Observation Methods and Experimental Designs in Research: A Comprehensive Guide, Study Guides, Projects, Research of Literature

Download Observation Methods and Experimental Designs in Research: A Comprehensive Guide and more Study Guides, Projects, Research Literature in PDF only on Docsity! Lecture Methods of Collecting Primary Data A Classification of Marketing Research Data Survey Data Observational and Other Data Experimental Data Fig. 5.1 Qualitative Data Quantitative Data Descriptive Causal Research Data Secondary Data Primary Data Observation Methods Disguised Versus Undisguised Observation  In disguised observation, the respondents are unaware that they are being observed. Disguise may be accomplished by using one-way mirrors, hidden cameras, or inconspicuous mechanical devices. Observers may be disguised as (secret) shoppers or sales clerks.  In undisguised observation, the respondents are aware that they are under observation. Observation Methods Natural Versus Contrived Observation  Natural observation involves observing behavior as it takes places in the environment. For example, one could observe the behavior of respondents eating fast food at Burger King.  In contrived observation, respondents' behavior is observed in an artificial environment, such as a test kitchen or a simulated store A Classification of Observation Methods Observation Methods Personal Observation Mechanical Observation Trace Analysis Content Analysis Audit Fig. 6.3 Classifying Observation Methods Experimentation — Causal research design  Experimentation: Determine the effects of various factors on a response variable by varying these factors in a controlled way, and often in controlled conditions. A very reliable and efficient means of collecting data and verifying or refuting theories.  Example: how exam scores are affected by whether students attended lectures or worked from a book, the gender of the student, and if the student had a job Experimentation  Full factorial experiment  Is lecturing or book-based study more useful? Are male students better than female students or vice versa? What about the effect of working?  The concepts of causality and control  Causality: ????????  Control: ???????? Concept of Causality A statement such as "X causes Y" will have the following meaning to an ordinary person and to a scientist. ____________________________________________________ Ordinary Meaning Scientific Meaning ____________________________________________________ X is the only cause of Y. X is only one of a number of possible causes of Y. X must always lead to Y The occurrence of X makes the (X is a deterministic occurrence of Y more probable cause of Y). (X is a probabilistic cause of Y). It is possible to prove We can never prove that X is a that X is a cause of Y. cause of Y. At best, we can infer that X is a cause of Y. Purchase of Fashion Clothing By Income and Education Low Income Purchase High Low High LowEd uc at io n 200 (100%) 300 (100%) 300 200 122 (61%) 171 (57%) 78 (39%) 129 (43%) High Income Purchase High High Low Low 241 (80%) 151 (76%) 59 (20%) 49 (24%) Ed uc at io n Definitions and Concepts  Independent variables are variables or alternatives that are manipulated and whose effects are measured and compared, e.g., price levels.  Test units are individuals, organizations, or other entities whose response to the independent variables or treatments is being examined, e.g., consumers or stores.  Dependent variables are the variables which measure the effect of the independent variables on the test units, e.g., sales, profits, and market shares.  Extraneous variables are all variables other than the independent variables that affect the response of the test units, e.g., store size, store location, and competitive effort. Main effects and interactions —— Moming —— Evening (b) Significant time of day effect, (a) No significant effects no other effects —_——_—— a wa | | | Light Heavy Light Heavy Intensity of exercise Intensity of exercise (d) Significant intensity of exercise (c)_ Significant intensity of exercise and time of day effects; no effect; no other effect interaction effect a a Ll 4 | | Light Heavy Light Heavy Intensity of exercise Intensity of exercise () Significant time of day and (@) Significant interaction effect; interaction effects; no other no other effects effects $ z a a ——— i J i a Light Heavy Light Heavy Intensity of exercise Intensity of exercise Extraneous Variables  History refers to specific events that are external to the experiment but occur at the same time as the experiment.  Maturation (MA) refers to changes in the test units themselves that occur with the passage of time.  Testing effects are caused by the process of experimentation. Typically, these are the effects on the experiment of taking a measure on the dependent variable before and after the presentation of the treatment.  The main testing effect (MT) occurs when a prior observation affects a latter observation. Extraneous Variables  In the interactive testing effect (IT), a prior measurement affects the test unit's response to the independent variable.  Instrumentation (I) refers to changes in the measuring instrument, in the observers, or in the scores themselves.  Statistical regression effects (SR) occur when test units with extreme scores move closer to the average score during the course of the experiment.  Selection bias (SB) refers to the improper assignment of test units to treatment conditions.  Mortality (MO) refers to the loss of test units while the experiment is in progress. Controlling Extraneous Variables  Randomization refers to the random assignment of test units to experimental groups by using random numbers. Treatment conditions are also randomly assigned to experimental groups.  Matching involves comparing test units on a set of key background variables before assigning them to the treatment conditions.  Statistical control involves measuring the extraneous variables and adjusting for their effects through statistical analysis.  Design control involves the use of experiments designed to control specific extraneous variables. A Classification of Experimental Designs Pre-experimental One-Shot Case Study One Group Pretest-Posttest Static Group True Experimental Pretest-Posttest Control Group Posttest: Only Control Group Solomon Four- Group Quasi Experimental Time Series Multiple Time Series Statistical Randomized Blocks Latin Square Factorial Design Figure 7.1 Experimental Designs One-Shot Case Study X 01  A single group of test units is exposed to a treatment X.  A single measurement on the dependent variable is taken (01).  There is no random assignment of test units.  The one-shot case study is more appropriate for exploratory than for conclusive research. One-Group Pretest-Posttest Design 01 X 02  A group of test units is measured twice.  There is no control group.  The treatment effect is computed as 02 – 01.  The validity of this conclusion is questionable since extraneous variables are largely uncontrolled. Posttest-Only Control Group Design EG : R X 01 CG : R 02  The treatment effect is obtained by: TE = 01 - 02  Except for pre-measurement, the implementation of this design is very similar to that of the pretest-posttest control group design. Quasi-Experimental Designs: Time Series Design 01 02 03 04 05 X 06 07 08 09 010  There is no randomization of test units to treatments.  The timing of treatment presentation, as well as which test units are exposed to the treatment, may not be within the researcher's control. Multiple Time Series Design EG : 01 02 03 04 05 X 06 07 08 09 010 CG : 01 02 03 04 05 06 07 08 09 010  If the control group is carefully selected, this design can be an improvement over the simple time series experiment.  Can test the treatment effect twice: against the pretreatment measurements in the experimental group and against the control group. Randomized Block Design Treatment Groups Block Store Commercial Commercial Commercial Number Patronage A B C 1 Heavy A B C 2 Medium A B C 3 Low A B C 4 None A B C Table 7.4 Latin Square Design  Allows the researcher to statistically control two noninteracting external variables as well as to manipulate the independent variable.  Each external or blocking variable is divided into an equal number of blocks, or levels.  The independent variable is also divided into the same number of levels.  A Latin square is conceptualized as a table (see Table 7.5), with the rows and columns representing the blocks in the two external variables.  The levels of the independent variable are assigned to the cells in the table.  The assignment rule is that each level of the independent variable should appear only once in each row and each column, as shown in Table 7.5. Latin Square Design Table 7.5 Interest in the Store Store Patronage High Medium Low Heavy B A C Medium C B A Low and none A C B Laboratory Versus Field Experiments Table 7.7 Factor Laboratory Field Environment Artificial Realistic Control High Low Reactive Error High Low Demand Artifacts High Low Internal Validity High Low External Validity Low High Time Short Long Number of Units Small Large Ease of Implementation High Low Cost Low High Triangulation (Multi-method, Validation, Robustness)  Triangulation: collect data by different means to obtain convergence on the truth To search both for accuracy of the data and alternate explanations.  Data source triangulation The analyst asks whether or not what they are reporting is likely to be constant at other times or circumstances.  Theory triangulation The analyst asks whether or not what they are reporting is likely to be constant at other times or circumstances.  Methodological triangulation This involves using a variety of data collection methods to build confidence in the interpretations made so far.  Member triangulation The respondent is asked to review the material for accuracy and to add further comments that might aid description and explanation. Problems and Complexities  Biases and problems: model specification and measurement  Spurious relationship  Confounding  Boundary effects  Complex relationships  Mediation  Moderation  Moderated mediation (with categorical variables)  Mediated moderation  ANCOVA, SEM, and Hayes bootstrap