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Social Work Research Cheat Sheet by

A cheat sheet of terminology related to research within the field of Social Work.

Types of Research

Quanti­tative Research:
1.
Numerical, statis­tical instead of giving reason­ings, inform­ation that is objective. Answers factual questions.
2.
Best for repres­ent­ati­veness and genera­liz­abi­lity. Broader study. Greater number of subjects.
3.
Harder to analyze and give reason­ings. Can't explore why.
Qualit­ative Research:
1.
Focused around opinion, feelings and WHY something is happening. Complex data and harder to analyze. Subjective Data.
2.
Detailed inform­ation that explores reason­ings. Based on human experience which gives better validity.
3.
Longer process to analyze the data as it all varies due to subjective nature. If not careful, researcher can have a negative impact on the results behavior.
Mixed Methods Research:
1.
Combines elements of quanti­tative research and qualit­ative research.
2.
Help to gain a more complete picture.
3.
Used often in Social Work Research.

Types of Research

Descri­ptive Research-
research that describes or define a particular phenom­enon.
Explantory Research-
explains why particular phenomena work in the way that they do, answers “why” questions.
Explor­atory Research-
conducted during the early stages of a project, usually when a researcher wants to test the feasib­ility of conducting a more extensive study.

Terms

Attrib­utes-
charac­ter­istics that make up a variable.
Exhaus­tiv­eness-
all possible attributes are listed.
Index-
measure that contains several indicators and is used to summarize a more general concept.
Indica­tors-
represent the concepts that we are interested in studying.
Interval-
the distance between attributes is known to be equal.
Operat­ion­ali­zation-
process by which resear­chers conducting quanti­tative research spell out precisely how a concept will be measured.
Ratio-
attributes can be rank ordered, the distance between attributes is equal, and attributes have a true zero point.
Scale-
composite measure designed in a way that accounts for the possib­ility that different items on an index may vary in intensity.
Typology-
measure that catego­rizes concepts according to particular themes.

Measur­ments

Nominal Scale-
Places people, events, percep­tions, etc. into categories based on a common charac­ter­istic.
Lowest form of measur­ement because it doesn’t capture inform­ation about the focal object other than whether the object belongs or doesn’t belong to a category.
Ordinal Scale-
Contains all of the inform­ation captured in the nominal scale but it also ranks data from lowest to highest.
Rank orders the subjects. Richer than nominal scaling, ordinal scaling still suffers from inform­ation loss in the data.
Interval Scale-
Indicates the distance one object is from another.
Ratio Scale-
Contains all of the inform­ation of the previous three levels plus it contains an absolute zero point.

Variables

Defini­tion-
any charac­ter­istics of an individual that can change from individual to indivi­dual.
Indepe­ndent Variable-
(Expla­nat­ory­/Pr­edi­ctor) manipu­lated by the resear­cher. Purposely change or control in order to see what effect it has.
Dependent Variable-
(Respo­nse­/Ou­tcome) responds to the change in the indepe­ndent variable.
Confou­nding Variable-
affects the relati­onship between the indepe­ndent variable and the dependent variable.
Mediating Variable-
explains the relati­onship between the indepe­ndent variable and the dependent variable. Comes in between the indepe­ndent and dependent variables and is affected by the indepe­ndent variable, which then affects the dependent variable.
Moderator Variable-
affects the strength or direction of the relati­onship between the indepe­ndent variable and the dependent variable.
Control Variable-
held constant or controlled by the researcher to ensure that it does not affect the relati­onship between the indepe­ndent variable and the dependent variable.
Continous Variable-
can take on any value within a certain range.
Catego­rical Variable-
can take on a limited number of values or catego­ries.
Discrete Variable-
can only take on specific values. Discrete variables are often used in counting or frequency analyses.
Dummy Variable-
takes on only two values, typically 0 and 1, and is used to represent catego­rical variables in statis­tical analyses. Dummy variables are often used when a catego­rical variable cannot be used directly in an analysis.
Extraneous Variable-
has no relati­onship with the indepe­ndent or dependent variable but can affect the outcome of the study. Extraneous variables can lead to erroneous conclu­sions and can be controlled through random assignment or statis­tical techni­ques.
Latent Variable-
cannot be directly observed or measured, but is inferred from other variables. Latent variables are often used in psycho­logical or social research to represent constructs such as person­ality traits, attitudes, or beliefs.
Modera­tor­-me­diator Variable-
acts both as a moderator and a mediator. It can moderate the relati­onship between the indepe­ndent and dependent variables and also mediate the relati­onship between the indepe­ndent and dependent variables. Modera­tor­-me­diator variables are often used in complex statis­tical analyses.
 

Sampling

Sample-
Specific group of indivi­duals that you will collect data from.
Sample Frame-
The actual list of indivi­duals that the sample will be drawn from.
Probab­ility Sampling-
Used in Quanti­tative research. Random selection, allowing you to make strong statis­tical inferences about the whole group.
Every member of the population has a chance of being selected.
Types of Probab­ility Sampling:
 
Simple Random Sampling-
Every member of the population has an equal chance of being select­ed.S­hould include the whole popula­tion.
 
Systematic Sampling-
Instead of randomly generating numbers, indivi­duals are chosen at regular intervals. Easier to conduct than Simple Sampling.
 
Stratified Sampling-
Dividing the population into subpop­ula­tions that may differ in important ways. Allows you draw more precise conclu­sions by ensuring that every subgroup is properly repres­ented in the sample.
 
Cluster Sampling-
Divide the population into subgroups, but each subgroup should have similar charac­ter­istics to the whole sample. Instead of sampling indivi­duals from each subgroup, you randomly select entire subgroups.
Non-Pr­oba­bility Sampling-
Used in Qualit­ative and Explor­atory research. Non-random selection based on conven­ience or other criteria, allowing you to easily collect data.
Indivi­duals are selected based on non-random criteria, and not every individual has a chance of being included.
Types of Non-Pr­oba­bility Sampling:
 
Conven­ience Sampling-
Includes the indivi­duals who happen to be most accessible to the resear­cher. Easy, inexpe­nsive. At risk for sampling bias and selection bias.
 
Voluntary Response Sampling-
People volunteer themse­lves. At risk for self-s­ele­ction bias.
 
Purposive Sampling-
Also known as Judgement Sampling. Researcher uses their expertise to select a sample that is most useful to the purposes of the research. At risk for observer bias.
 
Snowball Sampling-
Recruit partic­ipants via other partic­ipants. At risk for sampling bias.
 
Quota Sampling-
Non-random selection of a predet­ermined number or proportion of units.
 
 

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  • Published: 25th February, 2024
  • Last Updated: 26th February, 2024

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