4.559
270.482

v240
-.817
.257
10.110
1
.001
.442
.267
.731

a. The reference category is: never married and never registered partnership.