Measures of Central Tendency

Measures of Central Tendency The one single value that reflects the nature and characteristics of the entire given data is called as central value. Central tendency refers to the middle point of a given distribution. It is other wise called as ‘measures of location. The nature of this value is such that it always lies between the highest value and the lowest value of that series. In other wards, it lies at the centre or at the middle of the series. CHARACTERISTICS OF A GOOD AVERAGE: Yule and Kendall have pointed out some basic characteristics which an average should satisfy to call it as good average. They are: Average is the easiest method to calculate It should be rigidly defined. This says that, the series of whose average is calculated should have only one interpretation. One interpretation will avoid personal prejudice or bias. It should be representative of the entire series. In other wards, the value should lie between the upper and lower limit of the data. It should have capable of further algebraic treatment. In other wards, an ideal average is one which can be used for further statistical calculations. It should not be affected by the extreme values of the observation or series. DEFINITIONS: Different experts have defined differently to the concept of average. Gupta (2008) in his work has narrated Lawrence J. Kaplan definition as ‘one of the most widely used set of summery figures is known as measures of location, which are often referred to as averages, measures of central tendency or central location. The purpose of computing an average value for a set of observation is to obtain a single value which is representative of all the items and which the mind can grasp simply and quickly. The single value is the point of location around which the individual items cluster. This opinion clearly narrates the basic purpose of computing an average. Similarly, Croxton and Cowden define the concept as ‘an average is a single value within the range of the data that is used to represent all of the values in the series. Since the average is somewhere within the range of data, it is sometimes called a measure of central value. TYPES OF AVERAGES: Following five are frequently used types of an average or measure of central tendency. They are Arithmetic mean Weighted arithmetic mean Median Mode Geometric Mean and Harmonic Mean All the above five types are discussed below in detail. THE ARITHMETIC MEAN: Arithmetic mean is the most simple and frequently used technique of computing central tendency. The average is also called as mean. It is other wise called as a single number representing a whole data set. It can be computed in a several ways. Commonly it can be computed by dividing the total value by the number of observations. Let ‘n be the number of items in a case. Each individual item in a list can be represented in a relationship as x1, x2, x3, ,xn. In this relationship, ‘x1 is one value, ‘x2 is another value in the series and the value extends upto a particular limit represented by ‘xn. The dots in the relationship express that there are some values between the two extremes which are omitted in the relationship. Some people interprets the same relationship as, which can be read as ‘x-sub-i, as i runs from 1 upto n. In case the numbers of variable in list is more, then it requires a long space for deriving the mean. Thus the summation notation is used to describe the entire relationship. The above relationship can be derived with the help of summation as: , representing the sum of the ‘x values, using the index ‘i to enumerate from the starting value i =1 to the ending value i = n. thus we have and the average can be represented as The symbol ‘i is again nothing but a continuing covariance. The readers should not be confused while using the notation , rather they can also use or or any other similar notation which are of same meaning. The mean of a series can be calculated in a number of ways. Following are some basic ways that are commonly used in researchers related to management and social sciences, particularly by the beginners. However, the readers should not be confused on sample mean and population mean. A sample of a population of ‘n observations and the mean of sample is denoted by ‘. Where as when one measure the population mean i.e., the entire variables of a study than the mean is represented by the symbol ‘Â µ, which is pronounced as ‘mue and is derived from the Greek letter ‘mu. Below we are discussing the concepts of sample mean. Type-1: In case of individual observation: a. Direct method- Mean or average can be calculated directly in the following way Step-1: First of all the researcher has to add all the observations of a given series. The observations are x1, x2, x3, xn. Step-2- Count how many observations are their in that series (n) Step-3- the following procedure than adopted to get the average. Thus the average or mean denoted as ‘and can be read as ‘x bar is derives as: Thus it can be said that the average mark of the final contestants in the quiz competition is 67.6 marks which can be rounded over to 70 marks. b. Short-cut method- The average or mean can also be calculated by using short-cut method. This method is applicable when a particular series is having so many observations. In other wards, to reduce calculations this method is generally used. The steps of calculating mean by this method is as follows: i. The research has to assume any one value from the entire series. This value is called as assumed value. Let this value be denoted here as ‘P. ii. Differentiate each a value from this assumed vale. That is find out individual values of each observation. Let this difference value be denoted as ‘B. Hence B=xn-P where n= 1,2,3,n. iii. Add all the difference value or get sum of B and count the number of observation ‘n. iv. Putting the values in the following formula and get the value of mean. Type-2: In case of discrete observations or series of data: Discrete series are the variables whose values can be identified and isolated. In such a case the variant is a whole number, but is form frequency distribution. The data set derived in case-1 above is called as ungrouped data. The computations in case of these data are not difficult. Where as, if the data set is having frequencies are called as groped data. a. Direct method: Following are some steps of calculating mean by using the direct method i. In the first step, the values of each row (X) are to be multiplied by its respective frequencies (f). ii. Calculate the sum of the frequencies (column-2 in our example) at the end of the column denoted as iii. Calculate the sum of the X*f values at the end of the column (column-3 in our below derived example) denoted as iv. Mean () can be calculated by using the formula b. Short cut method: Arithmetic mean can also be calculated by using the short cut method or assumed mean method. This method is generally used by the researchers to avoid the time requirements and calculation complexities. Following are the steps of calculating mean by this method. i. The first step is to assume a value from the ‘X values of the series (denoted as A= assumed value) ii. In this step in another column we have to calculate the deviation value (denoted as D) of ‘X to that of assumed value (A) i.e., D = X-A iii. Multiply each D with f i.e., find our Df iv. Calculate the value of sum of at the end of respective columns. v. Mean can be calculated by using the formula as Type-3: In case of continuous observations or series of data: Another type of frequency distributions is there which consists of data that are grouped by classes. In such case each value of an observation falls somewhere in one of the classes. Calculation of arithmetic mean in case of grouped data is some what different from that of ungrouped data. To find out the arithmetic mean of continuous series, one has to calculate the midpoint of each class interval. To make midpoints come out in whole cents, one has to round up the value. Mean in continuous series can be calculated in two ways as derived below: a. Direct method: In this method, mean can be calculated by using the steps as i. First step is to calculate the mid point of each class interval. The mid point is denoted by ‘m and can be calculated as . ii. Multiply the mid points of each class interval (m) with its respective frequencies (f) i.e., find out mf iii. Calculate the value of sum of at the end of respective columns. iv. Mean can be calculated by using the formula as b. Short cut method: Mean can also be calculated by using short cut method. Following are the steps to calculate mean by this method. i. First step is to calculate the mid point of each class interval. The mid point is denoted by ‘m and can be calculated as . ii. Assume a value from the ‘m values of the series (denoted as A= assumed value) iii. In this step in another column we have to calculate the deviation value (denoted as D) of ‘m to that of assumed value (A) i.e., D = m A iv. Multiply each D with f i.e., find our Df v. Calculate the value of sum of at the end of respective columns. vi. Put the values in the following formula to get mean of the series THE WEIGHTED ARITHMETIC MEAN: In real life situation in management studies and social sciences, some items need more importance than that of the other items of that series. Hence, importance assigned to different items with the help of numerical value as per the priority basis in a series as called as weights. The arithmetic mean on the other hand, gives equal weightage or importance to each observation of the series. In such a case, the weighted mean acts as the most important tool for studying the behaviour of the entire set of study. Here use of weighted mean is the only measure of central tendency for getting correct and accurate result. Following is the procedures of computing mean of a weighted series. By the way, an important problem that arises while using weighted mean is regarding selection of weights. Weights may be either actual or arbitrary, i.e., estimated. The researcher will not face any difficulty, if the actual weights are assigned to the set of data. But in case, if actual data is not assigned than it is advisable to assign arbitrary or imaginary weights. Following are some steps of calculating weighted mean: i. In the first step, the values of each row (X) are to be multiplied by its respective weights (W) ii. Calculate the sum of the weights (column-2 in our example) at the end of the column denoted as iii. Calculate the sum of the X*W values at the end of the column (column-3 in our below derived example) denoted as iv. Mean () can be calculated by using the formula Advantages of Arithmetic mean: Following are some advantages of arithmetic mean. i. The concept is more familiar concept among the people. It is unique because each data set has only one mean. ii. It is very easy to compute and requires fewer calculations. As every data set has a mean, hence, as a measure mean can be calculated. iii. Mean represents a single value to the entire data set. Thus easily one can interpret a data set its characteristics. iv. An average can be calculated of any type of series. Disadvantages of Arithmetic mean: The disadvantages are as follows. i. One of the greatest disadvantages of average is that it is mostly affected by the extreme values. For example let consider Sachin Tendulkars score in last three matches. Let it be, 100 in first match, 2 in second match and 10 in third match. The average score of these three matches will me 100+2+10/3=37. Thus it implies that Tendulkars average score is 37 which is not correct. Hence lead to wrong conclusion. ii. It is not possible to compute mean for a data set that has open-ended classes at either the high or low end of the scale. iii. The arithmetic average sometimes gives such value which cannot be found from the data series from which it is calculated. iv. It is unrealistic. v. It cannot be identified observation or graphic method of representing the data and interpretation. THE MEDIAN: Another one technique to measure central tendency of a series of observation is the median. Median is generally that value of the entire series which divides the entire series into two equal parts from the middle. In other wards, it is the exactly middle value of the series. Hence, fifty percent of the observations in the series are above the median value and other fifty or half observations are remains below the median value. However, if the series are having odd numbers of observations like 3,5,7,9,11,13 etc., then the median value will be equal to one of the exact value from the series. On the other hand, if the series is having even observations, then median value can be calculated by getting the arithmetic mean of the two middle values of the observations of the series. Median an a technique of measuring central tendency can be best used in cases where the problem sought for more qualitative or psychological in nature such as health, intelligence, satisfaction etc. Definitions: The concept of median can be clearer from the definitions derived below. Connor defined it as ‘the median is the value which divides the distribution into two equal parts, one part comprising all values greater, and the other values less than the median. Where as Croxton and Cowden defined it as ‘the median is that value which divides a series so that one half or more of the items are equal to or less than it and one half or more of the items are equal to or greater than it. Median can be computed in three different series separately. All the cases are discussed separately below. Computation of Median in Individual Series Computation of Median in Discrete Series and Computation of Median in Continuous Series Computation of Median in Individual Series: Following are some steps to calculate the median in individual series. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. Than the median value can be calculated by using the formula th value or item from the series. Where, N= Number of observation in that series. When N is odd number (like 5, 7,9,11,13 etc.) median value is one of the item within that series, but in case N will be a even number than median is the arithmetic mean of the two middle value after applying the above formula. The following problem can make the concept clear. Computation of Median in Discrete Series: Discrete series are those where the data set is assigned with frequencies or repetitions. Following are the steps of computing the median when the series is discrete. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. In the third column of the table, calculate the cumulative frequencies. Than the median class can be calculated by using the formula th value or item from the cumulative frequencies of the series. Computation of Median in Continuous Series: Continuous series are the series of data where the data ranges are in class intervals. Each class is having an upper limit and a lower limit. In such cases the computation of median is little bit different from that of the other two cases discussed above. Following are some steps to get median in continuous series of data. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. In the third column of the table, calculate the cumulative frequencies. Than the median class can be calculated by using the formula th value or item from the cumulative frequencies column of the series. Form the cumulative frequencies, one can get the median class i.e., in which class the value lies. This class is called as median class and one can get the lower value of the class and the upper value of the class. The following formula can be used to calculate the median We have to get the median class first. For this, median class is N/2 th value or 70/2= 35. The value 35 lies in the third row of the table against the class 30-40. Thus 30-40 is the median class and it shows that the median value lies in this class only. After getting the median class, to get the median value we have to apply the formula . Advantages of Median: Median as a measure of central tendency has following advantages of its own. It is very simple and can be easily understood. It is very easy to calculate and interpret. It Includes all the observations while calculation. Like that of arithmetic mean, median is not affected by the extreme values of the observation. It has the advantages for using further analysis. It can even used to calculate for open ended distribution. Disadvantages of Median: Median as a means to calculate central tendency is also not free from draw backs. Following are some important draw backs that are leveled against median. Median is not a widely measure to calculate central tendency like that of arithmetic mean and also mode. It is not based on algebraic treatment. THE MODE: Mode is defined as the value which occurs most often in the series or other wise called as the value having the highest frequencies. It is, hence, the value which has maximum concentration around it. Like that of median, mode is also more useful in case of qualitative data analysis. It can be used in problems generally having the discrete series of data and particularly, problems involving the expression of psychological determinants. Definitions: The concept of mode can be clearer from the definitions derived below. Croxten and Cowden defined it as ‘the mode of a distribution is the value at the point around which the items tend to be most heavily concentrated. It may be regarded as the most typical of a series of value. Similarly, in the words of Prof. Kenny ‘the value of the variable which occurs most frequently in a distribution is called the mode. Mode can be computed in three different series separately. All the cases are discussed separately below. Computation of Mode in Individual Series Computation of Mode in Discrete Series and Computation of Mode in Continuous Series Computation of Mode in Individual Series: Calculation of mode in individual series is very easy. The data is to be arranged in a sequential order and that value which occurs maximum times in that series is the value mode. The following example will make the concept clear. Computation of Mode in Discrete Series: Discrete series are those where the data set is assigned with frequencies or repetitions. Hence directly, mode will be that value which is having maximum frequency. By the way, for accuracy in calculation, there is a method called as groping method which is frequently used for calculating mode. Following is the illustration to calculate mode of a series by using grouping method. Consider the following data set and calculate mode by using the grouping method. The calculation carried out in different steps is derived as: Step-1: Sum of two frequencies including the first one i.e., 1+2=3, then 4+3=7, then 2+1=3 etc. Step-2: Sum of two frequencies excluding the first one i.e., 2+4=7, then 3+2=5, then 1+2=3 etc. Step-3: Sum of three frequencies including the first one i.e., 1+2+4=7, then 3+2+1=6 etc. Step-4: Sum of two frequencies excluding the first one i.e., 2+4+3=9, then 2+1+2=5 etc. Step-5: Sum of three frequencies excluding the first and second i.e., 4+3+2=9, then 1+2+1=4. Computation of Mode in Continuous Series: As already discussed, continuous series are the series of data where the data ranges are in class intervals. Each class is having an upper limit and a lower limit. In such cases the computation of mode is little bit different from that of the other two cases discussed above. Following are some steps to get mode in continuous series of data. Select the mode class. A mode class can be selected by selecting the highest frequency size. Mode value can be calculated by using the following formula Advantages of Mode: Following are some important advantages of mode as a measure of central tendency. It is easy to calculate and easy to understand. It eliminates the impact of extreme values. It is easy to locate and in some cases we can estimate mode by mere inspection. It is not affected by extreme values. Disadvantages of Mode: Following are some important disadvantages of mode. It is not suitable for further mathematical treatment. It may lead to a wrong conclusion. Some critiques criticized mode by saying that mode is influenced by length of the class interval. THE GEOMETRIC MEAN: Geometric mean, as another measure of central tendency is very much useful in social science and business related problems. It is an average which is most suitable when large weights have to be assigned to small values of observations and small weights to large values of observation. Geometric mean best suits to the problems where a particular situation changes over time in percentage terms. Hence it is basically used to find the average percent increase or decrease in sales, production, population etc. Again it is also considered to be the best average in the construction of index numbers. Geometric mean is defined as the Nth root of the product where there are N observations of a given series of data. For example, if a series is having only two observations then N will be two or we will take square root of the observations. Similarly, when series is having three observations then we have to take cube root and the process will continue like wise. Geometric mean can be calculated separately for two sets of data. Both are discussed below. When the data is ungrouped: In case of ungrouped series of observations, GM can be calculated by using the following formula: where X1 , X2 , X3, XN various observations of a series and N is the Nth observation of the data. But it is very difficult to calculate GM by using the above formula. Hence the above formula needs to be simplified. To simplify the formula, both side of the above formula is to be taken logarithms. To calculate the G.M. of an ungrouped data, following steps are to be adopted. Take the log of individual observations i.e., calculate log X. Make the sum of all log X values i.e., calculate Then use the above formula to calculate the G.M. of the series. When the data is grouped: Calculation of geometric mean in case of grouped data is little bit different from that of calculation of G.M. in case of ungrouped series. Following are some steps to calculate the G.M. in case of grouped data series. To calculate the G.M. of a grouped data, following steps are to be adopted. Take the mid point of the continuous series. Take the log of mid points i.e., calculate log X and it can also be denoted as log m Make the sum of all log X values i.e., calculate or Then use the following formula to calculate the G.M. of the series. Advantages of G.M.: Following are some advantages of G.M. i. One of the greatest advantages of G.M. is that it can be possible for further algebraic treatment i.e., combined G.M., can be calculated when there is availability of G.M., of two or more series along with their corresponding number of observations. ii. It is a very useful method of getting average when the series of observation possess rates of growth i.e., increase or decrease over a period of time. iii. Since it is useful in averaging ratios and percentages, hence, are more useful in social science and business related problems. Disadvantages of G.M.: G.M., as a technique of calculating central value is also not free from defects. Following are some disadvantages of G.M. i. It is very difficult to calculate the value of log and antilog and hence, compared to other methods of central tendency, G.M., is very difficult to compute. ii. The greatest disadvantage of G.M., is that it cannot be used when the series is having both negative or positive observations and observations having more zero values. THE HARMONIC MEAN: The last technique of getting the central tendency of a series of data is the Harmonic mean (H.M.). Harmonic mean, like the other methods of central tendency is not clearly defined. It is the reciprocal of the arithmetic mean of the reciprocal of the individual observations. H.M., is very much useful in those cases of observations where the nature of data is such that it express the average rate of growth of any events. For example, the average rate of increase of sales or profits, the average speed of a train or bus or a journey can be completed etc. Following is the general formula to calculate H.M.: When the data is ungrouped: When the observations of the series are ungrouped, H.M., can be calculated as: The step for calculating H.M., of ungrouped data by using the derived formula is very simple. In such a case, one has to find out the values of 1/X and then sum of 1/X. When the data is grouped: In case of grouped data, the formula for calculating H.M., is discussed as below: Take the mid point of the continuous series. Calculate 1/X and it can also be denoted as 1/m Make the sum of all 1/X values i.e., calculate Then use the following formula to calculate the H.M. of the series. Advantages of H.M.: Harmonic mean as a measure of central tendency is having following advantages. i. Harmonic mean considers each and every observation of the series. ii. It is simple to compute when compared to G.M. iii. It is very useful for averaging rates. Disadvantages of H.M.: Following are some disadvantages of H.M. i. It is rarely used as a technique of measuring central tendency. ii. It is not defined clearly like that of other techniques of measuring central value mean, median and mode. iii. Like that of G.M., H.M., cannot be used when the series is having both negative or positive observations and observations having more zero values. CONCLUSION: An average is a single value representing a group of values. Each type of averages has their own advantages and disadvantages and hence, they are having their own usefulness. But it is always confusing among the researchers that which average is the best among the five different techniques that we have discussed above? The answer to this question is very simple and says that no single average can be considered as best for all types of data. However, experts opine two considerations that the researchers must be kept in mind while going for selecting a technique to determine the average. The first consideration is that of determining the nature of data. If the data is more skewed it is better to avoid arithmetic mean, if the data is having gap around the middle value of the series, then median should be avoided and on the other hand, if the nature of series is such that they are unequal in class-intervals, then mode is to be avoided. The second consideration is on the type of value req uired. When there is need of composite average of all absolute or relative values, then arithmetic mean or geometric mean is to be selected, in case the researcher is in need of a middle value of the series, then median may be the best choice, but in case the most common value is needed, then will not be any alternative except mode. Similarly, Harmonic mean is useful in averaging ratios and percentages. SUMMERY: 1. Different experts have defined differently to the concept of average. 2. Arithmetic mean is the most simple and frequently used technique of computing central tendency. The average is also called as mean. It is other wise called as a single number representing a whole data set. 3. The best use of arithmetic mean is at the time of correcting some wrong entered data. For example in a group of 10 students, scoring an average of 60 marks, in a paper it was wrongly marked 70 instead of 65. the solution in such a cases is derived below: 4. In such a case, the weighted mean acts as the most important tool for studying the behaviour of the entire set of study. Here use of weighted mean is the only measure of central tendency for getting correct and accurate result. 5. Median is generally that value of the entire series which divides the entire series into two equal parts from the middle. 6. Mode is defined as the value which occurs most often in the series or other wise called as the value having the highest frequencies. It is, hence, the value which has maximum concentration around it. 7. Geometric mean is defined as the Nth root of the product where there are N observations of a given series of data. 8. Harmonic mean is the reciprocal of the arithmetic mean of the reciprocal of the individual observations. QUESTIONS: 1. In a class containing 90 students following heights (in inches) has been observed. Based on the data calculate the mean, median and mode of the class. 2. In a physical test camp meant for selection of army solders the following heights of the candidates have been observed. Find the mean, median and mode of the distribution. 3. From the distribution derived below, calculate mean and standard deviation of the series. 4. The following table derives the marks obtained in Indian Economy paper by 90 students in a class. Calculate the mean, median and mode of the following distribution. 5. The monthly profits of 180 shop keepers selling different commodities in a city footpath is derived below. Calculate the mean and median of the distribution. 6. The daily wage of 130 labourers working in a cotton mill in Ahmadabad cith is derived below. Calculate the mean, median and mode. 7. There is always controversy before the BCCI before selection of batsmen between Rahul Dravid and V.V.S. Laxman. Runs of 10 test matches of both the players are given below. Suggest who the better run getter is and who the consistent player is. 8. Calculate the mean, median and mode of the following distribution. 9. What do you mean by measure of central tendency? How far it helpful to a decision-maker in the process of decision making? 10. Define measure of central tendency? What are the basic criteria of a good average? 11. What do you mean by measure of central tendency? Compare and contrast arithmetic mean, median and mode by pointing out the advantages and disadvantages. 12. The expenditure on purchase of snacks by a group of hosteller per week is Measures of Central Tendency Measures of Central Tendency The one single value that reflects the nature and characteristics of the entire given data is called as central value. Central tendency refers to the middle point of a given distribution. It is other wise called as ‘measures of location. The nature of this value is such that it always lies between the highest value and the lowest value of that series. In other wards, it lies at the centre or at the middle of the series. CHARACTERISTICS OF A GOOD AVERAGE: Yule and Kendall have pointed out some basic characteristics which an average should satisfy to call it as good average. They are: Average is the easiest method to calculate It should be rigidly defined. This says that, the series of whose average is calculated should have only one interpretation. One interpretation will avoid personal prejudice or bias. It should be representative of the entire series. In other wards, the value should lie between the upper and lower limit of the data. It should have capable of further algebraic treatment. In other wards, an ideal average is one which can be used for further statistical calculations. It should not be affected by the extreme values of the observation or series. DEFINITIONS: Different experts have defined differently to the concept of average. Gupta (2008) in his work has narrated Lawrence J. Kaplan definition as ‘one of the most widely used set of summery figures is known as measures of location, which are often referred to as averages, measures of central tendency or central location. The purpose of computing an average value for a set of observation is to obtain a single value which is representative of all the items and which the mind can grasp simply and quickly. The single value is the point of location around which the individual items cluster. This opinion clearly narrates the basic purpose of computing an average. Similarly, Croxton and Cowden define the concept as ‘an average is a single value within the range of the data that is used to represent all of the values in the series. Since the average is somewhere within the range of data, it is sometimes called a measure of central value. TYPES OF AVERAGES: Following five are frequently used types of an average or measure of central tendency. They are Arithmetic mean Weighted arithmetic mean Median Mode Geometric Mean and Harmonic Mean All the above five types are discussed below in detail. THE ARITHMETIC MEAN: Arithmetic mean is the most simple and frequently used technique of computing central tendency. The average is also called as mean. It is other wise called as a single number representing a whole data set. It can be computed in a several ways. Commonly it can be computed by dividing the total value by the number of observations. Let ‘n be the number of items in a case. Each individual item in a list can be represented in a relationship as x1, x2, x3, ,xn. In this relationship, ‘x1 is one value, ‘x2 is another value in the series and the value extends upto a particular limit represented by ‘xn. The dots in the relationship express that there are some values between the two extremes which are omitted in the relationship. Some people interprets the same relationship as, which can be read as ‘x-sub-i, as i runs from 1 upto n. In case the numbers of variable in list is more, then it requires a long space for deriving the mean. Thus the summation notation is used to describe the entire relationship. The above relationship can be derived with the help of summation as: , representing the sum of the ‘x values, using the index ‘i to enumerate from the starting value i =1 to the ending value i = n. thus we have and the average can be represented as The symbol ‘i is again nothing but a continuing covariance. The readers should not be confused while using the notation , rather they can also use or or any other similar notation which are of same meaning. The mean of a series can be calculated in a number of ways. Following are some basic ways that are commonly used in researchers related to management and social sciences, particularly by the beginners. However, the readers should not be confused on sample mean and population mean. A sample of a population of ‘n observations and the mean of sample is denoted by ‘. Where as when one measure the population mean i.e., the entire variables of a study than the mean is represented by the symbol ‘Â µ, which is pronounced as ‘mue and is derived from the Greek letter ‘mu. Below we are discussing the concepts of sample mean. Type-1: In case of individual observation: a. Direct method- Mean or average can be calculated directly in the following way Step-1: First of all the researcher has to add all the observations of a given series. The observations are x1, x2, x3, xn. Step-2- Count how many observations are their in that series (n) Step-3- the following procedure than adopted to get the average. Thus the average or mean denoted as ‘and can be read as ‘x bar is derives as: Thus it can be said that the average mark of the final contestants in the quiz competition is 67.6 marks which can be rounded over to 70 marks. b. Short-cut method- The average or mean can also be calculated by using short-cut method. This method is applicable when a particular series is having so many observations. In other wards, to reduce calculations this method is generally used. The steps of calculating mean by this method is as follows: i. The research has to assume any one value from the entire series. This value is called as assumed value. Let this value be denoted here as ‘P. ii. Differentiate each a value from this assumed vale. That is find out individual values of each observation. Let this difference value be denoted as ‘B. Hence B=xn-P where n= 1,2,3,n. iii. Add all the difference value or get sum of B and count the number of observation ‘n. iv. Putting the values in the following formula and get the value of mean. Type-2: In case of discrete observations or series of data: Discrete series are the variables whose values can be identified and isolated. In such a case the variant is a whole number, but is form frequency distribution. The data set derived in case-1 above is called as ungrouped data. The computations in case of these data are not difficult. Where as, if the data set is having frequencies are called as groped data. a. Direct method: Following are some steps of calculating mean by using the direct method i. In the first step, the values of each row (X) are to be multiplied by its respective frequencies (f). ii. Calculate the sum of the frequencies (column-2 in our example) at the end of the column denoted as iii. Calculate the sum of the X*f values at the end of the column (column-3 in our below derived example) denoted as iv. Mean () can be calculated by using the formula b. Short cut method: Arithmetic mean can also be calculated by using the short cut method or assumed mean method. This method is generally used by the researchers to avoid the time requirements and calculation complexities. Following are the steps of calculating mean by this method. i. The first step is to assume a value from the ‘X values of the series (denoted as A= assumed value) ii. In this step in another column we have to calculate the deviation value (denoted as D) of ‘X to that of assumed value (A) i.e., D = X-A iii. Multiply each D with f i.e., find our Df iv. Calculate the value of sum of at the end of respective columns. v. Mean can be calculated by using the formula as Type-3: In case of continuous observations or series of data: Another type of frequency distributions is there which consists of data that are grouped by classes. In such case each value of an observation falls somewhere in one of the classes. Calculation of arithmetic mean in case of grouped data is some what different from that of ungrouped data. To find out the arithmetic mean of continuous series, one has to calculate the midpoint of each class interval. To make midpoints come out in whole cents, one has to round up the value. Mean in continuous series can be calculated in two ways as derived below: a. Direct method: In this method, mean can be calculated by using the steps as i. First step is to calculate the mid point of each class interval. The mid point is denoted by ‘m and can be calculated as . ii. Multiply the mid points of each class interval (m) with its respective frequencies (f) i.e., find out mf iii. Calculate the value of sum of at the end of respective columns. iv. Mean can be calculated by using the formula as b. Short cut method: Mean can also be calculated by using short cut method. Following are the steps to calculate mean by this method. i. First step is to calculate the mid point of each class interval. The mid point is denoted by ‘m and can be calculated as . ii. Assume a value from the ‘m values of the series (denoted as A= assumed value) iii. In this step in another column we have to calculate the deviation value (denoted as D) of ‘m to that of assumed value (A) i.e., D = m A iv. Multiply each D with f i.e., find our Df v. Calculate the value of sum of at the end of respective columns. vi. Put the values in the following formula to get mean of the series THE WEIGHTED ARITHMETIC MEAN: In real life situation in management studies and social sciences, some items need more importance than that of the other items of that series. Hence, importance assigned to different items with the help of numerical value as per the priority basis in a series as called as weights. The arithmetic mean on the other hand, gives equal weightage or importance to each observation of the series. In such a case, the weighted mean acts as the most important tool for studying the behaviour of the entire set of study. Here use of weighted mean is the only measure of central tendency for getting correct and accurate result. Following is the procedures of computing mean of a weighted series. By the way, an important problem that arises while using weighted mean is regarding selection of weights. Weights may be either actual or arbitrary, i.e., estimated. The researcher will not face any difficulty, if the actual weights are assigned to the set of data. But in case, if actual data is not assigned than it is advisable to assign arbitrary or imaginary weights. Following are some steps of calculating weighted mean: i. In the first step, the values of each row (X) are to be multiplied by its respective weights (W) ii. Calculate the sum of the weights (column-2 in our example) at the end of the column denoted as iii. Calculate the sum of the X*W values at the end of the column (column-3 in our below derived example) denoted as iv. Mean () can be calculated by using the formula Advantages of Arithmetic mean: Following are some advantages of arithmetic mean. i. The concept is more familiar concept among the people. It is unique because each data set has only one mean. ii. It is very easy to compute and requires fewer calculations. As every data set has a mean, hence, as a measure mean can be calculated. iii. Mean represents a single value to the entire data set. Thus easily one can interpret a data set its characteristics. iv. An average can be calculated of any type of series. Disadvantages of Arithmetic mean: The disadvantages are as follows. i. One of the greatest disadvantages of average is that it is mostly affected by the extreme values. For example let consider Sachin Tendulkars score in last three matches. Let it be, 100 in first match, 2 in second match and 10 in third match. The average score of these three matches will me 100+2+10/3=37. Thus it implies that Tendulkars average score is 37 which is not correct. Hence lead to wrong conclusion. ii. It is not possible to compute mean for a data set that has open-ended classes at either the high or low end of the scale. iii. The arithmetic average sometimes gives such value which cannot be found from the data series from which it is calculated. iv. It is unrealistic. v. It cannot be identified observation or graphic method of representing the data and interpretation. THE MEDIAN: Another one technique to measure central tendency of a series of observation is the median. Median is generally that value of the entire series which divides the entire series into two equal parts from the middle. In other wards, it is the exactly middle value of the series. Hence, fifty percent of the observations in the series are above the median value and other fifty or half observations are remains below the median value. However, if the series are having odd numbers of observations like 3,5,7,9,11,13 etc., then the median value will be equal to one of the exact value from the series. On the other hand, if the series is having even observations, then median value can be calculated by getting the arithmetic mean of the two middle values of the observations of the series. Median an a technique of measuring central tendency can be best used in cases where the problem sought for more qualitative or psychological in nature such as health, intelligence, satisfaction etc. Definitions: The concept of median can be clearer from the definitions derived below. Connor defined it as ‘the median is the value which divides the distribution into two equal parts, one part comprising all values greater, and the other values less than the median. Where as Croxton and Cowden defined it as ‘the median is that value which divides a series so that one half or more of the items are equal to or less than it and one half or more of the items are equal to or greater than it. Median can be computed in three different series separately. All the cases are discussed separately below. Computation of Median in Individual Series Computation of Median in Discrete Series and Computation of Median in Continuous Series Computation of Median in Individual Series: Following are some steps to calculate the median in individual series. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. Than the median value can be calculated by using the formula th value or item from the series. Where, N= Number of observation in that series. When N is odd number (like 5, 7,9,11,13 etc.) median value is one of the item within that series, but in case N will be a even number than median is the arithmetic mean of the two middle value after applying the above formula. The following problem can make the concept clear. Computation of Median in Discrete Series: Discrete series are those where the data set is assigned with frequencies or repetitions. Following are the steps of computing the median when the series is discrete. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. In the third column of the table, calculate the cumulative frequencies. Than the median class can be calculated by using the formula th value or item from the cumulative frequencies of the series. Computation of Median in Continuous Series: Continuous series are the series of data where the data ranges are in class intervals. Each class is having an upper limit and a lower limit. In such cases the computation of median is little bit different from that of the other two cases discussed above. Following are some steps to get median in continuous series of data. The first and the most important requirement is that the data should be arranged in an ascending (increasing) or descending (decreasing) order. In the third column of the table, calculate the cumulative frequencies. Than the median class can be calculated by using the formula th value or item from the cumulative frequencies column of the series. Form the cumulative frequencies, one can get the median class i.e., in which class the value lies. This class is called as median class and one can get the lower value of the class and the upper value of the class. The following formula can be used to calculate the median We have to get the median class first. For this, median class is N/2 th value or 70/2= 35. The value 35 lies in the third row of the table against the class 30-40. Thus 30-40 is the median class and it shows that the median value lies in this class only. After getting the median class, to get the median value we have to apply the formula . Advantages of Median: Median as a measure of central tendency has following advantages of its own. It is very simple and can be easily understood. It is very easy to calculate and interpret. It Includes all the observations while calculation. Like that of arithmetic mean, median is not affected by the extreme values of the observation. It has the advantages for using further analysis. It can even used to calculate for open ended distribution. Disadvantages of Median: Median as a means to calculate central tendency is also not free from draw backs. Following are some important draw backs that are leveled against median. Median is not a widely measure to calculate central tendency like that of arithmetic mean and also mode. It is not based on algebraic treatment. THE MODE: Mode is defined as the value which occurs most often in the series or other wise called as the value having the highest frequencies. It is, hence, the value which has maximum concentration around it. Like that of median, mode is also more useful in case of qualitative data analysis. It can be used in problems generally having the discrete series of data and particularly, problems involving the expression of psychological determinants. Definitions: The concept of mode can be clearer from the definitions derived below. Croxten and Cowden defined it as ‘the mode of a distribution is the value at the point around which the items tend to be most heavily concentrated. It may be regarded as the most typical of a series of value. Similarly, in the words of Prof. Kenny ‘the value of the variable which occurs most frequently in a distribution is called the mode. Mode can be computed in three different series separately. All the cases are discussed separately below. Computation of Mode in Individual Series Computation of Mode in Discrete Series and Computation of Mode in Continuous Series Computation of Mode in Individual Series: Calculation of mode in individual series is very easy. The data is to be arranged in a sequential order and that value which occurs maximum times in that series is the value mode. The following example will make the concept clear. Computation of Mode in Discrete Series: Discrete series are those where the data set is assigned with frequencies or repetitions. Hence directly, mode will be that value which is having maximum frequency. By the way, for accuracy in calculation, there is a method called as groping method which is frequently used for calculating mode. Following is the illustration to calculate mode of a series by using grouping method. Consider the following data set and calculate mode by using the grouping method. The calculation carried out in different steps is derived as: Step-1: Sum of two frequencies including the first one i.e., 1+2=3, then 4+3=7, then 2+1=3 etc. Step-2: Sum of two frequencies excluding the first one i.e., 2+4=7, then 3+2=5, then 1+2=3 etc. Step-3: Sum of three frequencies including the first one i.e., 1+2+4=7, then 3+2+1=6 etc. Step-4: Sum of two frequencies excluding the first one i.e., 2+4+3=9, then 2+1+2=5 etc. Step-5: Sum of three frequencies excluding the first and second i.e., 4+3+2=9, then 1+2+1=4. Computation of Mode in Continuous Series: As already discussed, continuous series are the series of data where the data ranges are in class intervals. Each class is having an upper limit and a lower limit. In such cases the computation of mode is little bit different from that of the other two cases discussed above. Following are some steps to get mode in continuous series of data. Select the mode class. A mode class can be selected by selecting the highest frequency size. Mode value can be calculated by using the following formula Advantages of Mode: Following are some important advantages of mode as a measure of central tendency. It is easy to calculate and easy to understand. It eliminates the impact of extreme values. It is easy to locate and in some cases we can estimate mode by mere inspection. It is not affected by extreme values. Disadvantages of Mode: Following are some important disadvantages of mode. It is not suitable for further mathematical treatment. It may lead to a wrong conclusion. Some critiques criticized mode by saying that mode is influenced by length of the class interval. THE GEOMETRIC MEAN: Geometric mean, as another measure of central tendency is very much useful in social science and business related problems. It is an average which is most suitable when large weights have to be assigned to small values of observations and small weights to large values of observation. Geometric mean best suits to the problems where a particular situation changes over time in percentage terms. Hence it is basically used to find the average percent increase or decrease in sales, production, population etc. Again it is also considered to be the best average in the construction of index numbers. Geometric mean is defined as the Nth root of the product where there are N observations of a given series of data. For example, if a series is having only two observations then N will be two or we will take square root of the observations. Similarly, when series is having three observations then we have to take cube root and the process will continue like wise. Geometric mean can be calculated separately for two sets of data. Both are discussed below. When the data is ungrouped: In case of ungrouped series of observations, GM can be calculated by using the following formula: where X1 , X2 , X3, XN various observations of a series and N is the Nth observation of the data. But it is very difficult to calculate GM by using the above formula. Hence the above formula needs to be simplified. To simplify the formula, both side of the above formula is to be taken logarithms. To calculate the G.M. of an ungrouped data, following steps are to be adopted. Take the log of individual observations i.e., calculate log X. Make the sum of all log X values i.e., calculate Then use the above formula to calculate the G.M. of the series. When the data is grouped: Calculation of geometric mean in case of grouped data is little bit different from that of calculation of G.M. in case of ungrouped series. Following are some steps to calculate the G.M. in case of grouped data series. To calculate the G.M. of a grouped data, following steps are to be adopted. Take the mid point of the continuous series. Take the log of mid points i.e., calculate log X and it can also be denoted as log m Make the sum of all log X values i.e., calculate or Then use the following formula to calculate the G.M. of the series. Advantages of G.M.: Following are some advantages of G.M. i. One of the greatest advantages of G.M. is that it can be possible for further algebraic treatment i.e., combined G.M., can be calculated when there is availability of G.M., of two or more series along with their corresponding number of observations. ii. It is a very useful method of getting average when the series of observation possess rates of growth i.e., increase or decrease over a period of time. iii. Since it is useful in averaging ratios and percentages, hence, are more useful in social science and business related problems. Disadvantages of G.M.: G.M., as a technique of calculating central value is also not free from defects. Following are some disadvantages of G.M. i. It is very difficult to calculate the value of log and antilog and hence, compared to other methods of central tendency, G.M., is very difficult to compute. ii. The greatest disadvantage of G.M., is that it cannot be used when the series is having both negative or positive observations and observations having more zero values. THE HARMONIC MEAN: The last technique of getting the central tendency of a series of data is the Harmonic mean (H.M.). Harmonic mean, like the other methods of central tendency is not clearly defined. It is the reciprocal of the arithmetic mean of the reciprocal of the individual observations. H.M., is very much useful in those cases of observations where the nature of data is such that it express the average rate of growth of any events. For example, the average rate of increase of sales or profits, the average speed of a train or bus or a journey can be completed etc. Following is the general formula to calculate H.M.: When the data is ungrouped: When the observations of the series are ungrouped, H.M., can be calculated as: The step for calculating H.M., of ungrouped data by using the derived formula is very simple. In such a case, one has to find out the values of 1/X and then sum of 1/X. When the data is grouped: In case of grouped data, the formula for calculating H.M., is discussed as below: Take the mid point of the continuous series. Calculate 1/X and it can also be denoted as 1/m Make the sum of all 1/X values i.e., calculate Then use the following formula to calculate the H.M. of the series. Advantages of H.M.: Harmonic mean as a measure of central tendency is having following advantages. i. Harmonic mean considers each and every observation of the series. ii. It is simple to compute when compared to G.M. iii. It is very useful for averaging rates. Disadvantages of H.M.: Following are some disadvantages of H.M. i. It is rarely used as a technique of measuring central tendency. ii. It is not defined clearly like that of other techniques of measuring central value mean, median and mode. iii. Like that of G.M., H.M., cannot be used when the series is having both negative or positive observations and observations having more zero values. CONCLUSION: An average is a single value representing a group of values. Each type of averages has their own advantages and disadvantages and hence, they are having their own usefulness. But it is always confusing among the researchers that which average is the best among the five different techniques that we have discussed above? The answer to this question is very simple and says that no single average can be considered as best for all types of data. However, experts opine two considerations that the researchers must be kept in mind while going for selecting a technique to determine the average. The first consideration is that of determining the nature of data. If the data is more skewed it is better to avoid arithmetic mean, if the data is having gap around the middle value of the series, then median should be avoided and on the other hand, if the nature of series is such that they are unequal in class-intervals, then mode is to be avoided. The second consideration is on the type of value req uired. When there is need of composite average of all absolute or relative values, then arithmetic mean or geometric mean is to be selected, in case the researcher is in need of a middle value of the series, then median may be the best choice, but in case the most common value is needed, then will not be any alternative except mode. Similarly, Harmonic mean is useful in averaging ratios and percentages. SUMMERY: 1. Different experts have defined differently to the concept of average. 2. Arithmetic mean is the most simple and frequently used technique of computing central tendency. The average is also called as mean. It is other wise called as a single number representing a whole data set. 3. The best use of arithmetic mean is at the time of correcting some wrong entered data. For example in a group of 10 students, scoring an average of 60 marks, in a paper it was wrongly marked 70 instead of 65. the solution in such a cases is derived below: 4. In such a case, the weighted mean acts as the most important tool for studying the behaviour of the entire set of study. Here use of weighted mean is the only measure of central tendency for getting correct and accurate result. 5. Median is generally that value of the entire series which divides the entire series into two equal parts from the middle. 6. Mode is defined as the value which occurs most often in the series or other wise called as the value having the highest frequencies. It is, hence, the value which has maximum concentration around it. 7. Geometric mean is defined as the Nth root of the product where there are N observations of a given series of data. 8. Harmonic mean is the reciprocal of the arithmetic mean of the reciprocal of the individual observations. QUESTIONS: 1. In a class containing 90 students following heights (in inches) has been observed. Based on the data calculate the mean, median and mode of the class. 2. In a physical test camp meant for selection of army solders the following heights of the candidates have been observed. Find the mean, median and mode of the distribution. 3. From the distribution derived below, calculate mean and standard deviation of the series. 4. The following table derives the marks obtained in Indian Economy paper by 90 students in a class. Calculate the mean, median and mode of the following distribution. 5. The monthly profits of 180 shop keepers selling different commodities in a city footpath is derived below. Calculate the mean and median of the distribution. 6. The daily wage of 130 labourers working in a cotton mill in Ahmadabad cith is derived below. Calculate the mean, median and mode. 7. There is always controversy before the BCCI before selection of batsmen between Rahul Dravid and V.V.S. Laxman. Runs of 10 test matches of both the players are given below. Suggest who the better run getter is and who the consistent player is. 8. Calculate the mean, median and mode of the following distribution. 9. What do you mean by measure of central tendency? How far it helpful to a decision-maker in the process of decision making? 10. Define measure of central tendency? What are the basic criteria of a good average? 11. What do you mean by measure of central tendency? Compare and contrast arithmetic mean, median and mode by pointing out the advantages and disadvantages. 12. The expenditure on purchase of snacks by a group of hosteller per week is

Employment and Performance Essay

When allocating work, what things should be taken into consideration? When Allocating work you need to implement a clear and precise goal that is achievable within the given time. The availability, knowledge and skills of the staff need to be considered before assigning them to a particular role to ensure the best outcomes. Why are performance management systems necessary and how do you think performance appraisals contribute to performance and productivity in an organisation? Performance management systems help direct employees toward organisational goals by letting employees know what is expected of them and how it will be achieved. When an employee has been given the performance management system, they should know clearly what is expected of them and know what they will be assessed on. It is also a good opportunity for employers to praise employees on the areas that they have excelled in and also to identify areas that need further improvement. 3. What steps might be taken if it is necessary to follow-up performance appraisals? Some steps that need to be followed when evaluating performance appraisals are: †¢Set performance goals with each employee. †¢Set developmental goals with each employee. †¢Shift focus from their past performance to their future performance. †¢Working directly with the employees. †¢Provide the employees with formal and informal coaching, guidance, feedback and direction. Assessment Activity 11 Explain what coaching and mentoring are, making certain that the differences between the two processes are clearly explained. Mentoring is relationship orientated which provides a safe environment where the mentored shares whatever issues affect his or her professional and personal success. Mentoring is always long term because it requires time in which both partners can learn about one another and build trust. Coaching is more tasks specific. The focus is on concrete issues, such as managing more effectively and improving technical and practical skills. Also a coach can successfully be involved with the coached for a short period of time, maybe even just a few sessions. The coaching lasts for as long as is needed. Assessment Activity 12 Why is necessary to document and record performance? Documenting employee performance sends the message to an organisation’s workers that their work is observed and acknowledged. As employees realise their work is acknowledged their level of engagement in their job increases. This causes productivity to increase. Also when managers meet with employees to discuss their performance reviews, employees can ask their managers questions about comments they gave regarding their performance. They can also discuss personal items such as work and life balance issues that impact their performance and work with their managers to arrive at solutions to the challenges. Assessment Activity 8 1. Why is it necessary that performance monitoring and evaluation be continuous process? To ensure employee improvement, productivity, satisfaction and to make it into an effective communication tool that enables feedback from employees to management as well as from management to employees. 2. Why should both managers and employees receive suitable training in how to handle performance review/appraisal interviews? Managers and employees both need training so they understand how the system works, how they can and should contribute, what the results of an appraisal should be and how the appraisal process fits with the organisations procedures and expectations for future performance. 3. How and how often do you think performance appraisals should be held and why do you think this? I think performance appraisals should be held two times a year to discuss performance, counselling and developing employees, discussing compensation, job status, or disciplinary decisions. In what ways can you recognise the contribution of your work group members and why should you do this? Some ways you can recognise contribution of your work group members are: †¢Public recognition for their contributions. †¢A thank you for doing the job. †¢An email note. †¢Time off. †¢Getting the group together to do a fun activity that’s not work related. With rewarding the team, they will likely work much harder if they feel that what they’re doing really makes a difference, and that their efforts are noticed by those with power. What is feedback and why is it important that managers and supervisors provide informal feedback to staff on a regular basis? Feedback: Information about reactions to a product, a person’s performance of a task, etc. which is used as a basis for improvement. It’s important that managers provide informal feedback to employees on a regular basis to ensure they fully understand if they are meeting the required goals and also to encourage improvement and acknowledging a job well done in a causal and non-formal way.

Concepts of Race and Ethnicity Essay

Define the following terms. You may use definitions from the class readings, or from outside sources. If your definitions are from outside sources, cite the source(s) using APA style with in-text citations and a reference list. |Term |Definition | |Ethnicity |a shared cultural heritage, which typically involves common | | |ancestors, language, and religion | |Race |is a socially constructed category of people who share biologi-| | |cally transmitted traits that members of a society define as | | |important. | |Xenophobia |. The fear or hatred of strangers or foreigners. | |Segregation |the physical and social separation of categories of people. | |Assimilation |the process by which minorities gradually adopt cultural | | |patterns from the dominant majority population. | |Pluralism | a state in which people of all racial and ethnic categories | | |have about the same overall social standing | |White privilege | | |Colonialism | | |Racial profiling |in which police or others in power consider race or ethnicity | | |to be, by itself, a sign of probable guilt— illustrates the | | |operation of institutional racism. | Part II: Short Answer Using your course materials, answer the following questions in about 200 words each. Use your own words. Define de facto segregation and de jure segregation, and give an example of each. Which are we most likely to see today? Why? What conclusion do Crutchfeld, Fernandez, and Martinez (2010) come to regarding the presence of bias in the criminal justice system? How has bias in the system changed over time? Give examples of how an individual’s race or ethnicity might impact their experience as a suspect, a perpetrator, and a victim of crime. Describe anti-Chinese immigration sentiment in the 19th century. Compare this to anti-immigration perspectives today. How are they similar? How are they different? Part III: Personal Reflection sing as many words as you consider necessary. Consider the racial and ethnic groups that you belong to. Do you feel that you are knowledgable about the history of those groups? Now consider groups you do not belong to. Is there a group you would like to learn more about? |Worksheet 2 | PAGE 1 | |ETH/125 Version 7 | |. |Worksheet 2 | PAGE 1 | |ETH/125 Version 7 | |.

Women in Congress - List of US Congresswomen

More than 200 women have served in the United States House of Representatives. From 1789 to 1916, the House was all — male. Following is an index of the women whove been Representatives - sometimes called Congresswomen or Congressmen — beginning with the first in 1917. They are listed by the year they first took office. Jeannette Rankin Republican - MontanaServed: 1917 - 1919, 1941 - 1943 About Jeannette RankinCongressional Biographical DirectoryWomen in Congress Biography Alice Mary Robertson Republican - OklahomaServed: 1921 - 1923 Congressional Biographical DirectoryWomen in Congress Biography Winnifred S. Huck Republican - IllinoisServed: 1922 - 1923 Congressional Biographical DirectoryWomen in Congress Biography Mae Ella Nolan Republican - CaliforniaServed: 1923 - 1925 Congressional Biographical DirectoryWomen in Congress Biography Florence P. Kahn Republican - CaliforniaServed: 1925 - 1937 Congressional Biographical DirectoryWomen in Congress Biography Mary T. Norton Democrat - New JerseyServed: 1925 - 1951 Mary Norton QuoteCongressional Biographical DirectoryWomen in Congress Biography Edith Nourse Rogers Republican - MassachusettsServed: 1925 - 1960 Congressional Biographical DirectoryWomen in Congress Biography Katherine Gudger Langley Republican - KentuckyServed: 1927 - 1931 Congressional Biographical DirectoryWomen in Congress Biography Ruth Hanna McCormick Republican - IllinoisServed: 1929 - 1931 Congressional Biographical DirectoryWomen in Congress Biography Pearl Oldfield Democrat - ArkansasServed: 1929 - 1931 Congressional Biographical DirectoryWomen in Congress Biography Ruth Bryan Owen Democrat - FloridaServed: 1929 - 1933 Congressional Biographical DirectoryWomen in Congress Biography Ruth Sears Pratt Republican - New YorkServed: 1929 - 1933 Congressional Biographical DirectoryWomen in Congress Biography Effiegene Locke Wingo Democrat - ArkansasServed: 1930 - 1933 Congressional Biographical DirectoryWomen in Congress Biography Willa McCord Blake Eslick Democrat - TennesseeServed: 1932 - 1933 Congressional Biographical DirectoryWomen in Congress Biography Marian Williams Clarke Republican - New YorkServed: 1933 - 1935 Congressional Biographical DirectoryWomen in Congress Biography Kathryn OLoughlin McCarthy Democrat - KansasServed: 1933 - 1935 Congressional Biographical DirectoryWomen in Congress Biography Isabella S. Greenway Democrat - ArizonaServed: 1933 - 1937 Congressional Biographical DirectoryWomen in Congress Biography Virginia Ellis Jenckes Democrat - IndianaServed: 1933 - 1939 Congressional Biographical DirectoryWomen in Congress Biography Caroline ODay Democrat - New YorkServed: 1935 - 1943 Congressional Biographical DirectoryWomen in Congress Biography Nan Wood Honeyman Democrat - OregonServed: 1937 - 1939 Congressional Biographical DirectoryWomen in Congress Biography Elizabeth H. Gasque Democrat - South CarolinaServed: 1938 - 1939 Congressional Biographical DirectoryWomen in Congress Biography Clara G. McMillan Democrat - South CarolinaServed: 1939 - 1941 Congressional Biographical DirectoryWomen in Congress Biography Jessie Sumner Republican - IllinoisServed: 1939 - 1947 Congressional Biographical DirectoryWomen in Congress Biography Florence Reville Gibbs Democrat - GeorgiaServed: 1940 - 1941 Congressional Biographical DirectoryWomen in Congress Biography Margaret Chase Smith Republican - MaineServed: 1940 - 1949 Margaret Chase Smith QuotesCongressional Biographical DirectoryWomen in Congress Biography Frances Payne Bolton Republican - OhioServed: 1940 - 1969 Congressional Biographical DirectoryWomen in Congress Biography Katharine Edgar Byron Democrat - MarylandServed: 1941 - 1943 Congressional Biographical DirectoryWomen in Congress Biography Veronica Grace Boland Democrat - PennsylvaniaServed: 1942 - 1943 Congressional Biographical DirectoryWomen in Congress Biography Winifred Claire Stanley Republican - New YorkServed: 1943 - 1945 Congressional Biographical DirectoryWomen in Congress Biography Clare Boothe Luce Republican - ConnecticutServed: 1943 - 1947 Congressional Biographical DirectoryWomen in Congress Biography Willa Lybrand Fulmer Democrat - South CarolinaServed: 1944 - 1945 Congressional Biographical DirectoryWomen in Congress Biography Emily Taft Douglas Democrat - IllinoisServed: 1945 - 1947 Congressional Biographical DirectoryWomen in Congress Biography Chase Going Woodhouse Democrat - ConnecticutServed: 1945 - 1947, 1949 - 1951 Congressional Biographical DirectoryWomen in Congress Biography Helen Gahagan Douglas Democrat - CaliforniaServed: 1945 - 1951 Congressional Biographical DirectoryWomen in Congress Biography Helen Douglas Mankin Democrat - GeorgiaServed: 1946 - 1947 Congressional Biographical DirectoryWomen in Congress Biography Eliza Jane Pratt Democrat - North CarolinaServed: 1946 - 1947 Congressional Biographical DirectoryWomen in Congress Biography Georgia Lee Lusk Democrat - New MexicoServed: 1947 - 1949 Congressional Biographical DirectoryWomen in Congress Biography Katharine St. George Republican - New YorkServed: 1947 - 1965 Congressional Biographical DirectoryWomen in Congress Biography Reva Beck Bosone Democrat - UtahServed: 1949 - 1953 Congressional Biographical DirectoryWomen in Congress Biography Cecil Murray Harden Republican - IndianaServed: 1949 - 1959 Congressional Biographical DirectoryWomen in Congress Biography Edna Flannery Kelly Democrat - New YorkServed: 1949 - 1969 Congressional Biographical DirectoryWomen in Congress Biography Vera Daerr Buchanan Democrat - PennsylvaniaServed: 1951 - 1955 Congressional Biographical DirectoryWomen in Congress Biography Ruth Thompson Republican - MichiganServed: 1951 - 1957 Congressional Biographical DirectoryWomen in Congress Biography Marguerite Stitt Church Republican - IllinoisServed: 1951 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Maude Elizabeth Kee Democrat - West VirginiaServed: 1951 - 1965 Congressional Biographical DirectoryWomen in Congress Biography Gracie Bowers Pfost Democrat - IdahoServed: 1953 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Leonor K. Sullivan Democrat - MissouriServed: 1953 - 1977 Congressional Biographical DirectoryWomen in Congress Biography Mary E. (Betty) Farrington Republican - HawaiiServed: 1954 - 1957 Congressional Biographical DirectoryWomen in Congress Biography Coya Knutson Democrat - MinnesotaServed: 1955 - 1959 Congressional Biographical DirectoryWomen in Congress Biography Iris Faircloth Blitch Democrat - GeorgiaServed: 1955 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Edith Starrett Green Democrat - OregonServed: 1955 - 1974 Congressional Biographical DirectoryWomen in Congress Biography Martha Wright Griffiths Democrat - MichiganServed: 1955 - 1974 Congressional Biographical DirectoryWomen in Congress Biography Kathryn E. Granahan Democrat - PennsylvaniaServed: 1956 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Florence P. Dwyer Republican - New JerseyServed: 1957 - 1973 Congressional Biographical DirectoryWomen in Congress Biography Edna O. Simpson Republican - IllinoisServed: 1959 - 1961 Congressional Biographical DirectoryWomen in Congress Biography Jessica McCullough Weis Republican - New YorkServed: 1959 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Catherine Dean May Republican - WashingtonServed: 1959 - 1971 Congressional Biographical DirectoryWomen in Congress Biography Julia Butler Hansen Democrat - WashingtonServed: 1960 - 1974 Congressional Biographical DirectoryWomen in Congress Biography Catherine D. Norrell Democrat - ArkansasServed: 1961 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Louise G. Reece Republican - TennesseeServed: 1961 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Corinne Boyd Riley Democrat - South CarolinaServed: 1962 - 1963 Congressional Biographical DirectoryWomen in Congress Biography Charlotte T. Reid Republican - IllinoisServed: 1963 - 1971 Congressional Biographical DirectoryWomen in Congress Biography Irene Bailey Baker Republican - TennesseeServed: 1964 - 1965 Congressional Biographical DirectoryWomen in Congress Biography Patsy T. Mink Democrat - HawaiiServed: 1965 - 1977, 1990 - 2002 Congressional Biographical DirectoryWomen in Congress Biography Lera Millard Thomas Democrat - TexasServed: 1966 - 1967 Congressional Biographical DirectoryWomen in Congress Biography Margaret M. Heckler Republican - MassachusettsServed: 1967 - 1983 Congressional Biographical DirectoryWomen in Congress Biography Shirley Anita Chisholm Democrat - New YorkServed: 1969 - 1983 About Shirley ChisholmShirley Chisholm QuotesCongressional Biographical DirectoryWomen in Congress Biography Louise Day Hicks Democrat - MassachusettsServed: 1971 - 1973 Congressional Biographical DirectoryWomen in Congress Biography Ella Tambussi Grasso Democrat - ConnecticutServed: 1971 - 1975 Congressional Biographical DirectoryWomen in Congress Biography Bella Savitzky Abzug Democrat - New YorkServed: 1971 - 1977 About Bella AbzugBella Abzug QuotesCongressional Biographical DirectoryWomen in Congress Biography Elizabeth Bullock Andrews Democrat - AlabamaServed: 1972 - 1973 Congressional Biographical DirectoryWomen in Congress Biography Yvonne Brathwaite Burke Democrat - CaliforniaServed: 1973 - 1979 Congressional Biographical DirectoryWomen in Congress Biography Barbara Jordan Democrat - TexasServed: 1973 - 1979 About Barbara JordanBarbara Jordan QuotesCongressional Biographical DirectoryWomen in Congress Biography Elizabeth Holtzman Democrat - New YorkServed: 1973 - 1981 Congressional Biographical DirectoryWomen in Congress Biography Marjorie Sewell Holt Republican - MarylandServed: 1973 - 1987 Congressional Biographical DirectoryWomen in Congress Biography Corinne Claiborne (Lindy) Boggs Democrat - LouisianaServed: 1973 - 1991 Congressional Biographical DirectoryWomen in Congress Biography Cardiss Collins Democrat - IllinoisServed: 1973 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Patricia S. Schroeder Democrat - ColoradoServed: 1973 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Martha Elizabeth Keys Democrat - KansasServed: 1975 - 1979 Congressional Biographical DirectoryWomen in Congress Biography Helen Stevenson Meyner Democrat - New JerseyServed: 1975 - 1979 Congressional Biographical DirectoryWomen in Congress Biography Shirley N. Pettis Republican - CaliforniaServed: 1975 - 1979 Congressional Biographical DirectoryWomen in Congress Biography Gladys Noon Spellman Democrat - MarylandServed: 1975 - 1981 Congressional Biographical DirectoryWomen in Congress Biography Millicent Fenwick Republican - New JerseyServed: 1975 - 1983 Congressional Biographical DirectoryWomen in Congress Biography Virginia Dodd Smith Republican - NebraskaServed: 1975 - 1991 Congressional Biographical DirectoryWomen in Congress Biography Marilyn Lloyd Democrat - TennesseeServed: 1975 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Barbara Ann Mikulski Democrat - MarylandServed: 1977 - 1987 Congressional Biographical DirectoryWomen in Congress Biography Mary Rose Oakar Democrat - OhioServed: 1977 - 1993 Congressional Biographical DirectoryWomen in Congress Biography Geraldine Anne Ferraro Democrat - New YorkServed: 1979 - 1985 About Geraldine FerraroCongressional Biographical DirectoryWomen in Congress Biography Beverly Butcher Byron Democrat - MarylandServed: 1979 - 1993 Congressional Biographical DirectoryWomen in Congress Biography Olympia Jean Snowe Republican - MaineServed: 1979 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Bobbi Fiedler Republican - CaliforniaServed: 1981 - 1987 Congressional Biographical DirectoryWomen in Congress Biography Lynn Martin Republican - IllinoisServed: 1981 - 1991 Congressional Biographical DirectoryWomen in Congress Biography Claudine Schneider Republican - Rhode IslandServed: 1981 - 1991 Congressional Biographical DirectoryWomen in Congress Biography Margaret (Marge) Roukema Republican - New JerseyServed: 1981 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Jean Spencer Ashbrook Republican - OhioServed: 1982 - 1983 Congressional Biographical DirectoryWomen in Congress Biography Katie Beatrice Hall Democrat - IndianaServed: 1982 - 1985 Congressional Biographical DirectoryWomen in Congress Biography Barbara B. Kennelly Democrat - ConnecticutServed: 1982 - 1999 Congressional Biographical DirectoryWomen in Congress Biography Sala Galante Burton Democrat - CaliforniaServed: 1983 - 1987 Congressional Biographical DirectoryWomen in Congress Biography Barbara Boxer Democrat - CaliforniaServed: 1983 - 1993 Congressional Biographical DirectoryWomen in Congress Biography Barbara F. Vucanovich Republican - NevadaServed: 1983 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Nancy L. Johnson Republican - ConnecticutServed: 1983 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Marcia C. (Marcy) Kaptur Democrat - OhioServed: 1983 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Catherine S. Long Democrat - LouisianaServed: 1985 - 1987 Congressional Biographical DirectoryWomen in Congress Biography Helen Delich Bentley Republican - MarylandServed: 1985 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Jan L. Meyers Republican - KansasServed: 1985 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Patricia F. Saiki Republican - HawaiiServed: 1987 - 1991 Congressional Biographical DirectoryWomen in Congress Biography Elizabeth J. Patterson Democrat - South CarolinaServed: 1987 - 1993 Congressional Biographical DirectoryWomen in Congress Biography Constance A. Morella Republican - MarylandServed: 1987 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Nancy Pelosi Democrat - CaliforniaServed: 1987 - Present About Nancy PelosiNancy Pelosi QuotesOfficial WebsiteCongressional Biographical DirectoryWomen in Congress Biography Louise M. Slaughter Democrat - New YorkServed: 1987 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Jill L. Long Democrat - IndianaServed: 1989 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Jolene Unsoeld Democrat - WashingtonServed: 1989 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Nita M. Lowey Democrat - New YorkServed: 1989 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Ileana Ros - Lehtinen Republican - FloridaServed: 1989 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Susan Molinari Republican - New YorkServed: 1990 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Joan Kelly Horn Democrat - MissouriServed: 1991 - 1993 Congressional Biographical DirectoryWomen in Congress Biography Barbara-Rose Collins Democrat - MichiganServed: 1991 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Rosa DeLauro Democrat - ConnecticutServed: 1991 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Eleanor Holmes Norton Democrat - The District Of ColumbiaServed: 1991 - Present Eleanor Holmes Norton QuoteOfficial WebsiteCongressional Biographical DirectoryWomen in Congress Biography Maxine Waters Democrat - CaliforniaServed: 1991 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Eva M. Clayton Democrat - North CarolinaServed: 1992 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Corrine Brown Democrat - FloridaServed: 1993 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Leslie L. Byrne Democrat - VirginiaServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Maria E. Cantwell Democrat - WashingtonServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Karan English Democrat - ArizonaServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Marjorie Margolies-Mezvinsky Democrat - PennsylvaniaServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Lynn Schenk Democrat - CaliforniaServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Karen Shepherd Democrat - UtahServed: 1993 - 1995 Congressional Biographical DirectoryWomen in Congress Biography Blanche Lambert Lincoln Democrat - ArkansasServed: 1993 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Elizabeth Furse Democrat - OregonServed: 1993 - 1999 Congressional Biographical DirectoryWomen in Congress Biography Jane F. Harman Democrat - CaliforniaServed: 1993 - 1999, 2001 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Patsy Ann Danner Democrat - MissouriServed: 1993 - 2001 Congressional Biographical DirectoryWomen in Congress Biography Tillie Kidd Fowler Republican - FloridaServed: 1993 - 2001 Congressional Biographical DirectoryWomen in Congress Biography Carrie P. Meek Democrat - FloridaServed: 1993 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Karen L. Thurman Democrat - FloridaServed: 1993 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Cynthia McKinney Democrat - GeorgiaServed: 1993 - 2003, 2005 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Jennifer Dunn Republican - WashingtonServed: 1993 - 2005 Congressional Biographical DirectoryWomen in Congress Biography Anna Georges Eshoo Democrat - CaliforniaServed: 1993 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Eddie Bernice Johnson Democrat - TexasServed: 1993 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Carolyn B. Maloney Democrat - New YorkServed: 1993 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Deborah Pryce Republican - OhioServed: 1993 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Lucille Roybal-Allard Democrat - CaliforniaServed: 1993 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Nydia M. Velà ¡zquez Democrat - New YorkServed: 1993 - Present Official WebsiteWomen in Congress Biography Lynn C. Woolsey Democrat - CaliforniaServed: 1993 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Enid Greene Waldholtz Republican - UtahServed: 1995 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Andrea Seastrand Republican - CaliforniaServed: 1995 - 1997 Congressional Biographical DirectoryWomen in Congress Biography Linda Smith Republican - WashingtonServed: 1995 - 1999 Congressional Biographical DirectoryWomen in Congress Biography Helen P. Chenoweth Republican - IdahoServed: 1995 - 2001 Congressional Biographical DirectoryWomen in Congress Biography Lynn Nancy Rivers Democrat - MichiganServed: 1995 - 2003 Congressional Biographical DirectoryWomen in Congress Biography Karen McCarthy Democrat - MissouriServed: 1995 - 2005 Congressional Biographical DirectoryWomen in Congress Biography Sue W. Kelly Republican - New YorkServed: 1995 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Barbara L. Cubin Republican - WyomingServed: 1995 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Sheila Jackson Lee Democrat - TexasServed: 1995 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Zoe Lofgren Democrat - CaliforniaServed: 1995 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Sue Myrick Republican - North CarolinaServed: 1995 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Juanita Millender-McDonald Democrat - CaliforniaServed: 1996 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Jo Ann Emerson Republican - MissouriServed: 1996 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Deborah A. Stabenow Democrat - MichiganServed: 1997 - 2001 Congressional Biographical DirectoryWomen in Congress Biography Julia May Carson Democrat - IndianaServed: 1997 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Anne Meagher Northup Republican - KentuckyServed: 1997 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Donna M. Christian-Christensen Democrat - Virgin IslandsServed: 1997 - 2015 Congressional Biographical DirectoryWomen in Congress Biography Diana L. DeGette Democrat - ColoradoServed: 1997 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Kay Granger Republican - TexasServed: 1997 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Darlene K. Hooley Democrat - OregonServed: 1997 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Stephanie Tubbs Jones Democrat - OhioServed: 1997 - 2008 (died August 20, 2008) Congressional Biographical DirectoryWomen in Congress Biography Carolyn Cheeks Kilpatrick Democrat - MichiganServed: 1997 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Carolyn McCarthy Democrat - New YorkServed: 1997 -  2015 Congressional Biographical DirectoryWomen in Congress Biography Loretta Sanchez Democrat - CaliforniaServed: 1997 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Ellen OKane Tauscher Democrat - CaliforniaServed: 1997 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Mary Bono Mack Republican - CaliforniaServed: 1998 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Lois Capps Democrat - CaliforniaServed: 1998 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Barbara Lee Democrat - CaliforniaServed: 1998 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Heather A. Wilson Republican - New MexicoServed: 1998 - 2009 Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Tammy Baldwin Democrat - WisconsinServed: 1999 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Shelley Berkley Democrat - NevadaServed: 1999 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Judy Borg Biggert Republican - IllinoisServed: 1999 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Grace Napolitano Democrat - CaliforniaServed: 1999 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Janice Schakowsky Democrat - IllinoisServed: 1999 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Jo Ann Davis Republican - VirginiaServed: 2001 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Melissa A. Hart Republican - PennsylvaniaServed: 2001 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Shelley Moore Capito Republican - West VirginiaServed: 2001 -  2015 Congressional Biographical DirectoryWomen in Congress Biography Susan A. Davis Democrat - CaliforniaServed: 2001 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Betty McCollum Democrat - MinnesotaServed: 2001 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Hilda L. Solis Democrat - CaliforniaServed: 2001 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Diane Edith Watson Democrat - CaliforniaServed: 2001 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Denise Majette Democrat - GeorgiaServed: 2003 - 2005 Congressional Biographical DirectoryWomen in Congress Biography Katherine Harris Republican - FloridaServed: 2003 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Marsha Blackburn Republican - TennesseeServed: 2003 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Madeleine Z. Bordallo Democrat - GuamServed: 2003 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Virginia (Ginny) Brown-Waite Republican - FloridaServed: 2003 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Candice Miller Republican - MichiganServed: 2003 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Marilyn N. Musgrave Republican - ColoradoServed: 2003 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Linda T. Sà ¡nchez Democrat - CaliforniaServed: 2003 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Stephanie Herseth Sandlin Democrat - South DakotaServed: 2004 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Melissa Bean Democrat - IllinoisServed: 2005 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Thelma Drake Republican - VirginiaServed: 2005 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Virginia Foxx Republican - North CarolinaServed: 2005 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Cathy McMorris Rodgers Republican - WashingtonServed: 2005 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Gwen Moore Democrat - WisconsinServed: 2005 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Doris Matsui Democrat - CaliforniaServed: 2005 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Allyson Schwartz Democrat - PennsylvaniaServed: 2005 -  2015 Congressional Biographical DirectoryWomen in Congress Biography Jean Schmidt Republican - OhioServed: 2005 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Debbie Wasserman Schultz Democrat - FloridaServed: 2005 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Shelley Sekula Gibbs Republican - TexasServed: 2006 - 2007 Congressional Biographical DirectoryWomen in Congress Biography Michele Bachmann Republican - MinnesotaServed: 2007 -  2015 Congressional Biographical DirectoryWomen in Congress Biography Nancy Boyda Democrat - KansasServed: 2007 - 2009 Congressional Biographical DirectoryWomen in Congress Biography Kathy Castor Democrat - FloridaServed: 2007 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Yvette D. Clarke Democrat - New YorkServed: 2007 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Mary Fallin Republican - OklahomaServed: 2007 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Gabrielle Giffords Democrat - ArizonaServed: 2007 - 2012 Congressional Biographical DirectoryWomen in Congress Biography Kirsten Gillibrand Democrat - New YorkServed: 2007 - 2009Congressional Biographical DirectoryWomen in Congress Biography Mazie Hirono Democrat - HawaiiServed: 2007 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Laura Richardson Democrat - CaliforniaServed: 2007 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Carol Shea-Porter Democrat - New HampshireServed: 2007 - 2011, 2013 - 2015 Congressional Biographical DirectoryWomen in Congress Biography Betty Sutton Democrat - OhioServed: 2007 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Nicola S. (Niki) Tsongas Democrat - MassachusettsServed: 2007 - present Congressional Biographical DirectoryWomen in Congress Biography Donna Edwards Democrat - MarylandServed: 2008 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Marcia Fudge Democrat - OhioServed: 2008 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Jackie Speier Democrat - CaliforniaServed: 2008 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Kathleen A. (Kathy) Dahlkemper Democrat - PennsylvaniaServed: 2009 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Deborah L. Halvorson Democrat - IllinoisServed: 2009 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Lynn Jenkins Republican - CaliforniaServed: 2009 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Mary Jo Kilroy Democrat - OhioServed: 2009 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Ann Kirkpatrick Democrat - ArizonaServed: 2009 - 2011, 2013 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Suzanne Kosmas Democrat - FloridaServed: 2009 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Cynthia Lummis Republican - WyomingServed: 2009 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Betsy Markey Democrat - ColoradoServed: 2009 - 2011 Congressional Biographical DirectoryWomen in Congress Biography Chellie Pingree Democrat - MaineServed: 2009 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Dina Titus Democrat - NevadaServed: 2009 - 2011, 2013 - Present Congressional Biographical DirectoryWomen in Congress Biography Judy Chu Democrat - CaliforniaServed: 2009 - Present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Sandra (Sandy) Adams Republican - FloridaServed: 2011 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Karen Bass Democrat - CaliforniaServed: 2011 - present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Diane Black Republican - TennesseeServed: 2011 - present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Ann Marie Buerkle Republican - New YorkServed: 2011 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Renee Ellmers Republican - North CarolinaServed: 2011 - 2017 Congressional Biographical DirectoryWomen in Congress Biography Colleen Hanabusa Democrat - HawaiiServed: 2011 -  2015 Congressional Biographical DirectoryWomen in Congress Biography Vicky Hartzler Republican - MissouriServed: 2011 - present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Nan Hayworth Republican - New YorkServed: 2011 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Jaime Herrera Beutler Republican - WashingtonServed: 2011 - present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Kristi Noem Republican - South DakotaServed: 2011 - present Official WebsiteCongressional Biographical DirectoryWomen in Congress Biography Martha Roby Republican - AlabamaServed: 2011 - present Congressional Biographical DirectoryWomen in Congress Biography Terri Sewell Democrat - AlabamaServed: 2011 - present Congressional Biographical DirectoryWomen in Congress Biography Frederica Wilson Democrat - FloridaServed: 2011 - present Congressional Biographical DirectoryWomen in Congress Biography Kathy Hochul Democrat - New YorkServed: 2011 - 2013 Congressional Biographical DirectoryWomen in Congress Biography Janice Hahn Democrat - CaliforniaServed: 2011 - 2016 Congressional Biographical DirectoryWomen in Congress Biography Suzanne Bonamici Democrat - CaliforniaServed: 2012 - present Congressional Biographical DirectoryWomen in Congress Biography Suzan DelBene Democrat - WashingtonServed: 2012 - present Congressional Biographical DirectoryWomen in Congress Biography Joyce Beatty Democrat, OhioServed: 2013 - present Women in Congress Biography Susan Brooks Republican, IndianaServed: 2013 - present Women in Congress Biography Julia Brownley Democrat, CaliforniaServed: 2013 - present Women in Congress Biography Cheri Bustos Democrat, IllinoisServed: 2013 - present Women in Congress Biography Tammy Duckworth Democrat, IllinoisServed: 2013 - 2017 (became Senator in 2017) Women in Congress Biography Elizabeth Esty Democrat, ConnecticutServed: 2013 - present Women in Congress Biography Lois Frankel Democrat, FloridaServed: 2013 - present Women in Congress Biography Tulsi Gabbard Democrat, HawaiiServed: 2013 - present Women in Congress Biography Ann McLane Kuster Democrat, New HampshireServed: 2013 - present Women in Congress Biography Michelle Lujan Grisham Democrat, New MexicoServed: 2013 - present Women in Congress Biography Grace Meng Democrat, New YorkServed: 2013 - present Women in Congress Biography Gloria Negrete McLeod Democrat, CaliforniaServed: 2013 - 2015 Women in Congress Biography Kyrsten Sinema Democrat, ArizonaServed: 2013 - present Women in Congress Biography Ann Wagner Republican, MissouriServed: 2013 - present Women in Congress Biography Jackie Walorski Republican, IndianaServed: 2013 - present Women in Congress Biography Robin Kelly Democrat, IllinoisServed: April 11, 2013 - present Women in Congress Biography Katherine Clark Democrat, MassachusettsServed: December 10, 2013 - present Women in Congress Biography Alma Adams North CarolinaServed: November 12, 2014 - present Aumua Amata Republican, American Samoa at largeServed: 2015 - present Bonnie Watson Coleman New Jersey, DemocraticServed: 2015 - present Barbara Comstock Republican, VirginiaServed: 2015 - present Deborah Dingell Democratic, MichiganServed: 2015 - present Gwen Graham Democratic, FloridaServed: 2015 - 2017 Brenda Lawrence Democratic, MIchiganServed: 2015 - present Mia Love Republican, UtahServed: 2015 - present Martha McSally Republican, ArizonaServed: 2015 - present Stacey Plaskett Democratic, U.S. Virgin Islands at largeServed: 2015 - present Kathleen Rice Democratic, New YorkServed: 2015 - present Elise Stefanik Republican, New YorkServed: 2015 - present Norma Torres Democratic, CaliforniaServed: 2015 - present Mimi Walters Republican, CaliforniaServed: 2015 - present Nanette Barragà ¡n Democratic, CaliforniaServed 2017 - present Lisa Blunt-Rochester Democratic, DelawareServed 2017 - present Liz Cheney Republican, WyomingServed 2017 - present Val Demings Democratic, FloridaServed 2017 - present Jenniffer Gonzà ¡lez Republican, Puerto RicoServed 2017 - present Pramila Jayapal Democratic, WashingtonServed 2017 - present Stephanie Murphy Democratic, FloridaServed 2017 - present Jacky Rosen Democratic, NevadaServed 2017 - present Claudia Tenney Republican, New YorkServed 2017 - present For more information about women in the US government, check out our articles on women who have served in the Senate or as governors.