Political Behavior and Quantitative Methods in Southern Europe: a systematic review.

doi:10.21203/rs.3.rs-4615085/v1

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Systematic Review

Political Behavior and Quantitative Methods in Southern Europe: a systematic review.

https://doi.org/10.21203/rs.3.rs-4615085/v1

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Introduction: Through data analysis of the literature we aim to answer the central question that fills a gap in the literature: Do the methods used to interpret political behaviour in Southern Europe lead to homogeneous results for the political attitudes?

Methods: For this systematic review, we searched PubMed, Scopus, Heal-link, Register PROSPERO, and forward and backward citations for studies published between databases inceptions and May 30, 2024.

We included studies reporting on political science and quantitative methods, without date or country restriction. We planned to extract results on studies' reporting on the impact of quantitative methods on political behavior. We used the open-source software KNIME. We ranked the studies according to their characteristics.

Main findings: We classified the variables for voting behavior, quantitative analysis and we performed cluster analysis and sorted the variables for the different methods of quantitative analysis. We performed a cluster analysis and classified the studies into 3 categories. The studies using Quantitative Analysis in the interpretation of political behavior positively correlate the use of quantitative methods by using correlation with p-value (p = 0.985, < 0.015) and negative Methodology variables of quantitative analysis with the extent of using quantitative methods (p=-0.962,<0.038) and the reporting of political behaviour in the findings using quantitative method in which a p-value correlation is used (p=-0.908, < 0.092). The studies show that the application of quantitative research is usually done using correlation analysis, but the application this recedes in studies of general quantitative behaviour.

Discussion: Clustering of the studies showed that studies were classified into three categories. The heterogeneity in the studies is due to the variation in their targeting. Future growth of studies in the region will lead to a reassessment of the situation.

Conclusions: A heterogeneity in methodology was found among the included studies. Although the studies used the quantitative methods in a similar way, variations in the independent variables lead the studies to different conclusions. The survey question was answered in negatively: studies using quantitative analysis to explain political behaviour are not homogeneous. The social impact and the contribution of research to political decision-making is important.

Other Political Science

political science

political behavior

voting behavior

statistics

ANOVA

descriptive analysis

correlation analysis

Southern Europe

Background of the article

Quantitative methods in political science are applied by political scientists (John, 2018) solve political science puzzles and analyze often unique data types (King, Keohane, & Verba, 2021) or even exhibit uniform characteristics with qualitative methods (Box-Steffensmeier, Brady, & Collier, 2008). The applications of the quantitative methods mentioned with regards to the change in political behaviours are Correlation Analyses, Analyses of Variance, Descriptive Analysis, ANOVA or (Logistic) Regression Analysis, Binary Logistic Regression Models (Stockemer, 2019) and others such as the Game Theory Sjoberg, & Horowitz, (2013). Oppenhuis, (1995) analyzes voting behavior in Europe through the application of quantitative methods sush as regression analysis, analysis of variance or discriminant analysis.

Summary of the existing literature search

Since the mid-1920s and especially during the 1930s, a rapid development of opinion polls showed up in the USA which aimed to measure perceptions and attitudes of public opinion. This tred was stongly affected by the establishment of the American Institute of Public Opinion by George Gallup (Gallup, & Robinson, 1938). The development of public opinion polls gave rise to great optimism, since it seemed to provide the possibility to investigate social issues using measurement tools. Today, in political science, and particularly in the study of political behaviour, the use of quantitative methods are increasingly being used. Sjoberg, & Horowitz, (2013) choose from the use of quantitative methods to use game theory to demonstrate that the sense of a state's self (or "ontological security") can create incentives to use harmful sanctions, even when those sanctions may be contrary to their own material interest (and the interests of others). Stockemer (2019) develops Quantitative Methods for the Social Sciences in a practical introduction, with the methods used for data analysis. An example of identifying political behaviour with quantitative methods in the policy domain science, is that statistics and quantitative methods have allowed us to identify citizens who have a higher socioeconomic level (SES) are more likely to vote than individuals with a lower socioeconomic level (Stockemer, Stockemer, &Glaeser, 2019). A variety of methods and techniques appear in the study of electoral behaviors in political science: Klima, Schlesinger, Thurner, & Küchenhoff, (2019) use the Bayesian approach with a multinomial-Dirichlet model and an R-package eiwild, Bautista, Callegaro, Vera, & Abundis, (2005) adopt a non-deterministic approach with planned missingness with multiple imputation (PM-MI) exit polls and Multiple Imputation (MI) techniques, while Lieske, (1989) apply OLS Regression and GLS Regression.

The political systems of Southern Europe have recently formed common features, given the proximity to the Mediterranean Sea (Font, Della Porta, & Sintomer, (2014). Southern Europe According to the United Nations (UN) includes a number of states bordering (not necessarily the Mediterranean Sea) the Iberian and Italian peninsulas and the landlocked Balkan countries and some others.

Research motivations

The number of included papers for this systematic review is relatively limited. The relative breadth of the exclusion criteria we used, is a possible reason for this. This was dictated by the fact that we are conducting research on the geographic region of Southern Europe. We wanted to limit the survey to a region with relatively similar political characteristics. The papers we include are not many and are limited and by the fact that we seek to answer a specific research question.

Aims of the article

The aims of the article are to review literature using the methods of Systematic review of the studies that exist in the literature and refer to the political behaviours using quantitative methods of data analysis, in order to extract useful conclusions for research and policy making. We wanted to classify the studies into categories according to their characteristics, to find the change in measurement of quantities in relation to the type of quantitative methods.

Issue under discussion (clear statement of the problem)

We identified a gap in the literature regarding the study of political behaviour with using quantitative methods and drawing generalised conclusions from existing studies, as well as the lack of a systematic review. The existing studies appear to be apreliminary research, which are relatively heterogeneous and we want to address this with quantitative measures. We were interested in extracting results and drawing conclusions in order to to answer the central question of our research.

Research question

Main research question: Do studies that use quantitative methods to analyse political behaviour show homogeneity in their conclusions?

Main research hypothesis: Studies that use quantitative methods to analyse political behaviour do not show homogeneity in their conclusions.

Alternative research hypothesis H1: Studies that use quantitative methods to analyse political behaviour show homogeneity in their conclusions.

Secondary research question: Do studies of political science and quantitative methods establish the impact of quantitative methods on political behavior?

Inclusion criteria:

Studies reporting on data processing for voting behavior and quantitative. The criteria used in the analysis of voting behaviour and quantitative analysis in the countries of Southern Europe.

Exclusion criteria :

1) studies that focus on the educational process for students.

2) test methodologies that have been recently used and that do not have the characteristics of the widely used methods of quantitative analysis

3) studies that could not be found even after communication and it became impossible to study them.

4) studies of electoral behavior involving elected members in government or European institutions with quantitative analysis.

5) Studies in micro, island, landlocked and intercontinental countries Southern Europe.

Since we did not find any studies in French, we only included the studies we found in English. To include the studies in our research, we conducted a literature review on the following databases:

- PubMed (1996 to 30th May 2024)

- Scopus (1900 to 30th May 2024)

- Heal-link (1999 to 30th May 2024)

- Google Scholar (1999 to 30th May 2024)

The investigation also continued at the source for grey literature open Grey (formerly open SINGLE) and the OSF database. We used the following keywords to search the databases in order to find relevant studies:

- political science

- political behavior

- voting behavior

-statistics

- ANOVA

- descriptive analysis

- correlation analysis

- Southern Europe

- Portugal

- Spain

- Italy

- Greece

-Albania

-Vosnia-Erzegovina

- Montenegro

The PRISMA 2020 checklist was used to check the existence of appropriate items that we needed to report in this systematic review.

Responsibility for the survey and its supervision was assumed by the principal and supervisor researcher. They were the principal investigators to carry out the preliminary actions to consider the possibility of conducting the research and formulated the methodology.

The principal, second and third researchers conducted the literature review and inclusion of the reviewed studies based on the inclusion and exclusion criteria.

We constructed a specific tool according to the guidelines of the JBI Evidence Synthesis manual, which was used by the principal and third investigators to include the studies in the survey our study. The second and supervising investigators worked on the judgment in case of disagreement. When we determined that information was missing, we contacted the authors of the respective studies in order to obtain the missing information. KNIME workflow was designed by the first researcher. The principal and the second researcher carried out the data processing and extraction of results. The third and the supervisor researcher worked on the judgment in case of disagreement. The principal and the third researcher wrote the article, while the second and supervising researchers worked on the judgement in case of disagreements. The principal and supervising investigator worked on the publication in international scientific journal. We used the JBI evidence synthesis method and the corresponding manual to implement the systematic review (Aromataris & Munn, 2020). The outcomes we extracted from the research study are summarized as data that will we use for further processing as follows:

Categories and Sub-categories of the data

The information extracted from the surveys as outcomes was classified into the following categories and sub-categories to form the data for the study:

Α) Qualitative data

1. Authors

2. Years

3. Voting behavior

4. Quantitative analysis

5. range in time

6. study population

7. Voting behaviour - behaviour in relation to populism

8. Voting behavior - Behavior in relation to ideology

9. Voting behaviour - behaviour in relation to discrimination

10. Voting behaviour - Behaviour in relation to participation in networks

11. Psychological interpretation

B) Quantitative analysis (also according to Stockemer, 2019):

12. voting behavior

13. Spearman

14. Pearson

15. X2

16. cluster

17. cora

18. PCA

19. FACTOR

20. correspondence analysis

21ST MDS

22. analyses of variance (ANOVA)

23. descriptive analysis

24. correlation analyses

C) Identification of country group

25. South Europe

D) References to the conclusions of the surveys

26. Conclusions - Political attitudes

27. Conclusions - Electoral attitudes

28. Conclusions - Political participation

29. Conclusions - Importance of Quantitative methods

30. Conclusions - reference to ideology

31 Conclusions - Psychological interpretation

32. Conclusions - learning

33. Conclusions - corruption

34. Political or Voting behavior- extent of reporting

35. Quantitative methods - extent of use

36. p-value extent of use

In order to enable the data to be processed, we transformed the data using appropriate indicators and a scale score indicating the weight of each data point. We checked the included studies for risk of bias. The control for risk of bias was performed with the robvis risk of bias tool. This tool is used effectively to assess risk of bias (McGuinness, & Higgins, 2021). In Southern Europe According to the United Nations (UN), a a plurality of states bordering (not necessarily from the Mediterranean Sea) the Iberian and Italian peninsula and landlocked Balkan countries and some others. We in order to examine the situation in countries with as many common features as possible, we have chosen the the countries of the European continent excluding the very small countries, the landlocked countries, the island and intercontinental countries. Therefore, Portugal is included in our study, Spain, Italy, Greece, Albania, Albania, Bosnia and Herzegovina, Montenegro.

We reviewed the studies for the methodology that is appropriate for our study and we settled on the methodology of Lynch, Smith, Harper, Hillemeier, Ross, Kaplan, & Wolfson, (2004), so that we could use it to classify the findings of our study into results. With the help of this methodology we will extract the results and design the findings of the systematic review.

Quantitative Analysis

We transformed the data to use it in the data analysis. The data were transformed into numerical form and some were scored in an appropriate scale. We performed Correlation Analysis using KNIME software and specifically Pearson's correlation coefficient and p-value significance.

We plotted the data flow shown in Figure 1.

We identified a significant heterogeneity, from the results of the analysis with the software KNIME software. The causes of the heterogeneity were examined using the improved method we used for this purpose. We performed a sensitivity study to identify the effect of changing values of the variables in the outcome of our study and sensitivity analysis for nulls results.

We examined the risk of bias, in terms of the bias of the included studies our studies. Trial protocols where available were studied and the rest were searched. The risk of bias was studied with the Robvis risk of bias software.

For each outcome, we assessed the certainty of evidence, examined the evidence in terms of its homogeneity and the homogeneity of the methods we used in each result. We also examined the software for each of the included studies.

Systematic review

12 published studies were reviewed including political behaviors and quantitative analysis. Figure 2 shows the Prisma flow diagram for our study.

We conducted a systematic study in the scientific field we are researching and found interesting interactions between the topics developed in these studies and delineate the content of our research. These interactions are presented in Fig. 3.

The use of quantitative methods in studies of electoral behavior

One third of studies such as Salvati, Giacomantonio, Pellegrini, De Cristofaro, et al. (2022) extensively use Correlation Analysis to analyze relationships between two or more continuous (quantitative) variables and about one third of studies (such as Stockemer, & Calca, 2013) use Descriptive Analysis, with graphs, descriptive indicators and measures to study the data. Some authors use complex and less frequently used quantitative methods in studies of electoral behaviour. Weyden, & Meuleman, (2008) in one such study in Spain & Portygal over a number of years finds strategic electoral behavior as factor in the composition of voting lists and the choices of political elites. The Evangelia, & Theodore, (2017) use conjoint analysis in conjunction with other methods of quantitative analysis in order to examine the formation of political behaviours that lead to electoral behaviours, in terms of the participation of participants in networks.

Political and voting behavior

Most authors opt for the psychological interpretation of political and voting behaviour. Weyden, & Meuleman, (2008) in one such study in Spain & Portygal over a number of years establishes strategic voting behavior as a factor in the formation of electoral lists and the choices of political elites. Arcuri, Castelli, Galdi, Zogmaister, & Amadori, (2008) examine behavior in relation to ideology and point to the influence of implicit ideological tendencies on voting.

The effect of populism on political and electoral behavior is examined by Weyden, & Meuleman, (2008), Salvati, Giacomantonio, Pellegrini, De Cristofaro, et al. (2022) and Marcos-Marne, Plaza-Colodro, & Freyburg, (2020).

Findings on variables

The independent variables from voting (or voting intention) are politital participation and trust in institutions.

As for the dependent variables, Weyden, & Meuleman, (2008) highlight the disproportionality, the number of participants parties, and the effective number of regional parties. Others, such as Baum, & Santo, (2003), use different variables such as gender, financial resources or vote.

Table 1 shows the empirical studies of quantitative analyses and political behaviors. Table 2 depicts the Methodology variables and correlations with quantitative analysis.

Table 1

Characteristics of the included studies
Authors	Years	Study population	Q.Μ.	voting behaviour Behaviour in relation to I.D.*	Methods Quantitative analysis Indicated methods-models-indicators	Variables Quantitative analysis D.V.: *	Conclusions
Weyden, & Meuleman, (2008).	Spain & Portygal	504	Yes 10 years	Behaviour in relation to populism (Psychological interpretation) I.V.:Vote	Disproportionality measures Loosemore-Hanby index (D-index) Least Square index.	Discordance Number of participants parties Effective number of regional parties	The parties' electoral elites rely on the analysis of electoral behaviour to run ballots
Salvati, Giacomantonio, Pellegrini, De Cristofaro, et al. (2022).	Italy		Yes 2 years	Behaviour in relation to populism Behaviour in relation to ideology I.V.:Voting intention	Correlation analyses Analyses of variance Descriptive analysis Μoderated mediation model & ANOVA	Voter characteristics Political populist tendencies	Association of populist party voters with conspiracy beliefs
Marcos-Marne, Plaza-Colodro, & Freyburg, (2020)	Spain		Yes 2015	Behaviour in relation to populism I.V.:Voting intention	(Logistic) Regression analysis Binary logistics regression models	D.V.: Elitist tendencies Pluralistic attitudes,	Populist attitudes lead to voting for new parties The crisis has favoured populist parties They can continue after the crisis
Chirumbolo, & Leone, (2008)	Italy	243	Yes 2005	Behaviour in relation to ideology I.V.:Voting	Correlation analysis Regression analysis ANOVA	D.V.: closure	Closure is linked to votes for conservative parties.
Stockemer, & Calca, (2013).	Portygal		Yes 2005 2009	I.V.:Political (Electoral) participation	Descriptive analysis	D.V.: Corruption (judicial cases)	Areas with more corruption lead to higher participation.
Baum, & Santo, (2003).	Portygal			Behaviour in relation to discrimination I.V.:Political Participation ( trade union membership, contact with political, electoral, political, political, electoral, etc.) Participation	Regression Analysis ANOVA	D.V.: Gender Financial resources Vote	Women's political participation in Portugal is lagging behind.
Mariano Torcal (2014)	Spain		Yes 2011 2012	Behaviour in relation to ideology I.V.:Trust in institutions	Correlation (Longitudinal) regression model	D.V.: Political response Economic evaluations	Electoral behaviour in the crisis is not uniform Electoral behaviour depends on previous ideological beliefs
Arcuri, Castelli, Galdi, Zogmaister, & Amadori, (2008) *	Italy	400 undecided	Yes 2001	Behaviour in relation to ideology (Psychological interpretation) I.V.:Voting	(Logistic) Regression analysis Diat index &	D.V.: Characteristics (photos) of candidates	Electoral behaviour depends on previous ideological beliefs Voters choose the information that leads to political behaviour Relationship between implicit and conscious attitudes
Evangelia, & Theodore, (2017)	Greece	1103	Yes 2016	Behaviour in relation to participation in networks And political participation (Psychological Interpretation) I.V.:Political Participation (as participation in networks)	Conjoint Analysis Regression analysis ANOVA Pearson Kendall	D.V.: sex, Relationship Action spheres Involvement in politics.	Participants in networks are likely to form similar policy preferences. They then vote in the same way. Voter profiles useful in analysing political behaviour
Stankov, (2016)	Montenegro	240	Yes 2015	Behaviour in relation to ideology (Psychological interpretation) I.V.:Voting	ANOVA Descriptive analysis Confirmatory Factor Analysis	D.V.: Nationality Characteristics (photos) of candidates Political articles	Voting behaviour depends on nationality. Ethnicity, language and racial orientation subconsciously influence voters.
Ećim, Stupar, Čekrlija,Čekrlija, Vujaković (2018)	Vosnia and Erzegovina	260	Yes 20162018	I.V.:Voting intention	Descriptive analysis Correlation analysis t-Test	D.V.: Candidate characteristics (credibility, extroversion, conscientiousness)	Desirable voting preference characteristics are outgoing, experience & conscientiousness. Desirable characteristics change with time and events.
Čakar, & Čular, (2023)	Albania (comparative)			I.V.:Political participation	Correlation analysis ANOVA Cluster analysis	D.V.: Characteristics of candidates (reliability, extroversion, conscientiousness)	Large political involvement in former communist countries. High membership levels are due to the nationalist parties of the post-communist era
I.V.:Independent variables
D.V.: Dependent variables

Table 2

Methodology variables and correlations with quantitative analysis
Methodology variables Voting behavior	Quantitative methods (as self-identified in the studies)	Methodology variables Quantitative analysis	Quantitative methods − 2 (such as self-identified in the studies)	Conclusions
11 = digits 12 = voting intention 13 = political participation	31 = Correlation analyses 32 = Regression Analysis 33 = Analyses of variance 34 = Descriptive analysis 35 = Binary logistics 36 = ANOVA 37 = Conjoint Analysis 38 = Confirmatory factor analysis 39 = Cluster	51 = Duality 52 = Number of parties 53 = Voter characteristics 54 = Populism 55 = Culturalism 56 = Pluralism 57 = closure 58 = Corruption 59 = Sex 60 = Financial resources 61 = Population 62 = Political response 63 = Economic evaluations 64 = Silence 65 = Relationship 66 = Nationality 67 = Candidate characteristics 68 = Ideology 69= Customer relation	Models Disproportionality measures regression models Μoderated mediation model Indexes Loosemore-Hanby index (D-index) Least Square index. Diat Index t-Test	11 = Voting effect behavior on ballots

We briefly mention that excluded test methodologies that have been used recently and which do not have the characteristics of the widely used quantitative methods analysis such as the IAT method (Arcuri, Castelli, Galdi, Zogmaister, & Amadori, (2008). We used a commonly used tool to avoid risk of bias, the ROBVIS Risk of Bias. With this tool we scored the risk of bias for the tested studies examined. The studies did not show a high risk of bias. We performed an analysis sensitivity analysis using the KNIME software, changing values of some variables to assess the effect of these variables on the overall outcome. Of the these tests did not result in sensitivity.

Conclusions of the studies

Comparing the conclusions of the studies included in our study, we found some interesting observations. The conclusions of the studies are listed more on electoral behaviour and less on general political behaviour, such as political participation. Ideology-based interpretation is largely mentioned, while the studies use more of a psychological approach and interpretation of subject matter. They also do not make significant reference to the process of learning from research, which we believe is an important parameter, but neither do they comment on the quantitative methods they use to draw their results and conclusions.

Clustering

We used the KNIME software to process the data, which previously processed to enter it into this software. We processed the data in terms of clustering to extract the results. The Results using k-means are shown in Table 3 and the clusters are 3.

Table 3

Clustering using k-means for the included studies
Cluster number		Cluster 1	Cluster 2	Cluster 3
Number of studies		9	5	1
		Behaviour in relation to ideology	Psychological interpretations of politics behaviour	Measurement tool assessment

The distribution of the studies into 3 clusters was obtained by applying k-means using the KNIME software.

Percentage distribution of items

We constructed a chart for the percentage distribution of the items that extracted from the studies we included in our research. This chart is shown in Fig. 4.

Some of the included studies differ in terms of the percentages of the searched data reported by the authors. The voting behavior, quantitative analysis and reporting of voting behaviour in the conclusions are reported in all studies. Η extent over time and psychological interpretation in the conclusions is mentioned in a high percentage of of the studies. The large sample of individuals, boting behavior in relation to ideology the highlighting the importance of quantitative methds and learning in the findings are mentioned in a few studies. In none of the studies reviewed is voting mentioned in the conclusions. behavior in relation to ideology and discrimination nor political participation in the findings.

Correlation Analysis

We conducted a Corellation Analysis for our research. Table 3 shows some strong correlations for the variables we used in our research. For these correlations we used KNIME software to calculate the coefficients correlation coefficients and p-values.

Table 3

Correlations between the survey variables
Variable 1	Variable 2	Correlation	p-value
Methodology variables – quantitative analysis	Quantitative methods – extent utilization	-0.962	0.038
Conclusions - policies attitudes	p-value – Extent utilization	-0.908	0.092
Quantitative methods	p-value – Extent utilization	0.985	0.015

Studies using Quantitative Analysis in the interpretation of political behavior positively correlate the use of quantitative methods with the use of p- value (p = 0.985, < 0.015). In contrast, they negatively correlate the methodology variables of quantitative analysis with the extent of using quantitative methods (p=-0.962, < 0.038) and the reporting of political behaviors in the findings using quantitative method in which a p-value correlation is used (p=-0.908, < 0.092).

Pie-charts

We plotted Pie-charts for the % of methodology variables related to voting behavior and the 5 percentages of Quantitative methods used in the included studies. These Pie-charts are shown in Fig. 6.

From the Pie-chart of the percentages of the independent variables of the methodology (in particular voting behavior) in the included studies we find that only three variables are used and these share the participation rates with the first variable being voting intention. In the dependent variables of the qualitative methods there is a dispersion in which Correlation Analysis and ANOVA.

Summary and interpretation of the main findings

We observed that studies using quantitative methods to assess the political behaviour show a homogeneity and clustering around three independent variables: voting, voting intention and political participation. The choice of dependent variables around the number of votes, political participation, voting intention, political party affiliation, political party affiliation, political party affiliation, and political party affiliation. parties, populism, closure, gender, economic data, voting, political involvement, and relationships. This is explained by the fact that the studies approach the subject of electoral behavior from different points of view and interpret it in terms of populism, ideology, discrimination, network membership and corruption. This is also evidenced by result of examining the heterogeneity of studies based on k-means, which leads to distinct categories.

Certainly the included studies which refer to Southern European countries, seem to approach the issue from different angles. We would prefer to have large number of studies addressing the exact same research question, but it seems that this is difficult in a region that is both extensive and bounded. We believe that we have reduced the literature gap, however the literature search to be done in this subject in this area may enrich the literature and provide material for an updated systematic review.

Negative correlations between Methodology variables in Quantitative analysis and extent utilization in Quantitative methods and between political attitudes in the conclusions with the extent of utilization in quantitative methods, demonstrate a negative correlation of quantitative methods with politics (not just electoral politics) behaviour. It appears that quantitative methods are used more specifically in electoral behaviour rather than general political behaviour. Enrichment is needed literature to more fully explain political behaviour with quantitative methods. methods. We also observed the correlation between quantitative methods and correlation analysis, showing that this type of analysis is the most popular among of these methods.

The main research question was answered in the negative. The secondary research question was answered in the affirmative. We also statistically examined the effects of missing data for extracted from the independent studies (due to losses to follow-up or exclusions from analysis), but we found no statistically significant findings.

Indication of errors that limit the validity of the study.

We started with the premise that the area covered by the study is fairly to very extensive, with countries located with similar political characteristics and this confirmed by the multitude of political science studies that refer to this area. However, the exclusion of a large number of studies and the deviation from a central question of the remaining ones made it difficult to analyze and synthesize the evidence.

Suggestions for future research activity in the field

We wish that future primary research would provide more studies in journals that participating in authoritative databases in the subject under consideration in this area. In this way, we look forward to a repeat of the systematic review in five years or more, in order to achieve greater convergence of the questions of the included studies and that these are in a much larger number.

Evaluation of the validity of the results

We tested the validity of the research tools. The research tool was verified it is suitable to measure what it is intended to measure and this condition is verified.

Practical application of the results

Policy makers can use the results of our research in the future. These results can be used for the selection of quantitative methods that can be used in political behaviour research and in the targeting towards specialised categories of studies.

Applying the results of our study to a broader context

The results of our study are useful in guiding the context in which the research is being carried out to an extension. This can be done by incorporating the results of our research on the directions of studies depending on the independent variables.

Need for further studies to strengthen the result

Further studies are needed in this geographical area to strengthen the results. The studies need to be broken down by category, enriching the the results of each category, in order to study the policy in a comprehensive way. behaviour using quantitative methods in this region.

Our research shows that the authors use Quantitative Analysis in the study of politics and especially voting behaviour. A variety of methods are used, in most often we find the most well-known methods, while the specialized methods are rare. The dependent variables are similar but there is variation in the independent variables. This is related to the fact that the studies in question diverge in terms of ideology, discrimination, populism, participation in networks lead the studies to different paths. There is a heterogeneity and clustering of the included studies. The analysis and synthesis of the evidence, however, yields important results for the use of quantitative analysis in the study of political behaviour. The quantitative methods are used in a similar way. The studies show that ideological and underlying psychological characteristics shape political participation and electoral behaviour. Significance but not certainty appears in the homogeneity of the included studies: the research question is answered negatively. The literature in the specific area under consideration, which is an area of relatively similar political characteristics, needs enrichment, from which will emerge studies that will help to expand the systematic review and the better understanding of the issue in the future.

Ethics approval and consent to participate

We used deidentified publicly available secondary data which did not require ethical approval.

Consent for publication

Not applicable.

Availability of data and materials

Data is available upon request.

Competing interests

Authors declares that they have no competing interests.

Funding Statement

“The authors declare that there are no funding regarding the publication of this paper.”

“The authors declare that there are no financial or non-financial interests that are directly or indirectly related to the work submitted for publication.”

Helsinki Declaration

Note that a statement on the "Declaration of Helsinki" is not required, because this research is a systematic review and neither patients nor volunteers are involved.

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The authors declare no competing interests.

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Political Behavior and Quantitative Methods in Southern Europe: a systematic review.

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Abstract

Figures

Introduction

Background of the article

Summary of the existing literature search

Research motivations

Aims of the article

Issue under discussion (clear statement of the problem)

Research question

Methodology

Results

Systematic review

The use of quantitative methods in studies of electoral behavior

Political and voting behavior

Findings on variables

Conclusions of the studies

Clustering

Percentage distribution of items

Correlation Analysis

Pie-charts

Discussion

Summary and interpretation of the main findings

Suggestions for future research activity in the field

Evaluation of the validity of the results

Practical application of the results

Applying the results of our study to a broader context

Need for further studies to strengthen the result

Conclusions

Declarations

References

Additional Declarations

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