QUANTITATIVE RESEARCH METHODS Assoc. Prof. Nongluk Chintanadilok, R.N., D.N.S.

Quantitative Research Methods Measurement –Populations and Sampling –Random Assignment –Generalizability

Measurement – Sampling Specify your population of concern Sampling –Selecting respondents from population of concern –Random sampling –Systematic selection –Stratified sampling –Convenience sampling –Snowball sampling

Sampling Non-response bias –Be persistent –Offer incentives and rewards –Make it look Volunteer bias –Some people volunteer reliably more than others for a variety of tasks

Random assignment Different from random sampling Mostly used for experiments or quasi-experiments Protects against unsuspected sources of bias

Generalizability How do you know that what you found in your research study is, in fact, a general trend? Does A really, always cause B? If A happens, is B really as likely to happen as you claim? Always? Under certain conditions?

Experiments & Quasi-experiments - Experiment An operation or procedure carried out under controlled conditions to discover an unknown effect, to test or establish a hypothesis. Experiments explore the effects of things that can be MANIPULATED

An Experiment is A controlled empirical test of a hypothesis. Hypotheses include: –A causes B –A is bigger, faster, better than B –A changes more than B when we do X Two requirements: –Independent variable that can be manipulated –Dependent variable that can be measured

Quantitative Research Methods Time –Cross-sectional studies & single experiments –Longitudinal studies & repeated measures

Quantitative Research Methods Method –Experiments & Quasi-experiments –Behavioral Measures –Questionnaires & Surveys –Social Network Analysis –Meta-Analysis

Experiments in Research Comparing one design or process to another Deciding on the importance of a particular feature in a user interface Evaluating a technology or a social intervention in a controlled environment Finding out what really causes an effect Finding out if an effect really exists

Types of Experiments Randomized – units/participants assigned to receive treatment or alternative condition randomly Quasi – no random assignment

Questionnaires & Surveys Self-report measures –Questionnaires & surveys –Interviews –Diaries Types –Structured –Open-ended

Questionnaires & Surveys Advantages –Sample large populations (cheap on materials & effort) –Efficiently ask a lot of questions Disadvantages –Self-report is fallible –Response biases are unavoidable

Response biases Relying on people’s memory of events & behaviors –Emotional states can “prime” memory –Recency effects –Routines are deceiving No social desirability Variety of the direction of response alternativesVariety of the direction of response alternatives

General Survey Biases Sampling – are respondents representative of population of interest? How were they selected? Coverage – do all persons in the population have an equal change of getting selected? Measurement – question wording & ordering can obstruct interpretation Non-response – people who respond differ from those that do not

Ethical context in conducting research Reliability assessments Validity assessments Statistical analysis of data Interpretation of results Writing quantitative research reportWriting quantitative research report

Structural Equation Modeling

What is SEM? SEM – Structural Equation Modeling Also Known As –CSA – Covariance Structure Analysis (LISREL: LInear Structural RELationships) –Causal Models –Simultaneous Equations –Path Analysis –Confirmatory Factor Analysis –Latent Variable Modeling

SEM in a nutshell Combination of factor analysis and regression –Tests relationships variables –Specify models that explain data with few parameters –Flexible - Works with continuous and discrete variables –Significance testing and model fit

What is structural equation modeling? A framework for using statistical methods to ask complex questions of data. Macrohabitat η c1 Microhabitat η c2 Diversity η e3 Litter η e2 Herbaceous η e1 lake, x 1 impound, x 2 swale, x 3 vhit1, y 1 vhit2, y 2 herbl, y 3 herbc, y 4 wlitr, y 5 litrd, y 6 litrc, y 7 rich, y ns

The Origin of Structural Equation Modeling Sewell Wright st paper in: 1920

The Wright Idea Y 1 = α 1 + β 1 X + ε 1i Y 2 = α 2 + β 2 X + β 3 Y 1 + ε 2i XY1Y1 ε 1i Y2Y2 ε 2i

The LISREL Synthesis Karl Jöreskog 1934 Key Synthesis paper- 1973

Structural equation modeling (SEM), as a concept, is a combination of statistical techniques such as exploratory factor analysis and multiple regression. The purpose of SEM is to examine a set of relationships between one or more Independent Variables (IV) and one or more Dependent Variables (DV).

Both IV’s and DV’s can be continuous. Independent variables are usually considered either predictor or causal variables because they predict or cause the dependent variables (the response or outcome variables).

Structural equation modeling is also known as ‘causal modeling’ or ‘analysis of covariance structures’. Path analysis and confirmatory factor analysis (CFA) are special types of SEM.

Genetics S. Wright (1921): “Prior knowledge of the causal relations is assumed as prerequisite … [in linear structural modeling]”. y =  x + 

1. It is a “model-oriented” method, not a null-hypothesis-oriented method. Some Properties of SEM 2. Highly flexible modeling toolbox. 3. Can be applied in either confirmatory (testing) or exploratory (model building) mode. 4. Variety of estimation approaches can be used.

1. Seeks to model uncertainty rather than probabilities. Approach 2. Philosophically well suited for supporting decision making. 3. Popularity partly based on new algorithms that create great flexibility in modeling. 4. It's in determinant solution procedure, contributes to some uncertainty about results for more complex models(?)