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The Main Goal Of Statistical Research Psychology Essay

The Main Goal Of Statistical Research Psychology Essay

The present research is a descriptive cum exploratory with an applied basis where the researcher had used the information and facts already available as base in order to analyze, explore and evaluate the problem at hand critically to deduce conclusions.

Descriptive research is also known as Statistical Research. The main goal of Statistical Research is to define the data and characteristics about what is being studied. The notion behind Statistical Research is to study frequencies, averages, and other statistical calculations. Although this research is highly accurate, it does not pleat the causes behind a situation. Descriptive research is mainly undertaken when an investigator wants to gain a better understanding and knowledge of a topic.

Descriptive research explores a existing phenomena. It does not fit precisely into the definition of either quantitative or qualitative research methodologies but instead it utilizes elements of both type of research, often within the same study. The term descriptive research refers to the type of research question, design, and data analysis that is applied to a given topic. Descriptive statistics articulate what is, while inferential statistics try to define cause and effect.

The type of question asked by the researcher ultimately determines the type of approach necessary to complete an accurate assessment of the topic in study. Descriptive studies, primarily concerned with finding out “what is,” might be applied to investigate the various questions: Descriptive research can be either quantitative or qualitative. (Glass & Hopkins, 1984) stated that Descriptive statistics involves gathering the data that describes events and then organizing, tabulating, depicting and describing the data collected. It often uses visual aids such as graphs and charts to aid the reader in understanding the data distribution. Descriptive Statistics are very useful in reducing data to a manageable form and facilitates better and easy understanding of the data; because the human mind cannot extract the full import of a large mass of raw data, descriptive statistics are very important in reducing the data to manageable form. When in-depth, narrative descriptions of small numbers of cases are involved, the research uses description as a tool to organize data into patterns that emerge during analysis. Those patterns aid the mind in comprehending a qualitative study and its implications.

Most of the quantitative researches fall into two areas: studies that describe events and studies aimed at discovering inferences or causal relationships. Generally, descriptive studies are aimed at finding out “what is,” therefore observational and survey methods are recurrently used to collect descriptive data (Borg & Gall, 1999). Descriptive studies report data summary by means of measures of central tendency including the mean, median, mode, deviance from the mean, variation, percentage, and correlation between variables. Survey research commonly includes that type of measurement, but often goes beyond the descriptive statistics in order to draw inferences. For example, Descriptive research is unique in the number of variables employed. Like other types of research, descriptive research can include multiple variables for analysis, yet unlike other methods, it requires only one variable (Hopkins, 2000). Three main purposes of research are to describe, explain, and validate findings. Krathwohl (1993) explains description emerges following a creative exploration, and it serves to organize the findings to fit them with explanations, and then test or validate those explanations Many research studies call for the description of natural or man-made phenomena such as their form, structure, activity, change over time, relation to other phenomena, and so on. The description often illuminates knowledge that we might not otherwise notice or even encounter. Several important scientific discoveries as well as anthropological information about events outside of our common experiences have resulted from making such descriptions. Descriptive studies have an important role in educational research.

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4.1 Descriptive Research

The descriptive function of research is heavily dependent on instrumentation for measurement and observation (Borg & Gall, 1999).

There has been an ongoing debate among researchers about the value of quantitative versus qualitative research, and certain remarks have targeted descriptive research as being less pure than traditional experimental, quantitative designs. Descriptive studies can yield rich data that lead to important recommendations. Descriptive research can be misused by those who do not understand its purpose and limitations. For example, one cannot try to draw conclusions that show cause and effect, because that is beyond the bounds of the statistics employed.

Borg and Gall (1999) classify the outcomes of research into the four categories of description, prediction, improvement, and explanation. Descriptive research describes a natural or man-made educational phenomenon that is of interest to policy makers. The methods of collecting data for descriptive research can be employed singly or in various combinations, depending on the research questions at hand. Descriptive research often calls upon quasi-experimental research design (Koul, 2010). Some of the common data collection methods applied to questions within the realm of descriptive research includes surveys, interviews, observations, and portfolios.

4.2 Effect of Research Design

“A research design is the specification of methods and procedures for acquiring the information needed. It is the overall operational pattern or framework of the project that stipulates what information is to be collected from which source and by what procedure.” (Best & Kahn, 2010)

The type of design chosen for the study has a major impact on the sample size. Descriptive studies need hundreds of subjects to give acceptable confidence intervals (or to ensure statistical significance) for small effects. Experiments generally need a lot less-often one-tenth as many–because it’s easier to see changes within subjects than differences between groups of subjects. In the present study as mentioned survey design has been used.

This chapter describes the design employed, procedure followed, sample selected, tools used and sequence of the events that occur, procedure adopted for data collection and statistical analysis conducted to realize the objectives of the study. Research design is the blue print of the procedure that enables a researcher to test hypothesis by reaching valid conclusions about relationships between independent and dependent variables (Best, 2010). Kerlinger (1974) described “Research design is a plan, structure and model of investigation conceived so as to obtain answers to research questions and control variances”. Thus, design provides a picture of what and how to do the work. In any research project, design provides the investigator a blue print of the research dictates the boundaries of the project and helps in controlling the experimental, extraneous and error variances of the problem under investigation.

It is a planning stage of research which is usually made logically visualizing its practicability. The selection of the research components is done keeping in view the objectives of the research. Research design includes the following components:

Research method or strategy

Sampling design

Choice of research tools

Choice of statistical techniques

4.3 Population

Population means the entire mass of observation which is the parent group from which a sample is to be taken. In the present study, teachers in Government, Self Financed and Societies Owned institutions in Haryana constituted the population. The Population for the present study comprised of teachers in all designation in these three types of institutions.

4.4 Sampling

Sampling is the act, process or technique of selecting a suitable sample or a representative part of a population for the purpose of determining parameters or characteristics of the whole population. Sampling is fundamental to all the statistical techniques and statistical analysis. In fact, it is an indispensable technique of behavioral research. The sample observations provide an estimate of the population characteristics as the study of the total population is impossible and impractical. It means selections of individuals from population in such a way that every individual has the equal chance to be included in the sample. Several methods have been designed to select a representative sample. When dealing with people, it can be defined as a set of respondents (people) selected from a larger population for the purpose of a survey.

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To draw conclusions about populations from samples, inferential statistics must be use by the researcher which enables him to determine a population’s characteristics by directly observing only a portion (or sample) of the population. The researcher acquired a sample rather than a complete enumeration (a census) of the population for a number of reasons. Apparently, it is cheaper to observe a part rather than the whole, but the researcher should be prepared to cope with the dangers of using samples. There would be no need for statistical theory if a census rather than a sample was always used to obtain information about populations. But a census may not be practical and is almost never economical. There are five main reasons for using a sample instead of doing a census. These are: economy, timeliness, the large size of populations, inaccessibility to the population and destructiveness of the observation.

4.5 Sample Size for the present study

Stratified and Convenience sampling technique were used to obtain the responses from the respondents. Districts of Haryana were taken as different strata and from them samples were chosen using convenience sampling. Notwithstanding the methodological deficiencies, a non-probability (convenience) sampling design is considered appropriate for the purpose of proposed research, since it is less complicated than a probability sampling design, incurs less expense and may be done to take advantage of the available respondents without the statistical complexity of a probability sample (Welman and Kruger, 2001). 1006 respondents were selected from different designation i.e. Assistant Professors (873), Associate Professors (103) and Professors (30) from different type of institutions in such a manner that they represented different districts of Haryana.

4.6 Distribution of the sample

A Distribution of sample in different Type of Institutions

Government Institutions – 123

Self Financed Institutions – 773

Societies Owned Institutions – 110

Geographical location

Whole State of Haryana

Districts covered in Haryana. Number of Private Institutions covered

Ambala 4

Bhiwani 5

Faridabad 1

Gurgaon 5

Hissar 4

Jhajjar 4

Jind 3

Kaithal 2

Karnal 3

Kurukshetra 1

Mahendragardh 1

Panchkula 1

Panipat 2

Rewari 1

Rohatk 2

Sirsa 1

Sonipat 3

Yamuna Nagar 7

Government Intuitions covered

Maharishi Dayanand University, Rohtak

Guru Jambheshwar University of Science and Technology, Hisar

Kurukshetra University, Kurukshetra

Haryana Agriculture University, Hisar

Ch. Devi Lal University, Sirsa

Government College, Gurgaon

In total 50 Private and 06 Government institutions were covered in the study.

4.7 Tools Used

The term scaling is applied to the attempts to measure the response objectively. Response is a resultant of number of external and internal factors. Depending upon the response to be measured, appropriate instruments are designed. Scaling is a technique used for measuring qualitative responses of respondents for instance those related to their perception, likes, dislikes and preferences. Likert Scale was developed by Rensis Likert. Likert Scales are of ordinal type, they enable one to rank attitudes and opinions. In it, the respondents are asked to indicate a degree of agreement and disagreement with each of a series of statement.

Each statement is assigned a numerical score ranging from 1 to 5 or 1 to 7. Each degree of agreement is given a numerical score and the respondents total score is computed by summing these scores. This total score of respondent reveals the particular opinion of a person. They take about the same amount of efforts to create as Thurston scale and are considered more discriminating and reliable because of the larger range of responses typically given in Likert scale. A typical Likert scale has 20 – 30 statements. While designing a good Likert Scale, first a large pool of statements relevant to the measurement of response has to be generated and then from the pool statements, the statements which are vague and non-discriminating have to be eliminated. This type of scale is widely used in the social and behavioral sciences, including interdisciplinary subject centered fields of interest, such as organizational research, marketing and public opinion research.

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4.8 Tool Construction

For the study of responses from teachers in Haryana structured questionnaire were used. The instrument used for the present study consists of 4 set of questionnaire. First part consisted of questions about demographic profile of respondents; Second part consisted of Job Satisfaction Scale, to measure the response about Job Satisfaction; Third part consisted of Work Motivation Questionnaire, to measure response about Work Motivation and Forth part consisted of Organizational Commitment Scale, to measure response about Organizational Commitment from teachers in different institutions.

In order to answer established research objectives, three different instruments were used. Job Satisfaction Scale (JSC) developed by Dr. Amar Singh and Dr. T.R. Sharma was used to measure each participant satisfaction level. The JSC maintains a high reliability of 0.978. This study produced an alpha reliability coefficient of 0.86. The JSC was employed to determine the job satisfaction level of each participant in his or her institution.

To measure the each participant Work Motivation, the Work Motivation Questionnaire (WMQ) developed by K.G. Aggrawl (1988) was employed. The WMQ maintains a high reliability of 0.99. This study produced an alpha reliability coefficient of 0.91.

The forth instrument used in the study was Organizational Commitment Questionnaire developed by Mowday et al (1979). This was used to measure each participant commitment level towards institution they are working. The OCQ maintains a high reliability of .90. In the present study OCQ produced an alpha reliability of 0.84. However in all the three instruments the word “organization” was changed to “institution”.

The statements were written in English and responses were on five point scale. Score 1 was given to the most negative response and score 5 to most positive response.

After the selection of instruments, it was tried out on a sample of 101 teachers who were randomly selected from various institutions of Hissar and Bhiwani districts of Haryana. The scale was distributed to each of the respondent and they were asked to answer every item. The main objective of this pre-tryout was to study the test items for their suitability and practicability. The investigator personally approached the teachers in different institutions and all were encouraged to respond all the items. It was also made clear to them that their responses will be kept confidential. After the pilot, the scale was evaluated.

4.9 Method of Data Collection

Personal Interview method was used in the present study. The researcher visited various educational institutions of different districts of Haryana to collect the data. The researcher explained the sample, the method of responding to the statements of the scale. The Investigator discussed all the variables involved with respondents. The investigator promised the respondents about confidentiality of the responses.

4.10 Analysis of the Data

The data collected from various sources were tabulated and analyzed systematically with the help of appropriate tools such as Frequency, Arithmetic Mean, Standard Deviation, ANOVA and Co-relation to get the results. Use of SPSS (Statistical Package for Social Sciences) version 14.0 was made to analyze and present the data in tabular and graphical form, illustrating the key demographic variables in the study. Subsequently, the inferential statics based on the examination of each hypothesis formulated for the research were presented. The upper limit of statistical significance was set at 5% for null hypothesis. All statistical results were calculated at the 2-tailed level of significance in accordance with the non-directional hypotheses presented (Sekaran, 2000).

4.10.1 Descriptive Statistics

Descriptive Statistic describes the raw data in a clear manner. This method facilitates the presentation of numerical data in structured, accurate and summarized way (Neuman, 2000). The descriptive statistics considered appropriate for the current research is mean. According to Murphy and Davidshofer (1998), the mean refers to a measure of central tendency that gives a general picture of the data and is also referred as the average value for the distribution score.

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4.10.2 Inferential Statistics

These refer to statistical methods that can be utilized to make inferences about a specific population or sample based on results of the study (Welman & Kruger, 2001). The inferential statistics used in this study were Analysis of Variance and Pearson product-moment correlation coefficient.

4.10.2.1 Analysis of Variance

In analysis of variance comparisons are made between the groups, such as those found from analyzing the biographical data in comparing group findings regarding for example job satisfaction. Roberts (2005) argues that this type of analysis has the distinct advantage that all the groups are weighted against each other concomitantly with the appropriate variables.

Roberts (2005) acknowledges the following ANOVA assumptions:

The groups must be normally distributed

The groups must be independent

The population variance must be homogeneous

The population distribution must be normal

4.10.2.2 The Pearson Product – Moment Correlation Coefficient

According to Thorne and Giesen (2003), the Pearson product – moment correlation coefficient is used to establish the degree of relationship between variables. The outcome of this type of analysis results in finding whether a relationship exists between variables and their direction (positive and negative) and strength of such relationship- a



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