measure of Trust in Politicians

I hereby certify that the following work is entirely mine and has not been submitted for evaluation or credit at [Name of your University] University or any other institution of higher learning. As a result, this paper adheres to the University and School's guidelines on academic honesty and conduct, and every effort has been made to ensure that all work by other authors is properly recognized.

I also provide permission to utilize this research assignment in part or in whole for academic purposes, with or without my name appearing on the cover of the document or within the work.  Over the years, researchers, and social scientist have increasingly been concerned with the level of decline in political trust. In response to this decline, a number of scale designs have been developed to measure the level of political trust which is acts as a tool for determining the level of political legitimacy of the political office holder (politician). Trust is defined as a multi-dimensional construct (Wing & Angie, 2006) which has led to development of numerous conceptual models for social psychological study analysis.

This study derived the initial 4-factor model of trust from literature, which are: self-interest, benevolence, competence, and integrity. Based on the Exploratory Factor Analysis (EFA) the initial 4-factor model is reduced to a 3-factor principal components model. These results would enhance the overall perception of the above factors so as to inform future studies and provide a ground for the understanding and deepening of the trust in politicians and elected leaders.

Reliability and Validity of scale for the measure of Trust in Politicians

INTRODUCTION

According to Merriam-Webster online dictionary (2012), trust is defined as the act of having an assured confidence on the personality, strength and ability of someone or something owing to some rational belief or prior knowledge of the subject. The complex rigorous process of evaluating and considering an individual as worthy of trust has been a subject for many studies. In particular, before an individual arrives at the decision of having the assured confidence in trustworthiness of the individual or institution, there are quite a number of factors that would inform proper judgment by the subjects (Fricker et al, 2013)

Although empirical studies have proposed a list of such variables, however there still are questions on the exact number of factors. For example, Suzanne et al., (2015), posits that in measuring the level of trust of a politician, a number of these factors often exhibit limited power to adequately explain the dependent variable which for this case is the trustworthiness of the politician or institution. Due to the complexity of trust being seen as a judgment that is individual dependent and depicts high variability in measure from person to person and institution to institution, therefore in order to be able to constructively and adequately scale down a reliable and consistent tool of measure, we therefore have to ideally cast our net widely in order to be able to explain this variable, and also be able to pinpoint the underlying reasons for trust and mistrust of politicians.

Previous studies by Metlay (1999), Mayer (2005), Uslaner (2008), Woolcock (2001), and Wing & Angie, (2006), have all fronted the concept of trust as complex, multifaceted concept among other things the value bestowed, risk (in terms of investment), and expectations for a gain.

or a return. Subsequent studies have shown that a single-factor measure does not comprehensively and adequately attain the different components of this concept therefore justifying the need for an immediate intervention to conceptualize a reliable scale that can sufficiently measure the level of trust in politicians by incorporating all the above four factors.

It is in line with the above view that this study seeks to provide a greater conceptual framework easily adoptable to easily measure public trust in politicians and political institutions. A study by Lee (2004) showed that increase in level of public trust improves the level of public participation in governance; increase the rate of policy acceptance and implementation, reducing the cost of governance, while as promoting public enactment and implementation of laws and/or regulations (Ayres and Braithwaite 1992). As a result, sustaining public trust in politicians is crucial for betterment of the public government interaction and social wellbeing.

Literature Review

Trust is an important element for public and social interactions. We can ideally say that all the components of the day to day life in the society revolve around trust; and decisions are arrived at as a result of trust bestowed upon an individual or institution in the modern society where the level of interdependence reliance can exponentially increased. It would be right to assert that in the event that trust is no longer existence, then we would witness a scenario where there is a breakdown in social fabrics Uslaner (2000).

The study of trust as component of social and psychology factor dates back to the late 1950s in which first literatures were developed that have formed the base for future studies that have been anchored on the same (Passey, 1997). Studies in philosophy, psychology, organization science and even economics have articulated the role of trust in ensuring a firm and strong relationships that is vital for peaceful coexistence and cooperation on a day to day set up (Misztal, 2013).

In this research, I was tasked with the work of evaluating the design of the political trust measurement tool through examination of the constructs, design validity and reliability. This study was initiated with a view that it would further support previous studies by recreating a factor structure that can be used to predict the level of trust bestowed upon politicians by the populace. This study is also believed to compliment and build on a previous similar study done by Ben (2016), through demonstrating a strong convergent evidence in building on a previous instrument developed for the US National Election Study (NES) ( Lang and Hallman 2005).

The factors comprised of a Likert scale (Likert, 1932), in which the instrument sought to measure the respondent’s attitudes by responding to the answers with the best varying degrees of agreement.

In order to build on those previous studies, this study formed the following hypothesis: the number of factors shall not be less than 3 used, with the original components predicted to prove applicable in the set of factors used. Moreover, it’s hypothesized that, trust is expected to vary within the population demography like age, level of education, between varying time periods in relation to the next electioneering period. Finally the study hypothesized that there were no common factors against the alternate hypothesis that there were common factors.

Methodology

Sampling

In the assessment of the instrument’s validity and reliability, we selected a stratified sampling as the most convenient method of data capture. A total of 300 university students at an Australian University were randomly selected for the study. The data collection period ran from 1st to 25th May 2017. Out of 250 online survey questionnaires administered, 187 questionnaires were recalled and cleaned for analysis. The overall response rate was 74.8%. The sample size (n=187) was satisfactory since it exceeded the recommendation by Devellis (2012) in factor analysis the sample size (n) ought to exceed at least 5 fold the total number of constructs. In general, for more accurate analysis, the data has to be strong in that, there should be uniformly high communalities without cross loadings (Zhang, & Hong, 1999).

Procedure

An online survey was completed by undergraduate students at an Australian university and the data collected was cleaned and used for the study.

DATA ANALYSIS

The purpose of this research study being to scrutinize the appropriateness of the items and the internal structure of the constructs that the instrument measures for political trust. In line with these, an exploratory factor analysis (EFA) was performed to estimate the structure and scales. Secondly, a reliability analysis (RA) on the items was executed to test the reliability of the data capture questionnaire set.

Factor analysis on the other hand is the process of simplifying of connected measures where the researcher asks a series of questions to measure the phenomenon and then scientifically analyze the resulting responses in order to come up with a single measure also known as factor. After such combination, the factors become the measures of the latent phenomenon. Through analytical process of exploratory factor analysis the varied number constructs and design factors are identifiable and this results in a concrete and robust standard of measure (Marien & Hooghe, 2011)

Currently there exists a number of different methods used to perform factor analysis, these include: maximum likelihood, principal axis factor, un-weighted least squares, generalized least squares etc., with varied rotations types performed on the initial factors, which include: orthogonal (involves ‘equimax’ and ‘varimax’), a procedure that demands that there be no correlation between factors; and oblique rotations (e.g ‘promax’) where the restriction for correlation between analytical factors is not mandatory.

Again, in conducting the analysis, the researcher can affix the exact number of factor analytic techniques and the options of the number of constructs to be considered for the final output, although by making different assumptions above, different outcomes would be arrived at when analyzing the same data set. This therefore makes most researcher and analysts to adopt the simplest structure which ensures each individual variable being analyzed ‘loads highly’ on a one-to-one basis in the final outcome.

Upon extraction of factors, the researcher is tasked with the decision of choosing the amount of factors to be utilized in the subsequent part which involves rotation of these factors such that in the end there is neither over- nor under- extraction. This is because such decision would negatively influence the final outcome. In most statistical software like the one used for this analysis, the decision criteria is always to retain items whose eigenvalues are greater or equal to one. However, still there is contention on the best decision rules with some literatures and studies disregarding this method as the most accurate method to retain factors (Velicer & Jackson, 1990).

In order to gauge how reliable the sets were, an internal consistency analysis was performed and comparison made based on Chronbach's alpha. For the retained factors, a descriptive statistics analysis was utilized. The software program for the analysis was IBM SPSS v20.0 (SPSS Inc, Chicago, IL, USA).

RESULTS AND DISCUSSIONS

The distribution of respondents was as follows, of the total sampled 187, 78.0% were female, 20.4% indicated to be male, with 0.5% identifying themselves as transgender female and 1.1% being gender-queer. The average age of the sampled respondents was 29.5 (SD=9.814). The respondents were required to evaluate their political stand by using the slider to indicate their political stand. On average, the minimum political stand was 0.0 and the maximum was 90.0, with a mean political stand of 37.19 (SD=20.98). The responded were further asked whether they participated in the 2016 general election, a majority 91.4% indicated that they voted as compared to the 8.6% of those who indicated that they did not participate in the election.

The Exploratory Factor Analysis (EFA) with a population sample of 242 was during the analysis, which yielded the recommended number of latent (constructs) factors. Although the initial design of the questionnaire comprised of between six and factor model, results from the analysis showed that the most adequate is a 7-factor model, using the Kaiser criterion ( see table 3), the eigenvalue equal or greater than 1 and the RMSEA ≤0.6. The analysis further showed that questionnaire variables with factor loading greater than 0.4 were retained. A total of 23 out of the 25 items were retained.

Table 1 Component Score Coefficient Matrix

Component

Factors

1

2

3

Self Interest

Like having media attention

-0.065

-0.057

0.235

Are motivated by power

-0.059

0.037

0.203

Enjoy being treated as important people

-0.114

0.029

0.257

Want to be well-known

-0.033

-0.082

0.251

Are motivated by personal greed

0.096

-0.055

0.114

Are mostly concerned about their reputations

0.018

-0.026

0.167

Only make decisions that further their personal goals

0.092

-0.065

0.118

Benovelence

Want to make life better for ordinary people

0.227

-0.102

-0.068

Want to improve Australian society

0.228

-0.079

-0.103

Want to create a fair and just society

0.214

-0.082

-0.068

Genuinely care about the lives of others

0.206

-0.065

-0.065

Put the needs of the community ahead of their own

0.096

0.01

0.047

Competence

Consider the well-being of all Australians

-0.179

0.081

0.023

Are very intelligent people

-0.113

0.296

-0.05

Have a natural talent for their particular area

-0.117

0.252

0.013

Have a thorough understanding of global issues

-0.051

0.204

-0.045

Possess a high level of knowledge and skills

-0.092

0.266

-0.03

Effectively balance the needs of today with those of the future

-0.015

0.138

-0.008

Do not have the ability to make good decisions

-0.006

0.082

0.074

Are capable of effectively tending to the public’s needs

0.115

0.017

-0.038

Integrity

Follow the letter of the law

-0.047

0.203

-0.012

Follow through with promises made during election campaigns

0.029

0.091

0.031

Show consistency between what they say and what they do

0.021

0.086

0.053

Do not always tell the truth

-0.034

0.077

0.122

Are honest

0.068

0.066

0.029

Extraction Method: Principal Component Analysis.

Rotation Method: Varimax with Kaiser Normalization.

Component Scores.

Table 2 Total Variance Explained

Table 3 The KMO and Bartlett's test output

Figure 1 A Scree plot of the Components vs the Eigen values

Reliability and internal consistency test

Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha Based on Standardized Items

N of Items

.868

.873

25

Table 4 Reliability Test output

In order to assess the internal reliability and consistency of the items in the model the Cronbach's alpha reliability coefficient was calculated and the output is presented in the table 4. The coefficient for the model was 0.868, a value that is good based on George and Mallery (2003), the last column of the table indicated the values of the alpha if individual question items were deleted. As shown in bold text, only two question items’ deletion would result in an increase in the value of the Cronbach's alpha. By deleting question 7: “Like having media attention”, the value of alpha increase to 0.871, further by deleting the question that asked on Consider the well-being of all Australians, the highest value of alpha of 0.894 was recorded. Overally, the data showed a greater level of internal consistency of question items (variables) in the scale.

The researcher in this study encountered quite a number of limitations. First, the sampling frame for this study was limited to university students who were able to access online materials for this study; this method was biased as it eliminated other population group whose input would have enriched the outcome of this study. In future studies, a representative sampling frame; that ensures all the demographics (aged, non-educated, those unable to access internet) are factored in the study.

Secondly, although the tool was developed based on a template was developed based on the US National Election Study (NES) template, the reliability and validity of the scale were assessed in the Australian context. This therefore raises the need for modification and evaluation of the measurement scale on the international, regional, and local contexts. Finally, this study concentrated on construct validity and the reliability of the tool, however, in subsequent study undertakings, additional extensive concurrent validity analysis is recommended to the scale prior to its use. Needful to say that, despite the above outlined limitations, the outcome of this study propose a reliable and valid scale for future evaluation of the subject matter based on the adopted template and guidelines.

Conclusion

In conclusion, the researcher recommends the utilization of this study results in establishment of a more reliable and valid measure scale for political trust. Evidence yielded from this study would form part of useful scale to measure the status of a given politician’s level of trust by the electorate and the general public in Australia. In the future, the Political Trust Scale (PTC) will be useful in broadly evaluating the level of political trust, the progress of the politician political intervention in regards to the expectation of the general public. Further, this kind of scale developed therefore would greatly contribute to the knowledge base and facilitate for utility in both research and practice.

ReferenceS

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PytlikZillig, L. M., & Kimbrough, C. D. (2016). Consensus on conceptualizations and definitions of trust: Are we there yet? In E. Shockley & T. M. S. Neal (Eds.), Interdisciplinary Perspectives on Trust (pp. 17-47). New York: Springer International Publishing.

Marien, S., & Hooghe, M. (2011). Does political trust matter? An empirical investigation into the relation between political trust and support for law compliance. European Journal of Political Research, 50(2), 267-291

Parker, S. L., Parker, G. R., & Towner, T. L. (January 01, 2015). Rethinking the Meaning and Measurement of Political Trust. International Studies in Sociology and Social Anthropology, 125, 59-82.

Trust. (n.d.). Retrieved May 27, 2017, from https://www.merriam-webster.com/dictionary/trust