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Syed Hasan Qasim, profile picture
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Empirical research & Statistics

The document discusses empirical research methods, detailing data collection techniques like observation and questionnaires, and distinguishes between quantitative and qualitative research. It covers sampling techniques, descriptive and inferential statistics, and tests such as t-tests and chi-square for analyzing data relationships. The content emphasizes the importance of sample size and representativeness in drawing valid conclusions from research.

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Syed H. Qasim
In research, the word empirical refers to collection of data using evidence that is collected through observation, interview, questionnaire, checklist, rating scale or by using some measuring tool and test them to come up with conclusions. 1. Collection of Data 2. Testing of Data 1. Sampling Descriptive & Inferential 2. Tools Statistics
Empirical research can be conducted and analysed using qualitative or quantitative methods. Quantitative research: Quantitative research methods are used to gather information through numerical data.  Highly structured  Larger Sample  survey, questionnaire, scales ,etc Qualitative research: Qualitative research methods are used to gather non numerical data. It is used to find meanings, opinions, or the underlying reasons from its subjects.  unstructured or semi structured.  sample size is usually small  focus groups, observation, interviews, etc.
Population – Identifiable and well specified group of individuals  Finite  Infinite
Representative of the population A good sample Large in size [30+] Represent all the parts M1 M0 As M1 approaches M0, estimate is better Population Parameter Sample Statistic
What is Probability? Conditions  Size of population  Equal chance of being selected  Sample size must be clearly specified Simple Random Sampling Stratified Random Sampling Cluster or Area Sampling
Equal chance of being selected.  Sampling without replacement  Sampling with replacement Types  Fishbowl method  Table of Random numbers  Computer determined randomness
Gender SES Religion One strata Two strata Non overlapping & Multiple strata Homogeneous Proportionate stratified sampling Disproportionate stratified sampling
Population Sample (5%) Ph.D 5 PG 35 Random UG 60 Total 2000 100 Sampling error is minimized Time consuming 100 700 1200
Population Sample (5%) Ph.D 25 PG 25 Random UG 50 Total 2000 100 Common in Behavioural Research Less Time consuming 100 700 1200
Larger geographical area is covered Flexibility
No way of assessing the probability of elements of population being included in the sample. Quota Sampling Purposive Sampling Accidental Sampling Systematic Sampling Snowball Sampling
Quota Sampling Similar to stratified random sampling but final selection is not random but according to investigator convenience. Purposive Sampling Investigator makes a judgment regarding selection. Purpose should be solved Accidental Sampling Investigator select according to his convenience
Systematic Sample Selecting every nth person from predetermined list Snowball Sampling Sociometric technique
Man-Made Tool/Test Standardized Tool/Test Interview Observation Questionnaire Rating Scale
Descriptive statistics give information that describes the data in some manner. A graphical representation of data is another method of descriptive statistics. Descriptive statistics do not, allow us to reach conclusions regarding any hypotheses we might have made. They are simply a way to describe our data. Example Measures of Central Tendency – Mean, Median, Mode Measures of Variability – Range, Quartile, Standard Deviation
Inferential statistics is to draw conclusions from a sample and generalize them to the population. The most common methodologies used are z-test t-tests Analysis of Variance (ANOVA) Chi-square Correlation Factor Analysis
A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. X1= Mean of first group X2= Mean of Second group S1= Standard deviation of one group S2= Standard deviation of second group N1=Total number of observations in one group N2= Total number of observation in second group
 simple random sample, that the data is collected from a representative, randomly selected portion of the total population.  the data, when plotted, results in a normal distribution, bell-shaped distribution curve.  large sample size is used.  Homogeneous, or equal, variance exists when the standard deviations of samples are approximately equal.
Analysis of Variance (ANOVA) is a statistical technique applied where more than two samples are meant to be compared. It is also called F-test One way analysis: When we are comparing more than three groups based on one factor variable. Two way analysis: When factor variables are more than two.
Chi-square is a statistical test commonly used to compare observed data with data we would expect to obtain according to a specific hypothesis. It is used to determine whether there is a significant association between the two variables. Example: In an election survey, voters might be classified by gender (male or female) and voting preference (Democrat, Republican, or Independent). We could use a chi-square test for independence to determine whether gender is related to voting preference.
Correlation is the average relationship between two or more variables. When the change in one variable makes or causes a change in other variable then there is a correlation between these two variables.
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