Approaches to social research pdf download

How should social science researchers deal with data inaccuracies? This article uses Web-based survey data collected from faculty members in three social science disciplines to document variation in … Expand. Methods textbooks play a role in socializing a new generation of researchers about ethical research. How do undergraduate social research methods textbooks portray harm, its prevalence, and ways to … Expand.

Theoretical and philosophical considerations in the realm of the Social Sciences for Public Administration and Management emerging researchers. This article aims to provide a conceptual and theoretical analysis of the main theoretical and philosophical perspectives in social science research for researchers doing research in the disciplinary … Expand.

The goal of this chapter is to introduce some of the major issues related to the use of survey data in social science research in developing countries. I will not address all aspects of survey … Expand. Taking Action Using Systems Research. Libraries near you: WorldCat. Approaches to social research , Oxford University Press.

Approaches to social research First published in Subjects Social sciences , Methodology , Research , Analyse des donnees , Sciences sociales , Methodologie de recherche , Collecte de donnees , Traitement des donnees , Social sciences, research , Social sciences, methodology. Edition Notes Previous ed. Classifications Library of Congress H A , H S , H S The Physical Object Pagination xviii, p. Community Reviews 0 Feedback?

Toggle navigation. The thoroughly updated sixth edition offers unrivalled coverage of quantitative, qualitative, and mixed methods with renewed focus and a fresh, modern feel.

Social Research Methods 6E. Get Books. Clear, comprehensive, and trusted, Bryman's Social Research Methods has guided over a quarter of a million students through their research methods course and student research project. Social Research Methods. The author follows two chapters on the fundamentals of social science and social research with three on preparation, two on interviewing, one on scaling, and two on relative advantages and methods of participative, direct and indirect observation.

Methods of Social Research. This is to ensure the integrity of the scientific knowledge production. It may be important to put things in the right perspectives here. Every scientific study is supposedly a contribution to knowledge production. This is how scientific knowledge grows.

No one is too inexperienced to make a modest contribution and nobody is too experienced to do it at her own whims and caprices. Everyone must follow the rules—the rules of the scientific method. Data analysis is a critical part of the scientific method. Therefore in analyzing data, a researcher, irrespective of her level in research, has an obligation to herself, if she is worth the name, the scientific community, and the larger public to follow the rigorous procedures for analyzing her data.

It is better not to analyze data at all than to fraudulently through laziness, ineptitude, or outright fraud or a combination of these analyze data. It is tantamount to producing fake drugs for public consumption. So rather than defraud yourself, the scientific community and the general public in your data analyses, it is better you seek to acquire requisite skills, or employ the services of others who can help you to analyze your data.

Whatever decisions you make, you are still responsible for your data analyses, be it quantitative or qualitative. Statistics: The Analysis of Quantitative Data Quantitative data analysis is a powerful tool; nevertheless, it is only as good as the original data, data collection instrument, operational definitions, and research question.

So these must be given deserved attention prior to the data analysis stage. After collecting quantitative data, there is a need to make sense of the responses collected. We do this by organizing, summarizing, and doing exploratory analysis. We then communicate meanings to others using tables, graphical displays, and summary statistics. Quantitative analysis helps us to see similarities, differences and relationships between phenomena investigated, that is, things we have collected data on.

The analysis of quantitative data is generally called statistics. The good news, however, is that as a researcher you are not required to have a full knowledge of how it works in order to use it effectively. What is required, as Punch puts it is to understand the logic behind the main statistical tools and, and appreciation of how and when to use them in actual research situations.

Since quantitative data are in form of numbers, quantitative data requires using statistical tools. Analyzing quantitative data requires familiarity with certain foundational concepts germane to the field of numerical analysis. These include scales of data, parametric and non- parametric data, descriptive and inferential statistics, kinds of variables, hypotheses, one-tailed and two-tailed tests, and statistical significance.

Scales of Data Scales or levels of data refer to the nature or kinds of numbers that researchers deal with. The nature of data we are dealing with determines the kind of analysis that can be performed with such data.

There are four kinds of data: nominal, ordinal, interval and ratio, each subsuming incorporating the characteristics of its predecessor.

It is erroneous to not apply the statistics of a higher order scale of data to data at lower scale when analyzing the data. Nominal scale is a naming quality. It involves determining the presence or absence of a characteristic and categorizes to mutually exclusive groups. Nominal measurement is also referred to as categorical measurement, since naming involves categorisation.

For instance, according females the number 1 category and males the number 2 category. There cannot 1. Nominal data denote discrete variables. The implication of this for data analyses is that the mode the most frequent score in a set of scores is the only appropriate central tendency statistics for nominal data.

The chi-square analyses are the most appropriate statistics for nominal data Dane, Chi square analyses are used to determine whether the frequencies of scores in the categories defined by the variable match the frequencies one would expect is based on chance or based on theoretical predictions.

Ordinal scale classifies and introduces an order into the data, while keeping the features of nominal scale. It involves ranking or otherwise determining an order of intensity for a quality. Ordinal measurement identifies the relative intensity of a characteristic, but not reflects any level of absolute intensity.

However, it helps to place them in order. Ordinal measurement is like the place an athlete finishes in a sprint. The winner may be a head ahead of the second-place finisher or several seconds. The positions 1st, 2nd, 3rd … do not tell us how much faster an athlete in comparison to others is. Therefore, calculating a mean of ordinal score is as meaningless as that of nominal measurement.

Generally, any statistical technique that involves comparisons on the basis of the median is appropriate for ordinal data. Ordinal scales are frequently are such rating scales or Likert scales frequently used in asking for opinions or rating attitude.

Interval scale has a metric, that is, a regular and equal interval between each data point, as well as keeping the features of ordinal scales. It involves a continuum composed of equally spaced intervals. To all intents and purposes, the statistics for interval scale is the same for ratio scale. Ratio scale has all the features of interval scale and adds a powerful feature—a true zero.

This and the opportunity to use ratios make it the most powerful level of data. This assumption makes it safe for inferences to be made. Interval and ratio scales are considered to be parametric.

Non- parametric data, on the other hand, do not have assumptions about the population. This is usually because the characteristics of the population are not known. Nominal and ordinal data are considered to be non-parametric. The implications for data analysis is that, whilst non-parametric statistics can be applied to parametric data, parametric statistics cannot be applied to non-parametric data.

Non- parametric data tend to be derived from questionnaires, whilst parametric data tend to be derived from experiment and tests. Descriptive and Inferential Statistics Descriptive statistics describe and present data. They make no attempt to infer or predict population parameters. They are only concerned with data enumeration and organization, that is, they simply report what has been found using variety of ways.

Descriptive statistics therefore are for summarizing quantitative data. Simple frequency distributions and percentages are useful in summarizing and understanding data. Scores in the distribution are usually grouped in ranges and tabulated according to how many respondents responded this way or that way or fell into this or that category. They also help the researcher to stay close to the data at the initial stages of analysis.

Punch, Cross-tabulation is another presentational device in which one variable is presented in relation to another, with relevant data placed into corresponding cells.

It helps us to compare across groups and draw attention to certain factors. It also helps us to show trends or tendencies in the data. Moreover, it aids in rating scales of agreement to disagreement. The arithmetic mean is the standard average, that is, the arithmetic average of a set of values, or distribution.

The mean is the most commonly used measure of central tendency. It is calculated by adding up all the data, and then dividing this total by the number of values in the data.

There are two important things to know about the mean. First, it is very useful where scores within a distribution do not vary too much but not so effective where there is great variance. Second, the mean is very useful in estimating variance. The median is the middle score in a ranked ordered distribution, that is, the midpoint score of a range of data. It is useful for ordinal data. The mode is the score with the highest frequency. This could be more than one.

It is useful for all scales of data. It is particularly useful for nominal and ordinal data, that is, discrete and categorical data, rather than continuous data. While descriptive statistics may be useful, inferential statistics are usually more valuable for researchers and are typically more powerful. Inferential statistics seeks to make inferences and predictions based on the data collected. Inferential statistics include hypothesis testing, correlations, regression and multiple regression, difference testing, t-tests, analysis of variance, factor analysis and structural equation modeling.

It is important to know exactly what you want to do right from the outset. This will help you to choose the most appropriate data collection and analysis techniques for your study. If you intend to describe what happens with your sample of participants, then descriptive statistics will be appropriate.

But if you want to be able to generalize your findings to a wider population you will find inferential statistics more appropriate.

Hypotheses and Hypothesis Testing Quantitative research is often concerned with finding evidence to support or contradict an idea or hypothesis. For example one may propose that if undergraduates are taught Peace and Conflict Resolution it will improve their peace attitude.

One will then explain why a particular answer is expected with the aid of a theory. A null hypothesis indicates that there will be no change or effect while an alternative hypothesis which is usually the experimental hypothesis claim their will be change. For this purpose a sample is taken from the population. If it is an experiment a quantitative research method two groups of students will be set up for this purpose. One group will be taught Peace and Conflict Resolution and another will not be taught the course.

Then the peace attitude of each group will be tested using appropriate methods, for example observation in a laboratory or rated using rating scales in interviews or questionnaires. Then we determine which group does better in peace attitude rating. Quasi-experimental design will take advantage of pre-existing groups rather than create the groups for the purpose of the research and administer its tests on them.

Thereby the researcher determines which of the hypotheses null or alternative the collected data support. It is important to note that testing hypothesis does not prove or disprove a hypothesis or the theory behind it.

The evidence only support or contradicts a hypothesis. Hypothesis includes concepts which need to be measured.