What does causality mean when conducting social science research select all that apply quizlet?

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Quantitative research is a strategy which involves the collection of numerical data, a deductive view of the relationship between theory and research, a preference for a natural science approach (and for positivism in particular), and an objectivist conception of social reality.

It is important to note that quantitative research thus means more than the quantification of aspects of social life, it also has a distinctive epistemological and ontological position which distinguishes it from more qualitative research.

An ideal-typical outline of the stages of quantitative research:

1. Theory 

The fact that quantitative research starts off with theory signifies the broadly deductive approach to the relationship between theory and research in this tradition. The sociological theory most closely associated with this approach is Functionalism, which is a development of the positivist origins of sociology.

2. Hypothesis 

It is common outlines of the main steps of quantitative research to suggest that a hypothesis is deduced from the theory and is tested.

However, a great deal of quantitative research does not entail the specification of a hypothesis, and instead theory acts loosely as a set of concerns in relation to which social researcher collects data. The specification of hypotheses to be tested is particularly likely to be found in experimental research but is often found as well in survey research, which is usually based on cross-sectional design.

3. Research design 

The next step entails the selection of a research design which has implications for a variety of issues, such as the external validity of findings and researchers’ ability to impute causality to their findings.

4. Operationalising concepts

Operationalising concepts is a process where the researcher devises measure of the concepts which she wishes to investigate. This typically involves breaking down abstract sociological concepts into more specific measures which can be easily understood by respondents. For example, ‘social class’ can be operationalied into ‘occupation’ and ‘strength of religious believe’ can be measured by using a range of questions about ‘ideas about God’ and ‘attendance at religious services’.

5. selection of a research site or sites

With laboratory experiments, the site will already be established, in field experiments, this will involve the selection of a field-site or sites, such as a school or factory, while with survey research, site-selection may be more varied. Practical and ethical factors will be a limiting factor in choice of research sites.

6. Selection of respondents

Step six involves ‘choosing a sample of participants’ to take part in the study – which can involve any number of sampling techniques, depending on the hypothesis, and practical and ethical factors. If the hypothesis requires comparison between two different groups (men and women for example), then the sample should reflect this.

Step six may well precede step five – if you just wish to research ‘the extent of teacher labelling in schools in London’, then you’re pretty much limited to finding schools in London as your research site(s).

7. Data collection

Step seven,  is what most people probably think of as ‘doing research’.  In experimental research this is likely to involve pre-testing respondents, manipulating the independent variable for the experimental group and then post-testing respondents. In cross-sectional research using surveys, this will involve interviewing the sample members by structured-interview or using a pre-coded questionnaire. For observational research this will involve watching the setting and behaviour of people and then assigning categories to each element of behaviour.

8. Processing data

This means transforming information which has been collected into ‘data’. With some information this is a straightforward process – for example, variables such as ‘age’, or ‘income’ are already numeric.

Other information might need to be ‘coded’ – or transformed into numbers so that it can be analysed. Codes act as tags that are placed on data about people which allow the information to be processed by a computer.

9. Data analysis

In step nine, analysing data, the researcher uses a number of statistical techniques to look for significant correlations between variables, to see if one variable has a significant effect on another variable.

The simplest type of technique is to organise the relationship between variables into graphs, pie charts and bar charts, which give an immediate ‘intuitive’ visual impression of whether there is a significant relationship, and such tools are also vital for presenting the results of one’s quantitative data analysis to others.

In order for quantitative research to be taken seriously, analysis needs to use a number of accepted statistical techniques, such as the Chi-squared test, to test whether there is a relationship between variables. This is precisely the bit that many sociology students will hate, but has become much more common place in the age of big data!

10. Findings and conclusions 

On the basis of the analysis of the data, the researcher must interpret the results of the analysis. It is at this stage that the findings will emerge: if there is a hypothesis, is it supported? What are the implications of the findings for the theoretical ideas that formed the background of the research?

11. Writing up Findings 

Finally, in stage 11, the research must be written up. The research will be writing for either an academic audience, or a client, but either way, a write-up must convince the audience that the research process has been robust, that data is as valid, reliable and representative as it needs to be for the research purposes, and that the findings are important in the context of already existing research.

Once the findings have been published, they become part of the stock of knowledge (or ‘theory’ in the loose sense of the word) in their domain. Thus, there is a feedback loop from step eleven back up to step one.

The presence of an element of both deductivism (step two) and inductivism is indicative of the positivist foundations of quantitative research.

Sources

Bryman (2016) Social Research Methods

A cross-sectional study involves looking at data from a population at one specific point in time. The participants in this type of study are selected based on particular variables of interest. Cross-sectional studies are often used in developmental psychology, but this method is also used in many other areas, including social science and education.

Cross-sectional studies are observational in nature and are known as descriptive research, not causal or relational, meaning that you can't use them to determine the cause of something, such as a disease. Researchers record the information that is present in a population, but they do not manipulate variables.

This type of research can be used to describe characteristics that exist in a community, but not to determine cause-and-effect relationships between different variables. This method is often used to make inferences about possible relationships or to gather preliminary data to support further research and experimentation.

For example, researchers studying developmental psychology might select groups of people who are different ages but investigate them at one point in time. By doing this, any differences between the age groups can presumably be attributed to age differences rather than something that happened over time.

Some of the key characteristics of a cross-sectional study include:

• The study takes place at a single point in time
• It does not involve manipulating variables
• It allows researchers to look at numerous characteristics at once (age, income, gender, etc.)
• It's often used to look at the prevailing characteristics in a given population
• It can provide information about what is happening in a current population

Think of a cross-sectional study as a snapshot of a particular group of people at a given point in time. Unlike longitudinal studies, which look at a group of people over an extended period, cross-sectional studies are used to describe what is happening at the present moment.

This type of research is frequently used to determine the prevailing characteristics in a population at a certain point in time. For example, a cross-sectional study might be used to determine if exposure to specific risk factors might correlate with particular outcomes.

A researcher might collect cross-sectional data on past smoking habits and current diagnoses of lung cancer, for example. While this type of study cannot demonstrate cause and effect, it can provide a quick look at correlations that may exist at a particular point.

For example, researchers may find that people who reported engaging in certain health behaviors were also more likely to be diagnosed with specific ailments. While a cross-sectional study cannot prove for certain that these behaviors caused the condition, such studies can point to a relationship worth investigating further.

Cross-sectional studies are popular because they have several benefits that make them useful to researchers.

Cross-sectional studies are usually allow researchers to collect a great deal of information quite quickly. Data is often obtained inexpensively using self-report surveys. Researchers are then able to amass large amounts of information from a large pool of participants.

Researchers can collect data on a few different variables to see how differences in sex, age, educational status, and income, for example, might correlate with the critical variable of interest.

While cross-sectional studies cannot be used to determine causal relationships, they can provide a useful springboard to further research. When looking at a public health issue, such as whether a particular behavior might be linked to a particular illness, researchers might utilize a cross-sectional study to look for clues that will serve as a useful tool to guide further experimental studies.

For example, researchers might be interested in learning how exercise influences cognitive health as people age. They might collect data from different age groups on how much exercise they get and how well they perform on cognitive tests. Performing such a study can give researchers clues about the types of exercise that might be the most beneficial to cognitive health and inspire further experimental research on the subject.

No method of research is perfect. Cross-sectional studies also have potential drawbacks.

Other variables can affect the relationship between the inferred cause and outcomes, and this type of research doesn't allow for conclusions about causation.

Groups can be affected by cohort differences that arise from the particular experiences of a unique group of people. Individuals born during the same period may share important historical experiences, but people in that group who are born in a given geographic region may share experiences limited solely to their physical location.

Surveys or questionnaires about certain aspects of people's lives may not always result in accurate reporting, and there is usually not a mechanism for verifying this information.

This type of research differs from longitudinal studies in that cross-sectional studies are designed to look at a variable at a particular point in time. Longitudinal studies involve taking multiple measures over an extended period.

As you might imagine, longitudinal studies tend to require more resources and are often more expensive than cross-sectional resources. They are also more likely to be influenced by what is known as selective attrition, which means that some individuals are simply more likely to drop out of a study than others. This can influence the validity of the study.

One of the advantages of cross-sectional studies is that since data is collected all at once, it's less likely that participants will quit the study before data is fully collected.

Cross-sectional studies can be a useful research tool in many areas of health research. By learning more about what is going on in a specific population, researchers are better able to understand relationships that might exist between certain variables and develop further studies that explore these conditions in greater depth.

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