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Project work – Basic Concepts Snehlata Jaswal HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS What is research? Research is a systematic investigation of phenomena and their relationships Kerlinger (1986): Scientific research is a systematic, controlled, empirical, and critical investigation of natural phenomena guided by theory and hypotheses about the presumed relations among such phenomena. HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Process of research Gap in Gap in knowledge knowledge: Framing the Reduced??? problem Hypothesis Review of testing and literature statistical and framing inference hypotheses Designing the study and collecting data HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Types of Research Observational No control of variables is intended Correlational Control possible only through selection of sample No manipulation of variables possible Experimental Manipulation of independent variable(s) to study the effect on dependent variable Separate group design Single group design HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Hypothesis Every study – whether correlational or experimental – implies a problem to be studied by the researcher. The probable answer or solution to the problem is known as the hypothesis. A hypothesis has two essential qualities: It is precisely stated so that it is testable. It is not wild or improbable. It has a basis in previous research or theories. HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Types of hypotheses Null hypothesis Alternate hypothesis One tailed (directional) Two tailed (not specifying a direction) HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Levels of significance Level of significance refers to the arbitrarily decided probability of error accepted by a science. The probability of error tolerated in any prediction varies from one science to another. Since greater control and precise measurements are possible in physical sciences such as physics and chemistry, they test hypotheses at levels of significance as low as p < .001 or p < .002. Social sciences such as economics or sociology accept levels of significance as high as p < .10. Psychologists generally test hypotheses at either p < .05 or p < .01 (these may also be expressed as respectively = .05 and = .01) HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Errors in statistical decision making Two types of error are always present in statistical decision-making Type I: Rejecting the null hypothesis H0 when in fact it is true. Type II: Accepting the null hypothesis H0 when in fact it is false. Decision Decision Reject H0 Accept H0 H0 true Type I error Correct p= decision H0 false Correct Type II error decision p= HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Errors in statistical decision making The probability of making a Type I error is directly indicated by . We are less likely to make an error of Type I if we have a smaller value of . The trouble is that as we decrease , we automatically increase – the chances of making Type II error – accepting the null hypothesis when it is actually false. Thus the researcher has to decide which of the errors is more acceptable. Since scientists abhor the Type I error more than the Type II error, they generally choose as small as .05 or .01. Type I error implies rejecting the null and accepting the alternate hypothesis to be true. Thus a wrong idea, hypothesis or theory may be accepted. The extremely cautious researchers prefer to make a Type II error. By accepting the null hypothesis when it is actually false, they leave the problem unsolved – as it existed before the statistical test. Thus they do not add any erroneous ideas / facts to the general body of knowledge that exists in the science. HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Errors in statistical decision making Actually, the choice of should depend on the nature of the problem, and practical as well as theoretical considerations. For example, if a researcher is testing the effects of a new medicine, which may be harmful if taken in an overdose, a much smaller alpha is demanded. On the other hand, if a researcher tries to devise a test for identifying people at risk for committing suicide, such a test is useful, even if it makes very few correct predictions. A higher probability of error such as p < .10 may be quite acceptable in such cases. Thus, hypothesis testing is more a process of logical reasoning than statistical reasoning. HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Measurement Measurement implies assigning numerical values to different qualities or quantities of a variable. So, measurement may be qualitative or quantitative. Levels of measurement (Stevens, 1951) Nominal scale Ordinal scale Interval scale Ratio scale Most psychological variables are on an interval scale. Non-parametric vs. parametric scales HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Scales of measurement Scale Properties Possible statistical operations Nominal Identity Frequency Percentage Proportion Mode Coefficient of contingency Ordinal Identity All above; Magnitude Median Rank order correlation Interval Identity Almost all statistical operations possible; Magnitude except coefficient of variation Equal intervals Ratio Identity All statistical operations possible Magnitude Equal intervals True zero Statistics The term statistics has at least three different connotations. It refers to: – a body of knowledge; a subject of study – techniques and methods of treating data – data obtained from samples Every researcher is interested in a population, but for practical purposes we study only a sample of the population. Measures that describe a population are parameters. Measures that describe a sample are called statistics. Parameters are fixed and true but hypothetical values. Statistics vary from sample to sample and are thus prone to error. Yet they are real values. HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Techniques and methods of treating data Descriptive statistics: Describe a sample. Necessary for inferential statistics, but do not allow any conclusions/ inferences/ derivation of general principles and laws. Inferential statistics: Help to draw conclusions from the data. Predict parameters from statistics, and also specify the probability of error associated with the prediction(s). HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Descriptive statistics Measures of central tendency – Mean – Median – Mode Measures of variability – Range – Average deviation – Quartile deviation – Standard deviation Correlation HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Inferential statistics Correlation – Product moment – Rank order t – ratio – Independent groups – Repeated measures ANOVA – One way – Two way – Multi-factorial ANOVA – ANCOVA FACTOR ANALYSES REGRESSION ANALYSES HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS Thank you HUL 204 LEADERSHIP, COMMUNICATION, AND DECISION MAKING IN ORGANIZATIONS
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