Chapters 14-16 Capstone.

Secondary analysis 

Analysing data that you were not involved in the collection of, for purposes that may not have been envisaged by those responsible for data collection 

Advantages of secondary analysis

• Reduced costs and time

• Higher quality data

• Opportunity for longitudinal analysis, sub-group analysis, or cross-sectional analysis

• Reanalysis may suggest new interpretations

• Fulfils wider obligations

Disadvantages of secondary analysis

• Lack of familiarity with the data

• Complexity of the data

• Lack of control over data quality

• Likely absence of key variables

Meta-analysis

Summarising and comparing the results of a large number of quantitative studies performed on a particular topic and conducting various analytical tests to show whether or not a particular variable has an effect 

Advantages of official statistics

• Reduced time and cost

• Potential for complete picture

• Opportunity for cross-sectional analysis, longitudinal analysis, and cross-cultural analysis

• Lower risk of reactivity 

Unobtrusive method

Any method of observation that directly removes the observer from the set of interactions of events being studied

Disadvantages of official statistics

Criticisms of their reliability and validity:

• Reliability 

  • definitions, categories, and allocated resources change over time

  • reflects priorities of agencies/organizations

  • example: changing definitions of crime

• Validity

  • variation may be caused by factors not studied by official reports

  • the ecological fallacy

Ecological fallacy  

The error of assuming that inferences about individuals can be made from findings relating to aggregate data.

Big Data

Usually taken to refer to extremely large sources of data that are not immediately amenable to conventional ways of handling them

Univariate

Bivariate

Looking at patterns between two variables

Multivariate

Analysing three or more variables simultaneously 

Descriptive statistics

Methods used to describe data and their characteristics

Inferential statistics

Methods to make inferences (estimates or predictions) about what we don’t know

Missing data

When respondents fail to reply to a question—either by accident or because they do not want to answer it.

Interval/Ratio

Scale with where categories are equally distanced and constant

Ordinal

Categories that can be ranked

Nominal

Categories which cannot be ranked

Dichotomous 

Data that only has 2 categories

Mean

Sum all values in distribution, then divide by total number of values

Median

Middle point within entire range of values

Mode

Most frequently occurring value

Range

The difference between the maximum and minimum value in a distribution of values.

Standard Deviation

Average difference between values and the mean

Phi (ϕ)coefficients

For the relationship between two dichotomous variables  (values of -1 to +1)

Cramer’s V

For the relationship between two nominal variables, or one nominal and one ordinal variable (values between 0 and 1)

Comparing means

When a nominal variable is identified as the independent variable, the means of the interval/ratio variable are compared for each sub-group of the nominal variable

Eta (η)

For the level of association between different types of variables, even when there is no linear relationship between them

Multivariate analysis

• The relationship between two variables might be spurious

  • Each variable could be related to a separate, third variable

• There might be an intervening variable

• A third variable might be moderating the relationship

  • e.g. correlation between age and exercise could be moderated by gender

Multiple regression 

Allows us to account for more and more variation in our dependent variable

Statistical significance

How confident can we be that the findings from a sample can be generalised to the population as a whole?

Type One Errors

Risk of rejecting the null hypothesis when it should be confirmed

Type 2 Errors

Risk of confirming the null hypothesis when it should be rejected.

  Chi-square test

• Establishes how confident we can be that there is a relationship between the two variables in the population. 

• The chi-square value is determined by calculating the differences between the actual and expected values for each cell and then summing those differences.

ANOVA test

• Assesses for probability of random associations between groups based on trends in the data. 

• Assumes a 0.05 - p value

• Compares a calculated number with a critical value chart based on 0.05 p value

What is qualitative research? 

• Emphasizes words, images, and objects

• Broadly inductivist, constructionist, and interpretivist

• Aims to generate deep insights

Journals

• Qualitative Sociology

• Qualitative Research

• Ethnography

• Qualitative Inquiry

Textbooks

• An introduction to qualitative research

• Doing qualitative research

• Handbook of qualitative research

Data collection methods in qualitative research

• Ethnography/participant observation

• Qualitative interviewing

• Focus groups

• The collection of texts and documents

The main preoccupations of qualitative researchers

• Seeing through the eyes of those being studied

• Providing full descriptions and emphasizing context

• The importance of process

• Prioritising flexibility

• Grounding concepts and theory in data

Grounded theory

• Not actually a theory in itself, it is rather an approach to generating theory from data

• Data collection and analysis are done hand-in-hand, with constant checking back and forth

• Useful in producing concepts

Definitive concepts

Concepts typified by the way in which a concept, once developed, becomes entirely defined by its indicators

Sensitizing concepts

Provide a more general sense of what to look for and guide empirical work

Triangulation 

• Use of more than 1 method or source of data to study social phenomena

• Cross-referencing one method or source of data with another to increase the researcher’s field of vision and cross-validate findings

Respondent validation 

• Sometimes called participant validation or member checking

• A researcher asking their participants to validate aspects of the research

• Checking findings and impressions are consistent with the views of those on whom the research was conducted

Reflexivity

• Reflecting upon yourself and your experiences 

• Explaining the position of the researcher in relation to the position of the researched

Research quality and qualitative research

• Reliability

• Validity

• Generalizability

The critique of qualitative research

• Too subjective

• Difficult to replicate

• Difficult to generalize

• Not sufficiently transparent