statistics grand mean

Observation is a popular method of data collection in behavioral sciences. The power, observation has been summed by W.L. Prosser as follows

“there is still no man that would not accept dog tracks in the mud against the sworn testimony of a hundred eye witnesses that no dog had passed by.”

Observation refers to the monitoring and recording of behavioral and non behavioral activities and conditions in a systematic manner to obtain information about the phenomena of interest, ‘Behavioral Observation’ is:

  • Non verbal analysis like body movement. eye movement.
  • Linguistic analysis which includes observing sounds like ohs! and abs!
  • Extra linguistic analysis which observes the pitch timbre, rate of speaking etc.
  • Spatial analysis about how people relate to each other.

The non behavioral observation is an analysis of records e.g. newspaper archives, physical condition analysis such as checking the quality of grains in gunny bags and process analysis which includes observing any process. Observation can be classified into various, categories.


Type of Observation

  1. Structured Vs. Unstructured Observation – In structured observation the problem has been clearly defined, hence the behavior to be observed and the method by which it will be measured is specified beforehand in detail. This reduces the chances of observer introducing observer’s bias in research e.g. study of p1ant safety compliance can be observed in a structure manner.

    Unstructured analysis is used in situations where the problem has not been clearly defined hence it cannot be pre specified that what is to be observed. Hence a researcher monitors all relevant phenomena and a great deal of flexibility is allowed in terms of what they note and record e.g. the student’s behavior in a class would require monitoring their total behavior in the class environment. The data collected through unstructured analysis should be analyzed carefully so that no bias is introduced.

  2. Disguised Vs. Undisguised Observation – This classification has been done on the basis of whether the subjects should know that they are being observed or not. In disguised observation, the subjects are unaware of the facts that they are being observed. Their behavior is observed using hidden cameras, one way mirrors, or other devices. Since the subjects are unaware that they are being observed hence they behave in a natural way. The drawback is that it may take long hours of observation before the subjects display the phenomena of interest. Disguised observation may be:
    • Direct observation when the behavior is observed by the researcher himself personally.
    • Indirect observation which is the effect or the result of the behavior that is observed.

    In undisguised observation, the subjects are aware that they are being observed. In this type of observation, there is the fear that the subject might show a typical activity. The entry of observer may upset the subject, but for how long this disruption will exist cannot be said conclusively. Studies have shown that such descriptions are short-lived and the subjects soon resume normal behavior.

  3. Participant vs. Non-Participant Observation – If the observer participates in the situation while observing it is termed as participant observation. g. a researcher studying the life style of slum dwellers, following participant observation, will himself stay in slums. His role as an observer may be concealed or revealed. By becoming a part of the setting he is able to observe in an insightful manner. A problem that arises out of this method is that the observer may become sympathetic to the subjects and would have problem in viewing his research objectively.

    In case of non-participant observation, the observer remains outside the setting and does not involve himself or participate in the situation.

  4. Natural vs. Contrived Observation. – In natural observation the behavior is observed as it takes place in the actual setting e.g. the consumer preferences observed directly at Pizza Hut where consumers are ordering pizza. The advantage of this method is that the true results are obtained, but it is expensive and time consuming method.

    In contrived observation, the phenomena is observed in an artificial or simulated setting e.g. the consumers instead of being observed in a restaurant are made to order in a setting that looks like a restaurant but is not an actual one. This type of observation has the advantage of being over in a short time and recording of behavior is easily done. However, since the consumer’s are conscious of their setting they may not show actual behavior.

  5. Classification on the Basis of Mode of Administration – This includes: monitors and records the behavior as it occurs. The recording is done on an observation schedule. The personal observation not only records what, has been specified but also identifies and records unexpected behaviors that defy pre-established response categories.
    • Mechanical Observation – Mechanical devices, instead of human’s are to record the behavior. The devices record the behavior as it occurs and data is sorted and analyzed later on. Apart from cameras, other devices are galvanometer which measures the emotional arousal induced by an exposure to a specific stimuli, audiometer and people meter that record which channel on TV is being viewed with the latter also recording who is viewing the channel, coulometer records the eye movement etc.
    • Audit – It is the process of obtaining information by physical examination of data. The audit, which is a count of physical objects, is generally done by the researcher himself. An audit can be a store audit or a pantry audit. The store audits are performed by the distributors or manufacturers in order to ana1yse the market share, purchase pattern etc. e.g. the researcher may check the store records or do an analysis of inventory on hand to record the data. The pantry audit involves the researcher developing an inventory of brands quantities and package sizes of products in a consumer’s home, generally in the course of a personal interview. Such an audit is used to supplement or test the truthfulness of information provided in the direct questionnaire.
    • Content Analysis – Content analysis is the objective, systematic and initiative description of the manifest content of communication. This method consists of observation and analysis. It involves analysis of the contents of a communication spoken or printed. Through content analysis a quantitative analysis of the presence of a certain characteristic in a document can be done by identifying and counting the presence of a certain characteristic e.g. If we want to find out which- politicians frequently use secularism in their speech, then certain key words which are synonymous with secularism are identified. Next the speech of various politicians is analyzed to observe the numbers of times the keywords have appeared. The politician whose speech has the maximum number of key words appearing in its content is the one who uses secularism frequently to woo voters however these days’ content analysis is used for qualitative analysis whereby the general message of the document is analyzed.

Conducting an Observation Study

While conducting an observation study, care should be taken that it is free from errors and bias: The following basic steps can be followed.

  1. Specify the type of, study. If the, study is exploratory in nature then even simple observation will suffice. In other studies the more systematic. Depending on the environment of study the observation could be unstructured or it could be structured.
  2. Specify the contents of observation. The researcher should specify the variables of interest which, are to be observed along with other variables which influence the study, For each variable identified, its definition and the measurement terms to be used to record the results should be specified.
  3. Observer training. An observer has to be trained to record and observe the right things. He should have the ability to remember details, to objectively view the phenomena of interest and have high concentration. The more unstructured an observation, the greater the dependency on observer for securing the research results. Hence an observer’s experience in such a situation becomes necessary.
  4. Develop an observation form. The observation form is an observation plan which addresses the following:
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • What i.e. the aspects which are to be observed. The characteristics that are to be observed need to be specified.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • When i.e. the time of observation. It has to be decided whether the observation is to be conducted at a particular time or any time is the right time.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • Where i.e. the place where the observation is to be made. The methodology used to select the place where observation is to be done is also to be specified.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • How i.e. the method of observation. The details regarding how the data will be observed i.e. directly or indirectly, by single or multiple observers, through personal or mechanical method, all should be stated.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • Once the plan. Is drafted the observer is ready to monitor and record the phenomena of interest.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.
    • Who i.e. the subjects, the intermediaries who are to be studied and who help in studying, respectively.

Table of Contents
1.statistics adjusted rsquared

2.statistics analysis of variance

3.statistics arithmetic mean

4.statistics arithmetic median

5.statistics arithmetic mode

6.statistics arithmetic range

7.statistics bar graph

8.statistics best point estimation

9.statistics beta distribution

10.statistics binomial distribution

11.statistics blackscholes model

12.statistics boxplots

13.statistics central limit theorem

14.statistics chebyshevs theorem

15.statistics chisquared distribution

16.statistics chi squared table

17.statistics circular permutation

18.statistics cluster sampling

19.statistics cohens kappa coefficient

20.statistics combination

21.statistics combination with replacement

22.statistics comparing plots

23.statistics continuous uniform distribution

24.statistics cumulative frequency

25.statistics coefficient of variation

26.statistics correlation coefficient

27.statistics cumulative plots

28.statistics cumulative poisson distribution

29.statistics data collection

30.statistics data collection questionaire designing

31.statistics data collection observation

32.statistics data collection case study method

33.statistics data patterns

34.statistics deciles statistics

35.statistics dot plot

36.statistics exponential distribution

37.statistics f distribution

38.statistics f test table

39.statistics factorial

40.statistics frequency distribution

41.statistics gamma distribution

42.statistics geometric mean

43.statistics geometric probability distribution

44.statistics goodness of fit

45.statistics grand mean

46.statistics gumbel distribution

47.statistics harmonic mean

48.statistics harmonic number

49.statistics harmonic resonance frequency

50.statistics histograms

51.statistics hypergeometric distribution

52.statistics hypothesis testing

53.statistics interval estimation

54.statistics inverse gamma distribution

55.statistics kolmogorov smirnov test

56.statistics kurtosis

57.statistics laplace distribution

58.statistics linear regression

59.statistics log gamma distribution

60.statistics logistic regression

61.statistics mcnemar test

62.statistics mean deviation

63.statistics means difference

64.statistics multinomial distribution

65.statistics negative binomial distribution

66.statistics normal distribution

67.statistics odd and even permutation

68.statistics one proportion z test

69.statistics outlier function

70.statistics permutation

71.statistics permutation with replacement

72.statistics pie chart

73.statistics poisson distribution

74.statistics pooled variance r

75.statistics power calculator

76.statistics probability

77.statistics probability additive theorem

78.statistics probability multiplicative theorem

79.statistics probability bayes theorem

80.statistics probability density function

81.statistics process capability cp amp process performance pp

82.statistics process sigma

83.statistics quadratic regression equation

84.statistics qualitative data vs quantitative data

85.statistics quartile deviation

86.statistics range rule of thumb

87.statistics rayleigh distribution

88.statistics regression intercept confidence interval

89.statistics relative standard deviation

90.statistics reliability coefficient

91.statistics required sample size

92.statistics residual analysis

93.statistics residual sum of squares

94.statistics root mean square

95.statistics sample planning

96.statistics sampling methods

97.statistics scatterplots

98.statistics shannon wiener diversity index

99.statistics signal to noise ratio

100.statistics simple random sampling

101.statistics skewness

102.statistics standard deviation

103.statistics standard error se

104.statistics standard normal table

105.statistics statistical significance

106.statistics formulas

107.statistics notations

108.statistics stem and leaf plot

109.statistics stratified sampling

110.statistics student t test

111.statistics sum of square

112.statistics tdistribution table

113.statistics ti 83 exponential regression

114.statistics transformations

115.statistics trimmed mean

116.statistics type i amp ii errors

117.statistics variance

118.statistics venn diagram

119.statistics weak law of large numbers

120.statistics z table

121.discuss statistics

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