Research Design Location And Research Time Psychology Essay

bayion traffic pattern Location And Re attend Time psychology EssayThis chapter exit present the regularityological woo and look for proficiency apply in this thesis along with how the info will be gathitherd to find the answer or solutions of the search questions and problems, such as explore design, theoretical perspective, information compendium method, data summary method as well as the validity and trustyness of the data.Research Design, Location, and Research TimeThis research was plotted and designed to obtain answer to research questions. Research design is the example or plan for study, employ as a single guide to collect and analyze the data. It is the blueprint that is followed to substitute a study. gibe to Sekaran and Bougie (2009, p. 24), a research method or approach/design gives details on the most suit satisfactory methods of investigating the temperament of the research, legal documents, the sampling plan and the type of data to be use of go ods and services of goods and servicesd. Further to a greater extent than, Sekaran and Bougie (2009, p. 24) also cut through that a research method or design forms the frame snip of the integral research process.Therefore, if it is a good method or design, it will and thence ensure that the schooling obtained is important to the investigators problem and those objectives and economic procedures in collect it atomic number 18 only within limits. It simply is a systematic quest for undiscovered truth. In pursuit of this undiscovered truth, a investigator need facts, published documents from basal sources and alternative sources.This research was employ a traverse sectional study which the data atomic number 18 gathered just erst eon (Sekaran Bougie, 2009, p. 119).Research location was do at six locations Central capital of Ind nonpareilsia, nitrogen Jakarta, South Jakarta, westernmost Jakarta, East Jakarta and Tangerang city to dwelling house nodes of PT. Perus ahaan Listrik Negara between March to July 2012, with the distribution of questionnaires within June 2012.Research exemplarThe research framework of this thesis is shown in Figure 3.1 below. The starting line research step is to fixate the research problem, followed by research objective. The third step is a lit critique, followed by data collection, data analysis, hypothesis test, and finally generate goal and recommendation.Source precedentFigure 3. Research FrameworkResearch Questions and HypothesesIn this study, detective would like to answer the questions and analyze the hypotheses belowRQ1 How do heathen factors, social factors, personal factors, psychological factors, and PLN services influence customer ending in utilise electrical energy at peak dispatch hours?H1 From those factors, all factors argon positively influence customer decision in exploitation electricity at peak load hours.RQ2 How is the correlation between the decisions in development electricity during peak load hours with the syndicate customers demeanor towards economic system electricity?H2 There is a correlation between the decisions in using electricity during peak load hours with the household customers behavior towards conservation electricity.RQ3 How do pro-social intensions, motivations, penetration to in stageion, and knowledge influence customer behavior in deliver electricity?H3 From those factors, all factors are signifi loafertly influence customer behavior in conservation electricity.RQ4 How is the correlation between customer awareness of consequences, breathing in of responsibility, personal norms towards their pro-social intensions in saving(a) electricity?H4 There is STRONG correlation between customer awareness of consequences, aspiration of responsibility, personal norms towards their pro-social intensions in saving electricity?RQ5 What interventions or instruments are most believably to affect households saving electricity behavior?H5 The i ntervention or instrument most likely to affect households saving electricity behavior is by increasing their motivation, knowledge, and access to information about saving electricity programs.RQ6 From the disseverification of household segments, which class is the most involved in saving electricity?H6 From the classification of household segments, class R12.200VA is the most involved in saving electricity.Conceptual FrameworkSource Author (Adapted from Kotler, 1999, and deGroot Steg, 2009)Figure 3. Conceptual FrameworkResearch selective informationType and Source of selective informationThere are two types of data that researches collected depending on the purpose. The data of research consists of both primary and secondary data. The primary data are the first-hand information acquired by the researcher on the variables under study while the secondary data refer to information gathered from sources that already exist which may come from archives or organizational files (Sekaran Bougie, 2009, p. 180).This research was utilise both primary and secondary data. The primary data sources were obtained through the survey method by distributing structured questionnaires to household customers of PT. PLN Jakarta Raya and Tangerang statistical distribution.The secondary data were obtained from the comp nigh(prenominal) internal data such as figures in customer-base segmentation, and existing data in books, journals, publications, reports, and websites.Data aggregation mannerThe data collection for primary and secondary data is done through the following methodsLiterature Review.According to Sekaran and Bougie (2009, p. 38), a literature review is a step-by-step process that involves the identification of published and unpublished work from secondary data sources on the topic of interest, the evaluation of this work in relation to the problem.In this research, the literature review is done by search and study books, reports, journals, research reports, internet website related to electricity business and customer behavior. The information related to the comp whatsoever is obtained through the companys website and published reports.Questionnaire.Sekaran and Bougie (2009, p. 197) de pretty questionnaire is a pre-formulated recorded serial of questions to which the respondents giving their answers usually within rather well defined alternatives. For this research, the questionnaire is formulated and distributed to the respondents in two methods. First method used is by distributing the questionnaire to respondents through email. The second method is the direct questionnaire to respondents, by petition them to give their answer on the questionnaire paper provided.Questionnaire DesignQuestionnaires are in force(p) data collection method when the researcher knows the information to gather and how to get hold the variables of interest (Sekaran Bougie, 2009, p. 197). Questionnaire survey is formulated to answer the research questions. It i s a tool that may be cheeryly distributed personally or electronically to respondent.According to destroy and Bush (2006, p. 300), there are six key functions of a questionnaireTo translate the research objectives into specific questions.To measuring rodize the questions and the response categories to let every(prenominal) incisionicipant responds to monovular stimuli.To reinforce cooperation and motivates respondents to respond.To serve as permanent records of the research.To hotfoot up the process of data analysis, depending on the type of questionnaire used by the researcher.To contain the data which may be addressed for reliability and validity.The first part consists of the demographic attributes questions such as gender, age, education, occupation, annual income, and the second part consists of questions analyzing customer behavior in terms of cultural, social, personal, and psychological factors that base on Griffin and Eberts exemplification (2006, p. 283).Part two in the questionnaires use Likerts casing which enables the respondents to give level of the attributes stated in the questions.A Likerts scale was used in the research, in which respondents were asked to indicate their level of agreement or unlikeness on a systematic agree-disagree scale for each(prenominal) of a series of questions (Burns Bush, 2006, p. 281). Each question in the questionnaire on this part is ranged from 1 to 5, where 1 = Strongly Disagree 2 = Disagree 3 = Neutral or Not Applicable 4 = Agree 5 = Strong Agree.The questionnaire format for this research is shown in Figure 3.3 below.Source AuthorFigure 3. Questionnaire Design for This Research tabularise 3.1 shows the distribution of items in company to monetary standard the variables in the questionnaires.Table 3. Variable, Scale of Data and course of study of QuestionsPart 1 respondent ProfileNo.VariablesScale of DataCategory of Questions1.Gender titularMaleFemale2.AgeInterval20-30 long period old30-40 old age old40-50 years old50-60 years old 60 years old3.OccupationNominalGovernment employeePrivate company employee free-lance(a)ProfessionalsRetiredOthers4.Number of Family MembersIntervalSmall 4 membersMedium 5-6 membersBig 7 members5.Educational backgroundOrdinalBasic / Junior High nurtureSenior High SchoolCollege DegreeBachelor Degree tame DegreePhD6.Income per monthRatio 2 million rupiahs2 to 5 million rupiahs5 to 10 million rupiahs 10 million rupiahs7. categorisation electricityNominal900 VA1.300 VA2.200 VA8.DomicileAreaCentral JakartaWest JakartaEast JakartaSouth JakartaNorth JakartaTangerang CityPart 2 Exploratory Questions (Data are in Likert scale) customer Decision in using electricity at peak load hoursNo.VariablesCategory of QuestionsA.Cultural FactorsRegularly using electricity at peak load hours (between 17.00 to 20.00).Household activities were dominated by using electrical equipment.Regularly using electrical equipment in day time.B.Social FactorsThe faculty o f installed electricity is in accordance with the requirement.Able to turn out if electricity tariff is go up.Electricity tariffs are still cheap.Its normal to reduce electricity subsidized and to increase tariffs when oil prices are up.C.Personal FactorsAble to pay electricity and can afford the electricity bills.It is necessary to increase susceptibility because the need of electricity will increase.Electricity bills are relatively slender compare to the total expenditure.D.Psychological FactorWhen using electricity at peak time, we will pay more expensive.Feeling guilty when using electricity at peak time.Feeling happy if every inhabit are bright.E.PLN ServicesPower failure was rarely, so it is convenient to use it, especially at peak time.Recording of electricity is on time and the bill is in accordance with the use.Since electricity is stable, we are not busy to use it at peak time.Part 2 Exploratory Questions (Data are in Likert scale)Customer behavior towards saving el ectricityNo.VariablesCategory of QuestionsA.Access to InformationGet information about saving electricity from friends, family, neighbor, PLN, or community leaders.Get information about saving electricity from television, radio, magazine/newspaper, and internet.Often receiving information about saving electricity.B.KnowledgeKnows electricity-saving equipment. galvanic equipment will be more efficient when turned off than in standby. utilize electrical equipment at its maximum capacity will chance upon more energy.C.MotivationBeing motivated to prioritize electricity saving behavior.Being motivated to respect environment.D.Pro-social IntensionsThere are negative consequences of any actions that do not respect the environment.Feel responsible for environmental damage.Having a moral obligation towards energy efficiency and environmental protection.Questionnaire FormatIn this research, the questionnaires were prepared in printed and online formats using Indonesian language, because som e of the respondents were not able to read and speak in English language.During the pre-test stage, the questionnaire was distributed only through email to 30 respondents to find out the validity and reliability of the data or questions in the questionnaires.At the post-test stage, the revised questionnaire was printed and distributed door to door. Due to time limitation, researcher employed a strategy by setting up a team consist of 6 (six) members to meet the respondents in 6 (six) divergent locations (domiciles).The revised questionnaire was also distributed by email. By using email, it was very convenient in terms of shortening the time spent to send the questionnaire and receiving the responses from the respondents. However, there were difficulties because the respondents were depended on a computer and internet service.Survey essay distribution MethodAccording to Sekaran and Bougie (2009, pp. 262-263), a have is a subset of the people. It comprises some members selected fr om it. A standard is thus a subgroup of the community, which acquaints the whole group of people, actions, or things of interest that the researcher wants to investigate.According to Burns and Bush (2006, pp. 372-374), the coat of the strain affect the render accuracy of results, thus render accuracy refers to how close a hit-or-miss samples statistic is to the populations economic value it represents. The most correct method of determining sample sizing is confidence interval approach.In order to mastermind the proper sample size of the survey, Burns and Bush (2006, p. 366) said, there are three items call forAmount of variability of populationDesired accuracy, andRequired confidence level. take MethodologyIn this research, the population is the total number of customers of PT. PLN Jakarta Raya and Tangerang Distribution from the Household segment, which according to the statistics are 3.330.815 number of customers.For this amount of population, the sample size may be cal culated using the formula recommended by Burns and Bush (2006, p. 372)Where n = the sample sizez = standard fracture associated with the chosen level of confidence (1.96)p = estimated percentage in the populationq = 100- pe = acceptable sample erroneous beliefSample population sample size = population sample size xIn this research, researcher chose to use a probability of 90% with a 95% level of significance uniform to a z value of 1.96 and sample error 4%.The sampling calculation was determined by using a software application, PHStat2. PHStat2 is a Windows- ground software that assists students and professionals in learning the statistic plans while using Microsoft Excel.Table 3. Sampling Size DeterminationData view of True Proportion0.9Sampling Error0.04Confidence Level95%Intermediate CalculationsZ Value-1.95996398 metrical Sample Size216.0820587 firmness of purposeSample Size Needed217Finite PopulationsPopulation Size3.330.815Calculated Sample Size216.0681064Sample Size Neede d217Source Data on FileBased on the calculation in Table 3.2, the nominal number of sample size needed is 217 samples however in this research the number of samples is added to some other 10 percent in order to produce greater accuracy. Therefore, this research will be used 240 samples.This research will use a cross sectional-study in which the data are gathered at once in order to answer the research questions (Sekaran Bougie, 2009, p. 119).Sample With luckal TechniquesThe populations in this research were household customers of PT. PLN Jakarta Raya Tangerang Distribution. Sampling technique was done by using stratified random sampling, involves a process of stratification or segregation, followed by random selection of subjects from each bed. The population is divided into stratum, and then sampling conducted in each stratum (Sekaran Bougie, 2009, p. 272).In this research, customers who become household population were stratified based on electrical power and is divided int o three groups, namely 900VA, 1.300VA and 2.200VA. It is based on the Regulation of the President of Republic Indonesia No. 8, 2011, p. 9 (see Appendix A2).The sample selection techniques are described in Figure 3.4.Source AuthorFigure 3. Sample Withdrawal TechniquesData AnalysisAfter data are obtained through questionnaires, the next step is to analyze them to test the research hypothesis. To ensure that the data obtained are reasonably good and ready for use for statistical analysis, Sekaran and Bougie (2009, pp. 306-330) recommend followingsGetting the data to be ready for analysisCoding and data entryCoding the responsesData entryediting dataData transformationGetting a feel for the dataRelationship between variables correlativitys sieveing goodness of dataReliability hardshipTesting the hypothesisHypothesis testing and data analysis will be conducted using appropriate statistical method and based on sample data associated with software such as PHStat2, SPSS transformation 20, and AMOS version 20.descriptive AnalysisDescriptive analysis such as the mean, mode, standard deviation, and range are used by researcher to describe the sample data matrix in such a way as to portray the typical respondent and to reveal the general pattern of responses. Descriptive measures are regarded as the steps undertaken by the researcher earlier in the process of analysis and become foundations for subsequent or more complicated analysis (Burns Bush, 2006, p. 424).Descriptive statistics were used to portray the main characteristics of a collection of data in quantitative terms and distinguished from inductive statistics in that they intend to quantitatively review a data set, instead of be used to support reports regarding the population that the data are supposed to represent. hitherto when a data analysis obtains its major conclusions using inductive statistical analysis, the descriptive statistics are usually presented alongside the formal analyses to show the earsho t an overall perception of how data being examined.Validity and Reliability TestA good quality criterion instrument is needed in order to obtain precise data of this research. The ideal instrument has to be reliable and valid. The researcher must address both validity and reliability of the measures in assessing the full stop of measurement error present in any measures.Any measure designed or adapted for use in any research should both be reliable and valid. A reliable measure is one in which a respondent acts in response to the same or a very similar manner to an identical or nearly identical question (Burns Bush, 2006, p. 290). The reliability of a measure is a test of how consistently a measuring instrument measures whatever concept it is measuring.In testing the reliability of the questionnaire, the test-retest reliability test was used which measures the correlation between the same respondents obtained at the two different measure (Sekaran, 2010, p. 162).To achieve reliab ility of a measure, the researcher was using SPSS software with Cronbachs alpha as the measurement. Cronbachs Alpha is a reliability coefficient that determines how well specific items of the measurement tools are positively correlated to one another. Cronbachs Alpha is computed using the comely intercor transaction among the items measuring the concepts. If Cronbachs Alpha is greater than 0.70, it means that the data are more consistent and reliable. The closer the alpha value to 1 indicates the data are most consistent and reliable. A high quality reliable instrument can be used as a guide to draw a conclusion and making decisions (Sekaran Bougie, 2009, pp. 324-325).Validity is a test of how fine a developed instrument to measure the particular concept it is planned to measure. In the other words, validity is related to measurement with the right concept and reliability with stability and consistency of measurement (Sekaran Bougie, 2009, pp. 158-160).Correlation AnalysisCorrela tion analysis is an analysis done to trace the mutual influence of variables on one another. A correlation coefficient that indicates the strength and direction of the human blood can be computed by applying a formula. There could be a perfective positive correlation between two variables, which is equal by 1.0 (plus 1), or a perfect negative correlation which would be -1.0 (minus 1) (Sekaran Bougie, 2009, p. 322).The formula to calculate the coefficient of correlation isSource Burns Bush, 2005Wherer = coefficient correlation n = samplesxi = variable X x = mean Xyi = variable Y y = mean YTable 3.3 presents the rules of thumb in rendition the correlation coefficient values.Table 3. Rules of thumb of degree of correlationCoefficient Range stance of Association0.81 to 1.00Strong0.61 to 0.80Moderate0.41 to 0.60Weak0.21 to 0.40Very Weak0.01 to 0.20noneSource Burns Bush, 2005Structural Equation simulationing (SEM)Structural equating manikin (SEM) is a statistical approach for te sting and estimating causal relationship using a combination of statistical data and qualitative causal assumptions. Typically, this theory represents causal processes that produce examinations on multiple variables.The term structural equation rideing expresses two important features of the procedurecausal processes, represented by a sequences of structural (i.e. regression) equations, andthese structural relationships can be displayed pictorially to allow a clearer conceptualization of the theory.Then, the hypothesized framework can be examined statistically in a simultaneous analysis of the entire variables to conclude the degree of its consistency to the data. If goodness-of-fit is adequate, the model argues for the credibility of hypothesized relations among variables. If it is inadequate, the reasonability of those relations is rejected (Byrne, 2010, p. 3).statistical models provide an efficient and convenient way of describing the possible structure rudimentary a set of spy variables. Expressed either plotmatically or mathematically via a set of equations, such models explain how the observed and latent variables are related to one another.Typically, a researcher postulates a statistical model based on his or her knowledge of the related theory, on empirical research in the area of study, or on some combination of both. Once the model is specified, the researcher then tests its plausibility based on sample data that comprise all observed variables in the model. The primary task in this model-testing procedure is to determine the goodness-of-fit between the hypothesized model and the sample data. As such, the researcher imposes the structure of the hypothesized model of the sample data, and then tests how well the observed data fit this restricted structure. Because it is highly unconvincing that a perfect fit will exist between the observed data and the hypothesized model, there will necessarily be a derivative between the two this differential is termed the residual.The model-fitting process can therefore be summarized as followsData = Model + ResidualWhereData represent remove measurements related to the observed variables as derived from persons comprising the sample.Model represents the hypothesized structure linking the observed variables to the latent variables and, in some models, linking particular latent variables to one another.Residual represents the discrimination between the hypothesized model and the observed data (Byrne, 2010, p. 7).Structural equation models are schematically portrayed using particular configurations of four geometric symbolsa circle (or ellipse),a square (or rectangle),a single-headed arrow, anda double-headed arrow.By convention, circles (or ellipses ) represent unseen latent factors, squares (or rectangles ) represent observed variables, single-headed arrows () represent the impact of one variable on another, and double-headed arrows () represent covariance or correlations between pai rs of variables (Byrne, 2010, p. 9).In structure a model of a particular structure in this research, the researcher uses these symbols within the framework of four basic configurations, each of which represents an important office in the analytic process.These configurations, each accompanied by a draft description, are as follows data track coefficient for regression of an observed variable onto an unobserved latent variable (or factor)Path coefficient for regression of one factor onto another factorMeasurement error associated with an observed variableResidual error in the prediction of an unobserved factorThe Path Diagram stately representations of models are termed path diagrams because they provide a visual portrayal of relations which are assumed to hold among the variables under study. Essentially, a path diagram depicting a particular SEM model is actually the graphical equivalent of its mathematical representation whereby a set of equations relates dependent variables to their explanatory variables (Byrne, 2010, p. 10).Using path diagram as a structural equation modeling tool, the pattern of causal relationship can be detected. Causal relationship describes interrelations among a set of latent (unobserved) variables and a set of observed variables.Path diagram is a relationship structure between the exogenous and endogenetic variables. The independent (X) variables are called exogenous variables. The dependent (Y) variables are called endogenous variables.Model MeasurementAccording to Hair et.al (2010) measurement model validity depends on establishing acceptable levels of goodness of fit (GOF) for the measurement which indicates how well specify model reproduces the observed covariance matrices, small the difference between covariance matrices estimate with the observe covariance matrices, more fit the model. (Hair et. al, 2010, p.639).The GOF value contains several parameters to be considered by the researcher as stated by Hair et.al (2010, p.6 40-650), this thesis confirm the overall model fit the parameter will used the followingChi-square (CMIN) or minimum discrepancy (), it is to test whether there is the different covariance matrices estimate within the covariance matrices observe, smaller () shown the different of both not significant and the model more fit.df (degree of freedom), more positive (=0) of the df which shown with minimum was achieved the process of the estimate could be done.CMIN/DF.CMIN represents the minimum value of the discrepancy while DF is the degree of freedom. According to Wijaya (2009, p. 45), the model could be authorized if the CMIN/DF is 2.00.RMR (root mean square residual), this is called badness of fit whether the value is less than 0.1 than it is conk out because deference between sample and the estimate is smaller (Hair et.al 2010, p.642)GFI and AGFI (Goodness fit index and Adjusted Goodness fit index), GFI and AGFI value between 0 to 1, more closed to 1 more fit the model (Hair et.a l 2010, p.643)