This is about Quantitative Data Analysis, Schemes and Mind Maps of Sociology

Download This is about Quantitative Data Analysis and more Schemes and Mind Maps Sociology in PDF only on Docsity!

Quantitative Data Analysis

Introduction

 (^) This section explores:  (^) The importance of analysing data from the earliest stage of your research;  (^) The distinctions between the different variables generated in quantitative research;  (^) Knowing how to examine variables and relationships between them;  (^) Methods for analysing a single variable at a time (univariate analysis);  (^) Methods for analysing relationships between variables (bivariate analysis);  (^) The analysis of relationships between three variables (multivariate analysis).

 (^) She secures access to contact sample from nearby gym  (^) The gym has 1,200 members  (^) Student decided to take a random sample of 10% (120 members) of the membership  (^) She sends out postal questionnaires with a letter of support from the gym  (^) Student focused on how much time people spend on:  (^) Cardiovascular and weights equipment, and  (^) Exercises.  (^) Requested to return the questionnaires to her in a prepaid reply envelope  (^) Student ends up with a sample of 90 questionnaires—a response rate of 75%

Variables

 (^) Def: characteristics that can take on different values, such as height, age, test scores etc.  (^) Researchers often manipulate or measure independent and dependent variables  (^) To test cause-and-effect relationships.  (^) The independent variable is the cause.  (^) Its value is independent of other variables in your study.  (^) The dependent variable is the effect.  (^) Its value depends on changes in the independent variable.

1. Interval variable

 (^) Distance between variables is equal.

2. Ordinal variable

 (^) Categorical variable for which the possible values are rank ordered.  (^) The distances between the categories are uneven or unknown.

 Dichotomous variable

 (^) Nominal variables that can only take on two values,  (^) They can be considered as having attributes of the other three types of variable.  (^) They look as though they are nominal variables, but because they have only one interval  (^) They are sometimes treated as ordinal variables.  (^) However, it is probably safest to treat them as if they were ordinary nominal variables.  (^) For example, males and females.

Coding  (^) To conduct analysis, researchers often must engage in a coding process  (^) This is conducted after the data have been collected.  (^) Quantitative coding - categorising the non-numerical information into groups and  (^) Assigning the numerical codes to these groups.  (^) Raw numerical data is captured into a codebook  (^) This numerical data is categorized (assigned location) in the codebook  (^) This makes it easier for processing and analysis

 (^) A codebook serves two essential functions:

1. It’s the primary guide used in the coding process.
2. It’s a guide for locating variables and interpreting codes in your data file during analysis  (^) Example of codebook:

 (^) Independent variable attributes are typically presented in the table’s columns,  (^) Dependent variable attributes are presented in rows.

 (^) Table 7.1 shows association between gender and experiencing harassing behaviours at work  (^) Gender is the independent variable (the predictor);  (^) The harassing behaviours listed are the dependent variables (the outcome)  (^) Independent variable - influences outcomes – cause  (^) Dependent variable – outcome as a result of independent variable – effect