Top 7 Public Policy Models

 Public policy focuses on policy analysis of political, social, and economic interventions formulated by governments to solve problems that have an impact on society. As government interventions have continuously expanded in size and scope, there has been an increased reliance on scientific approaches to analyze how and why a decision is taken by the government. 

Public policy analysis deals with the utilization of scientific theories and methodologies in assessing issues of public importance to provide statistical data on the impact of the government’s various policies.

The type of model employed in public policy depends on certain assumptions or what you are trying to accomplish. Below are some of the foundational models commonly used to assess and understand the policy.

●     Institutionalism emphasizes the role of institutions and government as separate branches and the power structures that exist to ensure checks and balances.  It focuses on the policy process (structures, functions, and duties) of governmental institutions rather than policy outcomes. How policy moves from a conception stage, all the way to the evaluation stage and its impact. Institutionalism uses organizational charts in assessing policy and draws insights from social science disciplines such as political science, economics, and sociology. 

●     Rationalism focuses on an efficient decision-making process. It emphasizes that policymakers have a wide range of policy options to select from, and the consequences of each alternative policy option relative to cost (using the cost-benefit analysis), should ensure that policy options selected by institutions maximizes societal gains at the least cost. It recommends the use of predictive analysis in the policy option selection process.

●      Incrementalism emphasizes policy as a continuation of previous policy with minimal disruptions. The idea is that policies once in place, at least, move very slowly and steadily, with little incremental changes made to policies over a long period. Policymakers are reluctant to make huge jumps due to uncertainty about the consequences of new policies, thus only short-run analysis should be undertaken in the search for policy solutions.

●     Group theory also called pluralism talks about public policy and competing groups' control movements, such as special interests, trade unions, religious groups, and other lobby groups that voice their opinions and ideas to influence policies that impact social life and maximize their interests. Thus, policymakers enforce compromise between the various conflicting interests in the society by assessing the likelihood for conflicts between competing groups, and the ability to engage in conflict resolutions.

●     Punctuated Equilibrium Theory states that policy changes essentially occur gradually over long periods. However, policy change can be dramatic and quick if spurred by major or structural events necessitating a new direction for future policy. Punctuated Equilibrium Theory assesses the long periods of policy stability interrupted by the short but critical periods of change.

●     Public choice theory looks into market forces and their economic impact on policy mechanisms. It theorizes that self-interest motivates individuals' personal and communal choices, and as governments consist of individuals, policies are the result of individuals making decisions in their roles as elected officials. Therefore, to understand past governance processes, we need to analyze how individuals in government made personal economic decisions, as individuals have different values and interests, which collectively pursued, can be mutually beneficial.

●    Game theory focuses on rational choices, the idea of competition as opposed to cooperation and outcomes. Game theory is often used in military strategy, finance, and related areas and is a deductive model of policymaking. It assesses how rational decisions are made in competitive situations, using Nash equilibria, imperfect information, and mixed strategies.

 

Public policy is important because government rules and regulations impact various aspects of our lives. Individuals assess public policies based on their impact, that is, who are the policy beneficiaries and who are the losers. Even the most well-intentioned policies can have an unforeseen effect on social life.

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About the author: Emmanuel is an economist and certified project manager, working on operations and strategy for UK research and development network, Masterly Limited.

The Data Analysis Challenge !

Researchers and analysts oftentimes encounter various challenges when undertaking statistical and mathematical analysis.  Some of the challenges include choosing the wrong test for a particular data, misuse or inaccurate use of the statistical tools, inaccurate data retrieval and application process, biased hypothesis generation, and robustness issues. Other challenges in data analysis include ascribing predictions as facts, using data without error correction or treatment, violation of normality and linearity assumptions, handling outliers, and difficulty obtaining consistent data findings. Also, elementary issues, such as correctly tackling flawed theoretical models that tend to skew the analysis in a given direction, continue to be a problem for both researchers and analysts.

 

Masterly is a London-based research and development network, which connects research institutions and businesses to modellers from all over the world.

 

Sociology Research Dilemma: Quantitative or Qualitative Methods?

Research methods in sociology fall into broad categories of quantitative or qualitative approaches. Quantitative approach measures parameters that are derived from counts, or from a numerical scale. The data can be analysed statistically, and conclusions drawn are more likely to be reliable representations of the population, and can also be evaluated to observe for trends over time.

The qualitative approach, on the other hand, is more contextual data, in that it is presented in descriptive (written, verbal or visual) formats and open to interpretation using thematic analysis.

Sociologists that adopt a positivist methodology are more likely to use methods that generate quantitative data, as they are looking to establish trends and patterns of behaviour displayed by people in society.

Interpretivists on the other hand, are more likely to use methods that apply qualitative data as the best fit for the purposes of their research. They look for the meanings and motivations behind the behaviour of groups and this is more likely to be expressed in words and through images and symbols as it is relatively time-consuming to collect and analyze data quantitatively. Also, as the methods for collecting qualitative data are smaller in scale due to the nature of individual responses being different, they focus more on the experiences and perspectives of smaller groups and individuals rather than being able to generalize to a population.

Furthermore, quantitative methods tend to be more structured in nature and often the researcher is detached from the research subjects to allow for an objective viewpoint. Approaches used in natural sciences such as experiments, (both liable controlled and field experiments) usually produce quantitative data. In these experiments, the independent variables are usually subjected to statistical tests to assess the probability of the results being achieved by chance. Questionnaires are another method to produce quantitative data if the questions have been pre-coded and have a finite number of responses or closed questions. Responses can easily be analyzed and trends with patterns organized into charts tables and graphs. The same is true of opinion polls and other surveys that can assess an individual’s intended behaviour or past behaviours and these can be collated and represented through percentages in charts and diagrams. Non-participant observations do not exclusively produce quantitative data, but they can be used in a structured manner to observe the frequency of behaviour over a period of time, which produces quantitative data.

Qualitative methods may be analyzed through participant observations (whether covert or overt) or non-participant observations (where people’s actions, opinions, motivations, and insights are recorded, transcribed, and analyzed).

Another method is case studies, which are usually a mix of data sources, but also take the form of in-depth research into individuals or groups that are viewed as an anomaly. It does not fit into the normal behaviour patterns and the finding of these case studies are often presented in a qualitative format. Lastly, ethnographic approaches utilise qualitative data photographs, videos, blogs as well as methods such as unstructured interviews and journals, diaries and other forms of media such as TV, websites, films, articles and documentaries to produce qualitative data that researchers may use.

Which method is better applied, depends upon one’s theoretical perspective as both methods serve different research purposes. Structural theories prefer quantitative data as it enables analysis of society from a macro perspective. while social action theory prefers qualitative data as it offers them an insight into the experiences of those they are studying.

Perhaps, the most comprehensive way to research society, is to combine both methods, thus increasing the validity of the data by using qualitative methods and the reliability by employing a quantitative approach.

 

Masterly is a London-based research and development network, which connects research institutions and businesses to modellers from all over the world.

Tagged With: Data Science, Biostatistics, Economics, Behavioral Sciences

 Related Posts: What are the Data Analysis Issues, we face? The difference between Theory and Theoretical Framework

 

Why use MATLAB? 5 reasons.

 

MATLAB is a tool for analytical computing and graphical visualization in an integrated environment. MATLAB is employed in a variety of ways by lots of people in different disciplines, such as in public policy, political science, public health, and sociology. Disciplines, not necessarily associated with mathematical application or product. Here are 5 advantages of MATLAB, over other statistical packages.

1.    Plotting Awesome Data Visualization: One of the most important things that we can do with a dataset is to look at it. Good graphic visualization takes time and effort to turn data into insights that can be easily interpreted. MATLAB as a programming package makes it easy to create complex plots from data in a data frame. Its integrated graph editor offers a more dynamic interface for specifying what variables to plot, as well as the overall visual properties. 

2.    Breaking down Dynamic Programming:  Dynamic Programming is a method for solving a complex problem by breaking it down into a collection of simpler subproblems. MATLAB source codes make it easier to solve each of its subproblems and store their solutions using a memory-based data structure. This differentiates it from alternative programming software like GNU Octave and Python, which have relatively complex dynamic programming processes.

3.    Ease of Documentation: Documentation constitutes a record of the statistical activity, including the concepts, definitions, and methods used to collect, process, as well as analyze data and produce statistical products. MATLAB's accuracy stems from its thematic and general documents. Its functions have supporting documentation and keywords along with examples that describe the function inputs in detail.

4.    Walking through a Simulation: The reliability of research projects depends largely on the data analysis process. Amongst the command-driven statistical packages, MATLAB along with R, are the easiest to check commands and verify the simulation process in order to validate the analytical process and the accuracy of the result. 

5.    Saves Time: Using MATLAB shortcuts saves you a ton of time and effort. For instance, if you find yourself typing the same lines of code every day, you can store them as shortcuts. You begin this by creating a shortcut that contains all the statements. The next process is to select and run the shortcut to execute all the statements it contains. Finally, you create, run, and organize shortcuts from the Start -> Shortcut menu or the desktop Shortcuts toolbar. Also, MATLAB features can be easily assessed by using its menu, the desktop toolbar, or by using keyboard shortcuts and accelerators.

Businesses, modellers, and researchers rely heavily on mathematics and statistics to perform their tasks. With its rich toolbox and ease of use, anyone can benefit from the use of MATLAB.

Masterly is a London-based research and development network, which connects research institutions and businesses to modellers from all over the world.

Tagged With: Data Science, Biostatistics, Economics, Behavioral Sciences

 Related Posts: What are the Data Analysis Issues, we face? The difference between Theory and Theoretical Framework

 

The major difference between Econometrics and Statistics

 

A common generalization is that econometrics is just another applied branch of statistics that is focused on economics.

There is certainly a lot of overlay and similarities between econometrics and statistics. While the two disciplines evolved together, however, the 2 disciplines are motivated by different central approaches. Econometrics focuses on a theory-driven approach, which comprises a theoretical model that ascribes a causal relationship between an interpretation and its outcome (termed causal-effect relationship). The underlying objective here is that the data has no intrinsic value, but it is given value when interpreted and analyzed in a rational manner, and in light of a well-defined theory.

In econometrics, questions can be asked about the validity and quality of the data, if it does not fit the theoretical model or if efficient predictions cannot be derived from the data. Econometricians believe limitations exist in what data can disclose, and thus there is a need to rely on theoretical methods in order to produce results

 Statisticians on the other hand, are mostly concerned with statistical inference (data-driven approach), with a focus on data retrieval.  Its primary objective is to ensure that the data sample is a true representation of the population as a whole. The notion is that improved data analysis and interpretation is dependent on the value of the data and not the theory or theoretical model, and if the data is incompatible with the theory, it can be sacrificed for the data, by seeking alternative theories or theoretical models.

 However, econometrics fuses economic assumptions and theories with statistics and this interaction of theory and data-driven approaches are vital for success in research. Furthermore, it is very theoretical and focuses on causal questions such as does attending an expensive university impact on individual’s future earnings, what is the degree to which a monetary policy can alter economic growth and related public issues that matter to an individual, a firm, or the government.

In conclusion, both disciplines are interrelated, and whichever discipline gets precedence of use, depends on the context of the research problem.

 

Masterly is a London-based research and development network, which connects research institutions and businesses to modellers from all over the world.

 

Tagged With: Institutional research, Political Science, Public Health, Public Policy, Sociology

Related Posts: What are the Data Analysis Issues, we face?  The difference between Theory and Theoretical Framework

 

About Author:  Emmanuel Igbinoba is a researcher, associated with Masterly UK