Science is empirical, open, evolving and objective, but is religion the opposite?
What are the differences between science and religion? This post focuses on four areas of difference between the two:
The empirical versus the supernatural
Open versus closed belief systems
Evolving versus absolute knowledge
Objectivity versus subjectivity
Before reading this post, you might like to refresh your knowledge of what they key features of science are by reading this post: Is sociology a science?
Science limits itself to the empirical, religion concerns itself with the supernatural
Science tends to concern itself with the natural or physical world – that which can be observed and measured. If it cannot be observed or measured empirically, then it is not scientific.
Scientific knowledge is gained primarily through the experimental method: a hypothesis is formulated and then experiments designed to test the hypothesis. Experiments use standardized procedures of data collection, so that other scientists can repeat the exact same experiment in the same way and verify the data and test the findings for themselves.
In contrast religion tends to concern itself with the spiritual world, many aspects of which cannot necessarily be observed and measured in a scientific extent. For example, knowledge in many religions comes ultimately from God, and belief in the existence of God cannot be verified empirically. Belief in God is a matter of faith.
Any knowledge claims made by religions which are not verifiable by empirical observations cannot be regarded as ‘scientific’.
‘Open’ verses ‘closed belief systems
Science is an ‘open belief system’ – the data collected by scientists are open to testing by others. Research findings can thus be criticized.
According to Popper, the process of scientists critically scrutinizing findings of other scientists is fundamental to the scientific method. He argued that scientists should attempt to ‘falsify’ already existing hypotheses by designing experiments deliberately to disprove them. It is this process which ensures that scientific knowledge is valid: its ability to withstand the critical scrutiny of peers.
In contrast, religions tend to have ‘closed belief systems’ – religious knowledge is generally regarded as sacred, and should be accepted as is, rather than challenged.
Evolving versus absolute knowledge systems
Scientific knowledge is cumulative…. it evolves through a process of scientists learning about, criticizing, and improving upon the experimental work of previous scientists.
Religious belief systems, at least those based firmly on religious texts or an idea of an absolute truth are not open to change or growth. Those who challenge such religious belief systems may well be subject to sanctions.
Objectivity and value-freedom versus subjectivity
Subjective, personal feelings should be kept out the scientific process. Scientific knowledge should not be influenced by the personal opinions or biases of the researchers who conduct the experiments which provide the data to generate scientific knowledge.
In contrast, knowledge in many religious traditions is a matter of personal faith and intimate spiritual experience. Many religious experiences, prayer, for example, are highly personal, and not meant to be replicated by others.
This post was written primarily for students of A-level sociology (AQA exam board) and is one of the more difficult topics taught as part of the beliefs in society module.
Positivists prefer to the limit themselves the study of objective ‘social facts’ and use statistical data and the comparative method to find correlations, and multivariate analysis to uncover statistically significant ‘causal’ relationships between variables and thus derive the laws of human behaviour.
This post explores the Positivist approach to social research, defining and explaining all of the above key terms and using some examples from sociology to illustrate them.
Social Facts
The first rule of Positivist methodology is to consider social facts as things which means that the belief systems and customs of the social world should be considered as things in the same way as the objects and events of the natural world.
According to Durkheim, some of the key features of social facts are:
they exist over and above individual consciousness
they are not chosen by individuals and cannot be changed by will
each person is limited (constrained) by social facts
According to Durkheim what effects do social facts make people act in certain ways, in the same way as door limits the means whereby you can enter a room or gravity limits how far you can jump.
Positivists believed that we should only study what can be observed and measured(objective facts), not subjective thoughts and feelings. The role of human consciousness is irrelevant to explaining human behaviour according to Positivists because humans have little or no choice over how they behave.
For a more in-depth account of social facts, have a look at this blog post: What are Social Facts?
Statistical data, Correlation, and Causation
Positivists believed it was possible to classify the social world in an objective way. Using these classifications it was then possible to count sets of observable facts and so produce statistics.
The point of identifying social facts was to look for correlations – a correlation is a tendency for two or more things to be found together, and it may refer to the strength of the relationship between them.
If there is a strong correlation between two ore more types of social phenomena then a positivist sociologist might suspect that one of these phenomena is causing the other to take place. However, this is not necessarily the case and it is important to analyse the data before any conclusion is reach.
Spurious Correlations
Spurious correlations pose a problem for Positivist research. A spurious correlation is when two or more phenomena are found together but have no direct connection to each other: one does not therefor cause the other. For example although more working class people commit crime, this may be because more men are found in the working classes – so the significant relationship might be between gender and crime, not between class and crime.
Multivariate Analysis
Positivists engage in multivariate analysis to overcome the problem of spurious correlations.
Multivariate Analysis involves isolating the effect of a particular independent variable upon a particular dependent variable. This can be done by holding one independent variable constant and changing the other. In the example above this might mean comparing the crime rates of men and women in the working class.
Positivists believe multivariate analysis can establish causal connections between two or more variables and once analysis is checked establish the laws of human behaviour.
Positivism – Establishing the Laws of Human Behaviour
A scientific law is a statement about the relationship between two or more phenomena which is true in all circumstances.
According to Positivists, the laws of human behaviour can be discovered by the collection of objective facts about the world in statistical form and uncovering correlations between them, checked for their significance by multivariate analysis.
Positivism and The Comparative Method
The comparative method involves the use of comparisons between different societies, or different points in time
The purpose of using the comparative method is to establish correlations, and ultimately causal connections, seek laws and test hypotheses.
The comparative method overcomes the following disadvantages of experiments:
Moral problems are not as acute
The research is less likely to affect the behaviour or those being studied because we are looking at natural settings
The comparative method is superior to the experimental method because allows the sociologist to explore large scale social changes and changes over time
However, a fundamental problem with the comparative method is that the data you want may not be available, and you are limited to that data which already exists or which can be collected on a large scale via social surveys.
In the recent June 2017 General Election, Labour won more votes than it did in 2001, 2005, 2010 or 2015, proving almost all the forecasts and commentators wrong.According to this Guardian article there are three main reasons for this…
It motivated young people to get out and vote.
A lot’s been made of the historically high turnout by 18-24 year olds…. It looks like in key constituencies – from Harrow West to Canterbury (a seat that has been Conservative since 1918) – the youth vote was vital. Labour showed it cared about young people by promising to scrap tuition fees, an essential move to stop the marketisation of higher education, and it proposed a house-building programme that would mean many more could get on the property ladder.
This is in stark contrast to the two other major parties – the Lib Dems in 2010 under Nick Clegg lied to them, and the Conservatives have attacked them – cutting housing benefits for 18- to 21-year-olds, excluding under-25s from the minimum wage rise and slashing the education maintenance allowance. At this election, Theresa May offered nothing to young people in her manifesto. Their message was: put up with your lot. Under the Tories, young people have been taken for granted and sneered at as too lazy to vote.
The NUS reported a 72% turnout by young people, and there is a definite thread in the media attributing the swing towards Labour as down to this.
However, this is contested by Jack Sommors in this article who suggests that it was middle-aged people who swung the election result away from the Tories.
‘Lord Ashcroft’s final poll, which interviewed 14,000 people from Wednesday to Friday last week, found people aged 35 to 44 swung to Labour – 50% voted for them while just 30% voted for the Tories. This is compared to 36% of them voting Labour and 26% backing the Tories just two years ago’.
A further two reasons which might explain the swing, let’s say among the younger half of the voting population, rather than just the very youngest are:
Labour offered localised politics, not a marketing approach
Labour rejected the marketing approach to politics in favour of a strong, localised grassroots campaign… this was not simply an election May lost; it was one in which Corbyn’s Labour triumphed. Labour proposed collectivism over individualism and a politics that people could be part of.
Labour offered a genuine alternative to neoliberalism…
Labour offered a positive agenda to an electorate that’s been told its only choice is to swallow the bitter pill of neoliberalism – offering a decisive alternative to Tory austerity in the shape of a manifesto packed with policies directly challenging what has become the economic status quo in the UK. Labour no longer accepted the Tory agenda of cuts (a form of economics long ago abandoned in the US and across Europe): it offered investment in public services, pledged not to raise taxes for 95% of the population, talked about a shift to a more peaceful foreign policy, promised to take our rail, water and energy industries out of shareholders’ hands and rebalance power in the UK.
So how is this relevant to A-level Sociology…?
In terms of values…It seems to show a widespread rejection of neoliberal ideas among the youth, and possibly evidence that neoliberal policies really have damaged most people’s young people’s (and working class people’s) life chances, and this result is a rejection of this.
In terms of the media… It’s a reminder that the mainstream media doesn’t reflect public opinion accurately- just a thin sliver of the right wing elite. It also suggests that the mainstream media is losing its power to shape public opinion and behavior, given the negative portrayals of Corbyn in the mainstream. .
Value-Freedom and explaining election results…
The above article is written with a clearly left-leaning bias. Students may like to reflect on whether it’s actually possible to explain the dramatic voter swing towards Labour objectively, and how you might go about getting valid and representative data on why people voted like they did, given that there are so many possible variables feeding into the outcome of this election?!
Value Freedom in Social Research refers to the ability of the researcher to keep his or her own values (personal, political and religious) from interfering with the research process.
The idea that ‘facts’ should not be influenced by the researcher’s own beliefs is a central aspect of ‘science’ – and so when we say that Sociology can and should be value free this is essentially the same as saying that ‘Sociology can and should be scientific’.
Positivism and Value Freedom
In the late 19th and early 20th centuries Positivist Sociologists such as August Comte and Emile Durkheim regarded Sociology as a science and thus thought that social research could and should be value free, or scientific.
As illustrated in Durkheim’s study of Suicide (1899) – by doing quantitative research and uncovering macro-level social trends Sociologists can uncover the ‘laws of society’. Durkheim believed that one such law was that too high or too low levels of social integration and regulation would lead to an increasing suicide rate. Positivists believed that further research would be able to uncover how much of what types of integration caused the suicide rate to go up or down. We should be able to find out, for example, if a higher divorce rate has more impact on the suicide rate that the unemployment rate.
So at one level, Positivists believe that Sociology can be value free because they are uncovering the ‘objective’ laws of how social systems work – these laws exist independently of the researchers observing them. All the researcher is doing is uncovering ‘social facts’ that exist ‘out there’ in the world – facts that would exist irrespective of the person doing the observing.
Positivists argued that such value-free social research was crucial because the objective knowledge that scientific sociology revealed could be used to uncover the principles of a good, ordered, integrated society, principles which governments could then apply to improve society. Thus, research should aim to be scientific or value free because otherwise it is unlikely to be taken seriously or have an impact on social policy.
Being “value free” is sometime described as being objective: to uncover truths about the world, one must aspire to eliminate personal biases, a prior beliefs, and emotional and personal involvement, etc.
Questions
Identify the TWO methods you would use to achieve a high degree of objectivity. And explain why?
Is it possible to completely objective/value free?
‘Right Wing’ Perspectives on Value Freedom and Sociology
The New Right argue that sociology is not value free but rather left-wing propaganda.
In the 1970’s and 1980’s, Sociology came under attack for its ‘left-wing’ bias. Originally criticized for its inclusion in teacher training programmes, it was further suggested that teachers were indoctrinating their students with Marxist propaganda. David Marsland is particularly associated with the idea of Sociology as a destructive force in British society, exaggerating the defects of capitalism and ignoring its many benefits:
‘Sociology is the enemy within. It is an enemy that sows the seeds of bankruptcy and influences huge numbers of impressionable people… Sociologists are neglecting their responsibility for accurate, objective description and biasing their analyses of contemporary Britain to an enormous extent… huge numbers of people are being influenced by the biased one-sidedness of contemporary Sociology.’
In ‘Bias against Business’, Marsland suggests that many Sociology textbooks ignore the central features of capitalist economies Concentrating on job dissatisfaction and alienation:
‘Its treatment of work is consistently negative, focussing almost entirely on its pathologies – alienation, exploitation and inequality. It underestimates the high levels of job satisfaction which empirical research has consistently identified. It de-emphasises the enormous value for individual people and for society as a whole, in the way of increased standards of living and enhanced quality of life work provides. It neglects for the most part to inform students about the oppressive direction of labour of all sorts of socialist societies, or to keep them in mind of the multiple benefits of a free competitive labour market. It treats the need for economic incentives with contempt.’
Feminism – Sociology is biased against women
Feminists are critical of the ‘value-free’ scientific claims of ‘malestream’ Sociology, arguing that it is at best sex blind and at worst sexist, serving as an ideological justification for the subordination of women. Anne Oakley (1974) claims that ‘Sociology reduces women to a side issue from the start.’ While Sociology claims to put forward a detached and impartial view of reality, in fact it presents the perspective of men.
Feminist responses to the male bias in Sociology have been varied; on the one hand there are those who think that this bias can be corrected simply by carrying out more studies on women; a more radical view (arguing along the same lines of Becker’s ‘Whose Side are We On’) suggests that what is needed is a Sociology for women by women; that feminists should be concerned with developing a sociological knowledge which is specifically by and about women:
‘A feminist Sociology is one that is for women, not just or necessarily about women, and one that challenges and confronts the male supremacy which institutionalizes women’s inequality. The defining characteristic of feminism is the view that women’s subordination must be questioned and challenged… feminism starts from the view that women are oppressed and that their oppression is primary’. (Abbott & Wallace 1990).
Interpretivism – sociology cannot and should not be value free
There are three main Interpretivist Criticisms of ‘Positivist’ Sociology – from Gomm, Becker and Gouldner:
Gomm argues that ‘a value free Sociology is impossible… the very idea is unsociological’.He argues that Sociologists react to political, economic and social events – and what is seen as a political or social ‘issue’, a social ‘problem’ is dependent on the power of different groups to define and shape reality – to define what is worthy of research. Consequently, it is just as important to look at what sociologists do not investigate as what they do – Sociologists are not necessarily immune to ideological hegemony.
Gomm argues that social research always has social and moral implications. Therefore Sociology inevitably has a political nature. For the sociologists to attempt to divorce him/herself from the consequences of his/her research findings is simply an evasion of responsibility. Gomm further suggests that when the sociologist attempts to divorce himself from his own values to be scientific, to become a ‘professional sociologist’ he is merely adopting another set of values – not miraculously becoming ‘value free’ – what Positivists call value freedom often involves an unwitting-commitment to the values of the establishment.
‘The truth is, of course, not that values have actually disappeared from the social sciences, rather that the social scientist has become so identified with the going values of the establishment that it seems as if values have disappeared.’
Gouldner, along similar lines to Gomm,argues that it is impossible to be free from various forms of value judgment in the social sciences. Those who claim to be value free are merely gutless non-academics with few moral scruples who have sold out to the establishment in return for a pleasant university lifestyle.
Gouldner suggests that the principle of value freedom has dehumanised sociologists: ‘Smugly sure of itself and bereft of a sense of common humanity.’ He claims that sociologists have betrayed themselves and Sociology to gain social and academic respectability; confusing moral neutrality with moral indifference, not caring about the ways in which their research is used.
Howard Becker, in ‘Whose side are we on?’ takes this argument to its logical conclusion arguing that since all knowledge is political, serving some interests at the expense of others, the task for the sociologist is simply to choose sides; to decide which interests sociological knowledge should serve. Becker argues that Sociology should side with the disadvantaged.
Signposting and Related Posts
This topic is a core aspect of the social theories part of ‘theory and methods’ within A-level sociology.
Personally I tend to think of this topic as an extension of the Positivsm-Interpretivism debate within Sociology.
Experiments aim to measure the effect which an independent variable (the ’cause’) has on a dependent variable (‘the effect’).
The key features of an experiment are control over variables, precise measurement, and establishing cause and effect relationships.
In order to establish cause and effect relationships, the independent variable is changed and the dependent variable is measured; all other variables (known as extraneous variables) are controlled in the experimental process.
Different types of experiment
There are three main types of experimental: The Laboratory experiment, the field experiment and the comparative method.
Laboratory Experiments take place in an artificial, controlled environment such as a laboratory
Field Experiments – take place in a real world context such as a school or a hospital.
The comparative method – involves comparing two or more similar societies or groups which are similar in some respects but varied in others, and looking for correlations.
The Key Features of the Experiment
It’s easiest to explain what an experiment is by using an example from the natural sciences, so I’m going to explain about experiments further using an example used from biology
NB – You do need to know about the scientific method for the second year sociology theory and methods part of the course ( for an overview of theories and methods click here), so this is still all necessary information. I’ll return to the use of laboratory and field experiments in sociology (/ psychology) later on…
An example to illustrate the key features of an experiment
If you wished to measure the precise effect temperature had on the amount* of tomatoes a tomato plant produced, you could design an experiment in which you took two tomato plants of the same variety, and grow them in the same greenhouse with same soil, the same amount of light, and the same amount of water (and everything else exactly the same), but grow them on different heat pads, so one is heated to 15 degrees, and the other 20 degrees (5 degrees difference between the two).
You would then collect the tomatoes from each plant at the same time of year** (say in September sometime) and weigh them (*weighing would be a more accurate way of measuring the amount of tomatoes rather than the number produced), the difference in weight between the two piles of tomatoes would give you the ‘effect’ of the 5 degree temperature difference.
You would probably want to repeat the experiment a number of times to ensure good reliability, and then average all the yields of tomatoes to come up with an average difference.
After, say, 1000 experiments you might reasonably conclude that if you grow tomatoes at 20 degrees rather than 15 degrees, each plant will give you 0.5 kg more tomatoes, thus the ’cause’ of the 5 degree temperature increase is 0.5 Kg more tomatoes per plant.
In the above example, the amount of tomatoes is the dependent variable, the temperature is the independent variable, and everything else (the water, nutrients, soil etc. which you control, or keep the same) are the extraneous variables.
** of course, you might get different results if you collected the tomatoes as they ripened, but for the sake of controlling extraneous variables, you would need to collect all the tomatoes at the same time.
The Role of Hypotheses in Experiments
Experiments typically start off with a hypothesis which is a theory or explanation made on the basis of limited evidence as a starting point for further investigation. A hypothesis will typically take the form of a specific, testable statement about the effect which one or more independent variables will have on the dependent variable.
The point of using a hypothesis is that it helps with accuracy, focussing the researcher in on testing the specific relationship between two variables precisely, it also helps with objectivity (see below).
Having collected the results from the above experiment, you might reasonably hypothesise that ‘a tomato plant grown at 25 degrees compared to 20 degrees will yield 0.5K.G. more tomatoes’ (in fact a proper hypothesis would probably be even tighter than this, but hopefully you get the gist).
You would then simply repeat the above experiment, but heating one plant to 20 degrees and the other to 25 degrees, repeat 1000 (or so times) and on the basis of your findings, you could either accept or reject and modify the hypothesis.
Experiments and Objectivity
A further key feature of experiments are that they are supposed to produce objective knowledge – that is they reveal cause and effect relationships between variables which exist independently of the observer, because the results gained should have been completely uninfluenced by the researcher’s own values.
In other words, somebody else observing the same experiment, or repeating the same experiment should get the same results. If this is the case, then we can say that we have some objective knowledge.
A final (quick) word on tomato experiments, and objective knowledge…
NB – the use of tomato plants is not an idle example to illustrate the key features of the experiment – nearly everyone eats tomatoes (unless you’re the minority of Ketchup and Dolmio abstainers) – and so there’s a lot of profit in producing tomatoes, so I imagine that hundred of millions, if not billions of dollars has been spent on researching what combinations of variables lead to the most tomatoes being grown per acre, with the least inputs…. NB there would have to be a lot of experiments because a lot of variables interact, such as type of tomato plant, altitude, wave length of light, soil type, pests and pesticide use, as well as all of the basic stuff such as heat, light, and water.
A woman picks tomatoes at a desert experimental farming greenhouse.
The importance of objective, scientific knowledge about what combination of variables has what effect on tomato production is important, because if I have this knowledge (NB I may need to pay an agricultural science college for it, but it is there!) I can establish a tomato farm and set up the exact conditions for maximum production, and predict with some certainty how many tomatoes I’ll end up with in a season…(assuming I’m growing under glass, where I can control everything).
The advantages of the experimental method
It allows us to establish ’cause and effect relationships’ between variables.
It allows for the precise measurement of the relationship between variables, enabling us to make accurate predictions about how two things will interact in the future.
The researcher can remain relatively detached from the research process, so it allows for the collection of objective knowledge, independent of the subjective opinions of the researcher.
It has excellent reliability because controlled environments allow for the exact conditions of the research to be repeated and results tested.
Disadvantages of the experimental method
(Why it may not be applicable to studying society as a whole or even individual humans…)
There are so many variables ‘out there’ in the real world that it is impossible to control and measure them all.
Most social groups are too large to study scientifically, you can’t get a city into a laboratory to control all it’s variables, you couldn’t even do this with a field experiment.
Human beings have their own personal, emotionally charged reasons for acting, which often they don’t know themselves, so they are impossible to measure in any objective way.
Human beings have consciousness and so don’t just react in a predictable way to external stimuli: they think about things, make judgements and act accordingly, so it’s impossible to predict human behaviour.
There are also ethical concerns with treating humans as ‘research subjects’ rather than equal partners in the research process.
Experiments – Key Terms
Hypothesis – a theory or explanation made on the basis of limited evidence as a starting point for further investigation. A hypothesis will typically take the form of a testable statement about the effect which one or more independent variables will have on the dependent variable.
Dependent Variable – this is the object of the study in the experiment, the variable which will (possibly) be effected by the independent variables.
Independent variables – The variables which are varied in an experiment – the factors which the experimenter changes in order to measure the effect they have on the dependent variable.
Extraneous variables – Variables which are not of interest to the researcher but which may interfere with the results of an experiment
Experimental group – The group under study in the investigation.
Control group – The group which is similar to the study group who are held constant. Following the experiment the experimental group can be compared to the control group to measure the extent of the impact (if any) of the independent variables.
